Updated on 2024/05/29

写真a

 
FURUKAWA Taiki
 
Organization
Nagoya University Hospital Medical IT Center Lecturer
Graduate School
Graduate School of Medicine
Title
Lecturer
Profile
平成23年3月名古屋大学医学部医学科 卒業
平成23年4月公立陶生病院初期研修医
平成26年4月公立陶生病院呼吸器・アレルギー疾患内科専攻医
平成29年4月名古屋大学大学院医学系研究科呼吸器内科学医員
    10月理化学研究所画像情報処理研究チーム
平成30年11月名古屋大学医学部附属病院メディカルITセンター医員
令和3年4月名古屋大学医学部附属病院メディカルITセンター特任助教
令和5年6月名古屋大学医学部附属病院メディカルITセンター講師 (副センター長)

Degree 1

  1. 博士(医学) ( 2021.3   名古屋大学 ) 

Research Areas 2

  1. Informatics / Life, health and medical informatics  / 医療情報学

  2. Life Science / Respiratory medicine

Current Research Project and SDGs 2

  1. All Japan大規模レジストリデータを背景とした間質性肺炎の治療プログラム及びデバイスの開発

  2. 人工衛星による大気汚染情報を活用した間質性肺炎プレシジョンメディシンの実現

Research History 3

  1. Nagoya University   Medical IT Center, Hospital   Lecturer

    2023.6

  2. RIKEN

    2021.4

  3. Nagoya University   Medical IT Center, Hospital   Designated assistant professor

    2021.4 - 2023.5

Professional Memberships 5

  1. 日本メディカルAI学会

    2023.6

  2. Japan Association for Medical Informatics

    2021.5

  3. The Japanese Society for Artificial Intelligence

    2021.4

  4. Japanese Respiratory Society

    2013.10

  5. Japanese Society of Allergology

    2012.4

Committee Memberships 4

  1. 日本呼吸器学会   保険委員  

    2024.4   

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    Committee type:Academic society

  2. 日本呼吸器学会   MDD保険診療資格等認定委員会  

    2023.8   

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    Committee type:Academic society

  3. 厚生労働省難治性疾患等政策研究事業「びまん性肺疾患に関する調査研究」班   研究協力者  

    2023.4   

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    Committee type:Other

  4. 日本呼吸器学会びまん性肺疾患MDD診断保険収載検討タスクフォース   幹事  

    2021.10   

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    Committee type:Academic society

Awards 1

  1. Fukuchi Award

    2023.11   Asian Pacific Society of Respirology (APSR)   A comprehensible machine learning tool to differentially diagnose idiopathic pulmonary fibrosis from other chronic interstitial lung diseases

    Furukawa, T, Oyama, S, Yokota, H, Kondoh, Y, Kataoka, K, Johkoh, T, Fukuoka, J, Hashimoto, N, Sakamoto, K, Shiratori, Y, Hasegawa, Y

 

Papers 21

  1. A comprehensible machine learning tool to differentially diagnose idiopathic pulmonary fibrosis from other chronic interstitial lung diseases. Reviewed International journal

    Taiki Furukawa, Shintaro Oyama, Hideo Yokota, Yasuhiro Kondoh, Kensuke Kataoka, Takeshi Johkoh, Junya Fukuoka, Naozumi Hashimoto, Koji Sakamoto, Yoshimune Shiratori, Yoshinori Hasegawa

    Respirology (Carlton, Vic.)   Vol. 27 ( 9 ) page: 739 - 746   2022.9

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    Authorship:Lead author, Corresponding author   Language:English   Publishing type:Research paper (scientific journal)  

    BACKGROUND AND OBJECTIVE: Idiopathic pulmonary fibrosis (IPF) has poor prognosis, and the multidisciplinary diagnostic agreement is low. Moreover, surgical lung biopsies pose comorbidity risks. Therefore, using data from non-invasive tests usually employed to assess interstitial lung diseases (ILDs), we aimed to develop an automated algorithm combining deep learning and machine learning that would be capable of detecting and differentiating IPF from other ILDs. METHODS: We retrospectively analysed consecutive patients presenting with ILD between April 2007 and July 2017. Deep learning was used for semantic image segmentation of HRCT based on the corresponding labelled images. A diagnostic algorithm was then trained using the semantic results and non-invasive findings. Diagnostic accuracy was assessed using five-fold cross-validation. RESULTS: In total, 646,800 HRCT images and the corresponding labelled images were acquired from 1068 patients with ILD, of whom 42.7% had IPF. The average segmentation accuracy was 96.1%. The machine learning algorithm had an average diagnostic accuracy of 83.6%, with high sensitivity, specificity and kappa coefficient values (80.7%, 85.8% and 0.665, respectively). Using Cox hazard analysis, IPF diagnosed using this algorithm was a significant prognostic factor (hazard ratio, 2.593; 95% CI, 2.069-3.250; p < 0.001). Diagnostic accuracy was good even in patients with usual interstitial pneumonia patterns on HRCT and those with surgical lung biopsies. CONCLUSION: Using data from non-invasive examinations, the combined deep learning and machine learning algorithm accurately, easily and quickly diagnosed IPF in a population with various ILDs.

    DOI: 10.1111/resp.14310

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  2. Mitochondrial DNA in bronchoalveolar lavage fluid is associated with the prognosis of idiopathic pulmonary fibrosis: a single cohort study Reviewed International journal

    Fukihara, J; Sakamoto, K; Ikeyama, Y; Furukawa, T; Teramachi, R; Kataoka, K; Kondoh, Y; Hashimoto, N; Ishii, M

    RESPIRATORY RESEARCH   Vol. 25 ( 1 ) page: 202   2024.5

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    Language:English   Publishing type:Research paper (scientific journal)   Publisher:Respiratory Research  

    Background: Extracellular mitochondrial DNA (mtDNA) is released from damaged cells and increases in the serum and bronchoalveolar lavage fluid (BALF) of idiopathic pulmonary fibrosis (IPF) patients. While increased levels of serum mtDNA have been reported to be linked to disease progression and the future development of acute exacerbation (AE) of IPF (AE-IPF), the clinical significance of mtDNA in BALF (BALF-mtDNA) remains unclear. We investigated the relationships between BALF-mtDNA levels and other clinical variables and prognosis in IPF. Methods: Extracellular mtDNA levels in BALF samples collected from IPF patients were determined using droplet-digital PCR. Levels of extracellular nucleolar DNA in BALF (BALF-nucDNA) were also determined as a marker for simple cell collapse. Patient characteristics and survival information were retrospectively reviewed. Results: mtDNA levels in serum and BALF did not correlate with each other. In 27 patients with paired BALF samples obtained in a stable state and at the time of AE diagnosis, BALF-mtDNA levels were significantly increased at the time of AE. Elevated BALF-mtDNA levels were associated with inflammation or disordered pulmonary function in a stable state (n = 90), while being associated with age and BALF-neutrophils at the time of AE (n = 38). BALF-mtDNA ≥ 4234.3 copies/µL in a stable state (median survival time (MST): 42.4 vs. 79.6 months, p < 0.001) and ≥ 11,194.3 copies/µL at the time of AE (MST: 2.6 vs. 20.0 months, p = 0.03) were associated with shorter survival after BALF collection, even after adjusting for other known prognostic factors. On the other hand, BALF-nucDNA showed different trends in correlation with other clinical variables and did not show any significant association with survival time. Conclusions: Elevated BALF-mtDNA was associated with a poor prognosis in both IPF and AE-IPF. Of note, at the time of AE, it sharply distinguished survivors from non-survivors. Given the trends shown by analyses for BALF-nucDNA, the elevation of BALF-mtDNA might not simply reflect the impact of cell collapse. Further studies are required to explore the underlying mechanisms and clinical applications of BALF-mtDNA in IPF.

    DOI: 10.1186/s12931-024-02828-9

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  3. Analysis of In-Hospital Morality Prediction by Machine Learning Reviewed

    TAIKI FURUKAWA, SHOTARO MISAWA, SHINTARO OYAMA RYUJI KANO, HIROKAZU YARIMIZU, TOMOKI TANIGUCHI, KOHEI ONODA, KIKUE SATO, YOSHIMUNE SHIRATORI

    JAPAN JOURNAL OF MEDICAL INFORMATICS   Vol. 44 ( 1 ) page: 29 - 37   2024.4

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    Authorship:Lead author, Corresponding author   Language:Japanese   Publishing type:Research paper (scientific journal)  

  4. Artificial intelligence in a prediction model for post-ERCP pancreatitis Reviewed International journal

    Takahashi Hidekazu, Eizaburo Ohno, Taiki Furukawa, Kentaro Yamao, Takuya Ishikawa, Yasuyuki Mizutani, Tadashi Iida, Yoshimune Shiratori, Shintaro Oyama, Junji Koyama, Kensaku Mori, Yuichiro Hayashi, Masahiro Oda, Takahisa Suzuki, Hiroki Kawashima

    Digestive Endoscopy   Vol. 36 ( 4 ) page: 463 - 472   2024.4

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    OBJECTIVES: In this study we aimed to develop an artificial intelligence-based model for predicting postendoscopic retrograde cholangiopancreatography (ERCP) pancreatitis (PEP). METHODS: We retrospectively reviewed ERCP patients at Nagoya University Hospital (NUH) and Toyota Memorial Hospital (TMH). We constructed two prediction models, a random forest (RF), one of the machine-learning algorithms, and a logistic regression (LR) model. First, we selected features of each model from 40 possible features. Then the models were trained and validated using three fold cross-validation in the NUH cohort and tested in the TMH cohort. The area under the receiver operating characteristic curve (AUROC) was used to assess model performance. Finally, using the output parameters of the RF model, we classified the patients into low-, medium-, and high-risk groups. RESULTS: A total of 615 patients at NUH and 544 patients at TMH were enrolled. Ten features were selected for the RF model, including albumin, creatinine, biliary tract cancer, pancreatic cancer, bile duct stone, total procedure time, pancreatic duct injection, pancreatic guidewire-assisted technique without a pancreatic stent, intraductal ultrasonography, and bile duct biopsy. In the three fold cross-validation, the RF model showed better predictive ability than the LR model (AUROC 0.821 vs. 0.660). In the test, the RF model also showed better performance (AUROC 0.770 vs. 0.663, P = 0.002). Based on the RF model, we classified the patients according to the incidence of PEP (2.9%, 10.0%, and 23.9%). CONCLUSION: We developed an RF model. Machine-learning algorithms could be powerful tools to develop accurate prediction models.

    DOI: 10.1111/den.14622

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  5. Predictive Models for Palliative Care Needs of Advanced Cancer Patients Receiving Chemotherapy. Reviewed International journal

    Arisa Kawashima, Taiki Furukawa, Takahiro Imaizumi, Akemi Morohashi, Mariko Hara, Satomi Yamada, Masayo Hama, Aya Kawaguchi, Kazuki Sato

    Journal of pain and symptom management   Vol. 67 ( 4 ) page: 306 - 316.e6   2024.4

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    CONTEXT: Early palliative care is recommended within eight-week of diagnosing advanced cancer. Although guidelines suggest routine screening to identify cancer patients who could benefit from palliative care, implementing screening can be challenging due to understaffing and time constraints. OBJECTIVES: To develop and evaluate machine learning models for predicting specialist palliative care needs in advanced cancer patients undergoing chemotherapy, and to investigate if predictive models could substitute screening tools. METHODS: We conducted a retrospective cohort study using supervised machine learning. The study included patients aged 18 or older, diagnosed with metastatic or stage IV cancer, who underwent chemotherapy and distress screening at a designated cancer hospital in Japan from April 1, 2018, to March 31, 2023. Specialist palliative care needs were assessed based on distress screening scores and expert evaluations. Data sources were hospital's cancer registry, health claims database, and nursing admission records. The predictive model was developed using XGBoost, a machine learning algorithm. RESULTS: Out of the 1878 included patients, 561 were analyzed. Among them, 114 (20.3%) exhibited needs for specialist palliative care. After under-sampling to address data imbalance, the models achieved an Area Under the Curve (AUC) of 0.89 with 95.8% sensitivity and a specificity of 71.9%. After feature selection, the model retained five variables, including the patient-reported pain score, and showcased an 0.82 AUC. CONCLUSION: Our models could forecast specialist palliative care needs for advanced cancer patients on chemotherapy. Using five variables as predictors could replace screening tools and has the potential to contribute to earlier palliative care.

    DOI: 10.1016/j.jpainsymman.2024.01.009

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  6. Mild elevation of pulmonary vascular resistance predicts mortality regardless of mean pulmonary artery pressure in mild interstitial lung disease Reviewed International journal

    Tomonori Sato, Taiki Furukawa, Ryo Teramachi, Jun Fukihara, Yasuhiko Yamano, Toshiki Yokoyama, Toshiaki Matsuda, Kensuke Kataoka, Tomoki Kimura, Koji Sakamoto, Makoto Ishii, Yasuhiro Kondoh

    Thorax   Vol. 79 ( 5 ) page: 422 - 429   2024.2

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    Authorship:Lead author   Language:English   Publishing type:Research paper (scientific journal)  

    DOI: 10.1136/thorax-2023-220179

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  7. Changes in patient-reported outcomes in patients with non-idiopathic pulmonary fibrosis fibrotic interstitial lung disease and progressive pulmonary fibrosis Reviewed International journal

    Reoto Takei, Toshiaki Matsuda, Jun Fukihara, Hajime Sasano, Yasuhiko Yamano, Toshiki Yokoyama, Kensuke Kataoka, Tomoki Kimura, Atsushi Suzuki, Taiki Furukawa, Junya Fukuoka, Takeshi Johkoh, Yasuhiro Kondoh

    Frontiers in Medicine   Vol. 10   page: 1067149   2023.6

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    Language:English   Publishing type:Research paper (scientific journal)   Publisher:Frontiers Media SA  

    Background

    Health-related quality of life (HRQoL) captures different aspects of the fibrotic interstitial lung disease (FILD) evaluation from the patient’s perspective. However, little is known about how HRQoL changes in patients with non-idiopathic pulmonary fibrosis (IPF) FILD, especially in those with progressive pulmonary fibrosis (PPF). The aim of this study is to clarify whether HRQoL deteriorates in patients with non-IPF FILD and to evaluate the differences in the changes in HRQoL between those with and without PPF.

    Methods

    We collected data from consecutive patients with non-IPF FILD and compared annual changes in HRQoL over 2 years between patients with PPF and those without. The St George’s respiratory questionnaire (SGRQ) and COPD assessment test (CAT) were used to assess HRQoL. Changes in the SGRQ and CAT scores for 24 months from baseline were evaluated with a mixed-effect model for repeated measures.

    Results

    A total of 396 patients with non-IPF FILD were reviewed. The median age was 65 years and 202 were male (51.0%). The median SGRQ and CAT scores were 29.6 and 11, respectively. Eighty-six (21.7%) showed PPF. Both SGRQ and CAT scores were significantly deteriorated in patients with PPF compared to those without PPF (p &amp;lt; 0.01 for both). Clinically important deterioration in the SGRQ and CAT scores were observed in 40.0 and 35.7% of patients with PPF and 11.7 and 16.7% of those without, respectively. PPF was significantly associated with clinically important deterioration in the SGRQ score (odds ratio 5.04; 95%CI, 2.61–9.76, p &amp;lt; 0.01) and CAT score (odds ratio 2.78; 95%CI, 1.27–6.06, p = 0.02).

    Conclusion

    The SGRQ and CAT scores were significantly deteriorated in patients with non-IPF FILD and PPF. Considering an evaluation of HRQoL would be needed when assessing PPF.

    DOI: 10.3389/fmed.2023.1067149

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  8. DEVELOPMENT OF A MACHINE-LEARNING MODEL FOR PREDICTING POST-ERCP PANCREATITIS Reviewed International journal

    Takahashi Hidekazu, Eizaburo Ohno, Taiki Furukawa, Kentaro Yamao, Takuya Ishikawa, Yasuyuki Mizutani, Tadashi Iida, Yoshimune Shiratori, Shintaro Oyama, Junji Koyama, Kensaku Mori, Yuichiro Hayashi, Masahiro Oda, Takahisa Suzuki, Hiroki Kawashima

    Gastrointestinal Endoscopy   Vol. 97 ( 6 ) page: AB656 - AB656   2023.6

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    DOI: 10.1016/j.gie.2023.04.1087

  9. 機械学習による院内死亡予測モデルの特性分析 Reviewed

    古川 大記, 三沢 翔太郎, 大山 慎太郎, 狩野 竜示, 鑓水 大和, 谷口 友紀, 小野田 浩平, 佐藤 菊枝, 白鳥 義宗

    日本医療情報学会春季学術大会プログラム・抄録集   Vol. 27回   page: 74 - 75   2023.6

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    Authorship:Lead author   Language:Japanese   Publishing type:Research paper (other academic)   Publisher:(一社)日本医療情報学会  

  10. In-Hospital Cancer Mortality Prediction by Multimodal Learning of Non-English Clinical Texts International journal

    Oyama S., Furukawa T., Misawa S., Kano R., Yarimizu H., Taniguchi T., Onoda K., Sato K., Shiratori Y.

    Studies in Health Technology and Informatics   Vol. 302   page: 821 - 822   2023.5

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    Predicting important outcomes in patients with complex medical conditions using multimodal electronic medical records remains challenge. We trained a machine learning model to predict the inpatient prognosis of cancer patients using EMR data with Japanese clinical text records, which has been considered difficult due to its high context. We confirmed high accuracy of the mortality prediction model using clinical text in addition to other clinical data, suggesting applicability of this method to cancer.

    DOI: 10.3233/SHTI230276

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  11. Interstitial pneumonia with autoimmune features and histologic usual interstitial pneumonia treated with anti-fibrotic versus immunosuppressive therapy Reviewed International journal

    Yasuhiko Yamano, Kensuke Kataoka, Reoto Takei, Hajime Sasano, Toshiki Yokoyama, Toshiaki Matsuda, Tomoki Kimura, Yuta Mori, Taiki Furukawa, Junya Fukuoka, Takeshi Johko, Yasuhiro Kondoh

    Respiratory Investigation   Vol. 61 ( 3 ) page: 297 - 305   2023.5

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    Background: Therapeutic strategies in patients with interstitial pneumonia with autoimmune features (IPAF) and histological usual interstitial pneumonia (UIP) pattern (IPAF-UIP) have not been thoroughly evaluated. We compared the therapeutic efficacy of anti-fibrotic therapy with that of immunosuppressive treatment for patients with IPAF-UIP. Methods: In this retrospective case series, we identified consecutive IPAF-UIP patients treated with anti-fibrotic therapy or immunosuppressive therapy. Clinical characteristics, one-year treatment response, acute exacerbation, and survival were studied. We performed a stratified analysis by the pathological presence or absence of inflammatory cell infiltration. Results: Twenty-seven patients with anti-fibrotic therapy and 29 with immunosuppressive treatment were included. There was a significant difference in one-year forced vital capacity (FVC) change between patients with anti-fibrotic treatment (4 in 27 improved, 12 stable, and 11 worsened) and those with immunosuppressive treatment (16 in 29 improved, eight stable, and five worsened) (p = 0.006). There was also a significant difference in one-year St George's Respiratory Questionnaire (SGRQ) change between patients with anti-fibrotic therapy (2 in 27 improved, ten stable, and 15 worsened) and those with immunosuppressive treatment (14 in 29 improved, 12 stable, and worsened) (p < 0.001). There was no significant difference in survival between the groups (p = 0.32). However, in the subgroup with histological inflammatory cell infiltration, survival was significantly better with immunosuppressive therapy (p = 0.02). Conclusion: In IPAF-UIP, immunosuppressive therapy seemed to be superior to anti-fibrotic treatment in terms of therapeutic response, and provided better outcomes in the histological inflammatory subgroup. Further prospective studies are needed to clarify the therapeutic strategy in IPAF-UIP.

    DOI: 10.1016/j.resinv.2023.01.007

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  12. Prevalence and prognosis of chronic fibrosing interstitial lung diseases with a progressive phenotype. Reviewed International journal

    Reoto Takei, Kevin K Brown, Yasuhiko Yamano, Kensuke Kataoka, Toshiki Yokoyama, Toshiaki Matsuda, Tomoki Kimura, Atsushi Suzuki, Taiki Furukawa, Junya Fukuoka, Takeshi Johkoh, Yoshihito Goto, Yasuhiro Kondoh

    Respirology (Carlton, Vic.)   Vol. 27 ( 5 ) page: 333 - 340   2022.5

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    BACKGROUND AND OBJECTIVE: The development of clinically progressive fibrosis complicates a wide array of interstitial lung diseases (ILDs). However, there are limited data regarding its prevalence and prognosis. METHODS: We analysed consecutive patients seen for initial evaluation of a fibrosing form of ILD (FILD). Patients were evaluated for evidence of progressive fibrosis over the first 24 months of follow-up. We defined a progressive phenotype as the presence of at least one of the following: a relative decline in forced vital capacity (FVC) of ≥10%; a relative decline in FVC of ≥5%-<10% with a relative decline in diffusing capacity of the lung for carbon monoxide of ≥15%, increased fibrosis on HRCT or progressive symptoms. RESULTS: Eight hundred and forty-four patients (397 with idiopathic pulmonary fibrosis [IPF] and 447 non-IPF FILD) made up the final analysis cohort. Three hundred and fifty-five patients (42.1%) met the progressive phenotype criteria (59.4% of IPF patients and 26.6% of non-IPF FILD patients, p <0.01). In both IPF and non-IPF FILD, transplantation-free survival differed between patients with a progressive phenotype and those without (p <0.01). Multivariable analysis showed that a progressive phenotype was an independent predictor of transplantation-free survival (hazard ratio [HR]: 3.36, 95% CI: 2.68-4.23, p <0.01). Transplantation-free survival did not differ between non-IPF FILD with a progressive phenotype and IPF (HR: 1.12, 95% CI: 0.85-1.48, p = 0.42). CONCLUSION: Over one-fourth of non-IPF FILD patients develop a progressive phenotype compared to approximately 60% of IPF patients. The survival of non-IPF FILD patients with a progressive phenotype is similar to IPF.

    DOI: 10.1111/resp.14245

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  13. The prognostic value of the COPD Assessment Test in fibrotic interstitial lung disease. Reviewed International journal

    Toshiaki Matsuda, Yasuhiro Kondoh, Taiki Furukawa, Atsushi Suzuki, Reoto Takei, Hajime Sasano, Yasuhiko Yamano, Toshiki Yokoyama, Kensuke Kataoka, Tomoki Kimura

    Respiratory investigation   Vol. 60 ( 1 ) page: 99 - 107   2022.1

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    BACKGROUND: The COPD Assessment Test (CAT) has been studied as a measure of health status in idiopathic pulmonary fibrosis (IPF) and interstitial lung disease associated with connective tissue disease. However, its prognostic value is unknown. The present study explored the association between CAT score and mortality in fibrotic interstitial lung disease (FILD), including IPF and other forms of ILD. METHODS: We retrospectively analyzed 501 consecutive patients with FILD who underwent clinical assessment, including pulmonary function test and CAT. The association between CAT score and 3-year mortality was assessed using Cox proportional hazard analysis, Kaplan-Meier plots, and the log-rank test for trend. To handle missing data, the imputed method was used. RESULTS: The patients' median age was 68 years, and 320 were male (63.9%). Regarding CAT severity, 203 patients had a low impact level (score <10), 195 had a medium level (10-20), 80 had a high level (21-30), and 23 had a very high level (31-40). During the 3-year study period, 118 patients died. After adjusting for age, sex, forced vital capacity, diffusion capacity for carbon monoxide, IPF diagnosis, and usual interstitial pneumonia pattern on high-resolution computed tomography, the CAT score was significantly associated with 3-year mortality (hazard ratio in increments of 10 points: 1.458, 95% confidence interval 1.161-1.830; p < 0.001). In addition, patients with high and very high impact levels had twofold and threefold higher mortality risk than those with low levels, respectively. CONCLUSION: The CAT has prognostic value in FILD.

    DOI: 10.1016/j.resinv.2021.07.007

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  14. 新型コロナウイルス感染症(COVID-19)拡大におけるDPCデータを用いた受療状況分析 Reviewed

    佐藤 菊枝, 小林 大介, 山下 暁士, 大山 慎太郎, 古川 大記, 白鳥 義宗

    医療情報学連合大会論文集   Vol. 41回   page: 570 - 572   2021.11

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    Language:Japanese   Publishing type:Research paper (conference, symposium, etc.)   Publisher:(一社)日本医療情報学会  

  15. 東海国立大学機構が実現しようとしているSociety5.0 Invited Reviewed

    白鳥 義宗, 大山 慎太郎, 山下 暁士, 佐藤 菊枝, 小林 大介, 舩田 千秋, 古川 大記, 菅野 亜紀, 森 龍太郎, 矢部 大介

    医療情報学連合大会論文集   Vol. 41回   page: 113 - 115   2021.11

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    Language:Japanese   Publishing type:Research paper (international conference proceedings)   Publisher:(一社)日本医療情報学会  

  16. The current issues and future perspective of artificial intelligence for developing new treatment strategy in non-small cell lung cancer: harmonization of molecular cancer biology and artificial intelligence. Reviewed International journal

    Ichidai Tanaka, Taiki Furukawa, Masahiro Morise

    Cancer cell international   Vol. 21 ( 1 ) page: 454 - 454   2021.8

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    Comprehensive analysis of omics data, such as genome, transcriptome, proteome, metabolome, and interactome, is a crucial technique for elucidating the complex mechanism of cancer onset and progression. Recently, a variety of new findings have been reported based on multi-omics analysis in combination with various clinical information. However, integrated analysis of multi-omics data is extremely labor intensive, making the development of new analysis technology indispensable. Artificial intelligence (AI), which has been under development in recent years, is quickly becoming an effective approach to reduce the labor involved in analyzing large amounts of complex data and to obtain valuable information that is often overlooked in manual analysis and experiments. The use of AI, such as machine learning approaches and deep learning systems, allows for the efficient analysis of massive omics data combined with accurate clinical information and can lead to comprehensive predictive models that will be desirable for further developing individual treatment strategies of immunotherapy and molecular target therapy. Here, we aim to review the potential of AI in the integrated analysis of omics data and clinical information with a special focus on recent advances in the discovery of new biomarkers and the future direction of personalized medicine in non-small lung cancer.

    DOI: 10.1186/s12935-021-02165-7

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  17. Prognosis in Non-IPF with Progressive Fibrotic Phenotype Results in Similar Prognosis in IPF Reviewed International journal

    Ito T., Takei R., Sasano H., Yamano Y., Yokoyama T., Matsuda T., Kimura T., Furukawa T., Johkoh T., Fukuoka J., Kondoh Y.

    AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE   Vol. 203 ( 9 )   2021.5

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  18. Impact of post-capillary pulmonary hypertension on mortality in interstitial lung disease. Reviewed International journal

    Ryo Teramachi, Hiroyuki Taniguchi, Yasuhiro Kondoh, Tomoki Kimura, Kensuke Kataoka, Toshiki Yokoyama, Taiki Furukawa, Mitsuaki Yagi, Koji Sakamoto, Naozumi Hashimoto, Yoshinori Hasegawa

    Respiratory investigation   Vol. 59 ( 3 ) page: 342 - 349   2021.5

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    Language:English   Publishing type:Research paper (scientific journal)  

    BACKGROUND: Pulmonary hypertension (PH) influences mortality in patients with interstitial lung disease (ILD). Almost all studies on patients with ILD, have focused on the clinical impact of pre-capillary PH on survival. Therefore, little is known about the influence of post-capillary PH. We aimed to assess the prevalence of post-capillary PH and its clinical impact on survival in patients with ILD, followed by comparison with pre-capillary PH. METHODS: This retrospective study enrolled 1152 patients with ILD who were diagnosed with PH using right heart catheterization between May 2007 and December 2015. We analyzed the demographics and composite outcomes (defined as death from any cause or lung transplantation) of patients with post-capillary PH and compared them with patients with pre-capillary PH. RESULTS: Thirty-two (20%) of the 157 patients with ILD-PH were diagnosed with post-capillary PH. Patients with post-capillary PH had significantly lower modified Medical Research Council scores, higher diffusion capacity for carbon monoxide, higher resting PaO2, lower pulmonary vascular resistance (PVR), and higher lowest oxygen saturation during the 6-min walk test compared to those with pre-capillary PH. Cardiovascular diseases were associated with a higher risk of mortality in patients with post-capillary PH. Multivariate Cox proportional hazards analysis demonstrated no significant difference between the composite outcomes in pre-capillary and post-capillary PH, while PVR and the ILD Gender-Age-Physiology Index were significantly associated with the composite outcome. CONCLUSIONS: We found that approximately one-fifth of patients with ILD-PH were diagnosed with post-capillary PH, and that PVR and not post-capillary PH was associated with mortality.

    DOI: 10.1016/j.resinv.2020.12.010

    Web of Science

    Scopus

    PubMed

  19. Smart hospital infrastructure: geomagnetic in-hospital medical worker tracking. Reviewed International journal

    Keiko Yamashita, Shintaro Oyama, Tomohiro Otani, Satoshi Yamashita, Taiki Furukawa, Daisuke Kobayashi, Kikue Sato, Aki Sugano, Chiaki Funada, Kensaku Mori, Naoki Ishiguro, Yoshimune Shiratori

    Journal of the American Medical Informatics Association : JAMIA   Vol. 28 ( 3 ) page: 477 - 486   2021.3

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    Language:English   Publishing type:Research paper (scientific journal)  

    PURPOSE: Location visualization is essential for locating people/objects, improving efficiency, and preventing accidents. In hospitals, Wi-Fi, Bluetooth low energy (BLE) Beacon, indoor messaging system, and similar methods have generally been used for tracking, with Wi-Fi and BLE being the most common. Recently, nurses are increasingly using mobile devices, such as smartphones and tablets, while shifting. The accuracy when using Wi-Fi or BLE may be affected by interference or multipath propagation. In this research, we evaluated the positioning accuracy of geomagnetic indoor positioning in hospitals. MATERIALS AND METHODS: We compared the position measurement accuracy of a geomagnetic method alone, Wi-Fi alone, BLE beacons alone, geomagnetic plus Wi-Fi, and geomagnetic plus BLE in a general inpatient ward, using a geomagnetic positioning algorithm by GiPStech. The existing Wi-Fi infrastructure was used, and 20 additional BLE beacons were installed. Our first experiment compared these methods' accuracy for 8 test routes, while the second experiment verified a combined geomagnetic/BLE beacon method using 3 routes based on actual daily activities. RESULTS: The experimental results demonstrated that the most accurate method was geomagnetic/BLE, followed by geomagnetic/Wi-Fi, and then geomagnetic alone. DISCUSSION: The geomagnetic method's positioning accuracy varied widely, but combining it with BLE beacons reduced the average position error to approximately 1.2 m, and the positioning accuracy could be improved further. We believe this could effectively target humans (patients) where errors of up to 3 m can generally be tolerated. CONCLUSION: In conjunction with BLE beacons, geomagnetic positioning could be sufficiently effective for many in-hospital localization tasks.

    DOI: 10.1093/jamia/ocaa204

    Web of Science

    Scopus

    PubMed

  20. Serum mitochondrial DNA predicts the risk of acute exacerbation and progression of idiopathic pulmonary fibrosis. Reviewed International coauthorship International journal

    Koji Sakamoto, Taiki Furukawa, Yasuhiko Yamano, Kensuke Kataoka, Ryo Teramachi, Anjali Walia, Atsushi Suzuki, Masahide Inoue, Yoshio Nakahara, Changwan Ryu, Naozumi Hashimoto, Yasuhiro Kondoh

    The European respiratory journal   Vol. 57 ( 1 )   2021.1

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    Authorship:Lead author   Language:English   Publishing type:Research paper (scientific journal)  

    DOI: 10.1183/13993003.01346-2020

    Web of Science

    Scopus

    PubMed

  21. Hand hygiene monitoring by positioning technology utilizing IoT devices. Reviewed International journal

    Keiko Yamashita, Shintaro Oyama, Satoshi Yamashita, Chiaki Funada, Kikue Sato, Taiki Furukawa, Aki Sugano, Daisuke Kobayashi, Hiroshi Tomozawa, Yuji Sakamoto, Yoshinori Ideno, Kensaku Mori, Yoshimune Shiratori

    AMIA     2021

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    Language:English   Publishing type:Research paper (international conference proceedings)  

    Other Link: https://dblp.uni-trier.de/rec/conf/amia/2021

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Books 10

  1. 呼吸器内科学レビュー 2024-’25

    古川大記( Role: Contributor ,  間質性肺炎診療と新テクノロジー)

    総合医学社  2023.11 

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    Language:Japanese

  2. 【最新主要文献とガイドラインでみる 呼吸器内科学レビュー 2024-'25】(XIV章)新テクノロジーと肺疾患 間質性肺炎診療と新テクノロジー

    古川 大記( Role: Contributor)

    (株)総合医学社  2023.11  ( ISBN:9784883784769

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    Language:Japanese Book type:Scholarly book

    <最近の研究動向とガイドライン>●間質性肺疾患(interstitial lung disease:ILD)では,臨床的な知識・判断を補助する医療ソフトウェアアルゴリズムの発展,クライオバイオプシーをはじめとする内視鏡の開発と臨床応用,ウェアラブルデバイスを用いた疾病管理の研究開発,などテクノロジーの発展が進んでいる.●執筆時点(2023年7月)の一般社会では,Chat GPTに代表される大規模言語モデル(large language models:LLM)を用いた人工知能(AI)が物凄い速さで普及しているが,形態学的な特徴を捉えることが有用であるILDでは,胸部放射線画像や病理画像から疾患の特徴を捉えるAIの一種である機械学習や,機械学習の一種である深層学習の研究開発が進んできた.●また,ILDの診療に目を向けると,本邦では全国規模のILDレジストリが進行中であり,遠隔医療とAIを実臨床に応用できる可能性が期待されている.●本稿では,特にILDの診療におけるテクノロジーの発展を主眼に,近年の動向を述べる.(著者抄録)

  3. 間質性肺炎のレジストリ研究

    古川 大記( Role: Contributor)

    (有)科学評論社  2022.12 

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    Language:Japanese Book type:Scholarly book

  4. 呼吸器内科 間質性肺疾患のAI画像診断の開発状況と今後の展望について 実臨床で使用できるようにAI開発とシステム構築が進められている

    古川 大記, 伊藤 健太郎

    (株)日本医事新報社  2022.10 

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    Language:Japanese Book type:Scholarly book

  5. 【びまん性肺疾患における多職種合議(MDD)診断とAI支援の現在と未来】MDD診断へのAI「画像診断」支援の現状と可能性について

    古川 大記, 寺町 涼( Role: Contributor)

    (有)科学評論社  2022.2 

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    Language:Japanese Book type:Scholarly book

  6. 肺線維症の病態に関連する2つの新規マーカー分子:メフリンとミトコンドリアDNA

    阪本 考司, 橋本 直純, 中原 義夫, 古川 大記( Role: Contributor)

    (有)科学評論社  2022.2 

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    Language:Japanese Book type:Scholarly book

  7. 間質性肺炎診療と新テクノロジー

    古川大記( Role: Sole author)

    呼吸器内科学レビュー 2022-’23  2021.12 

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    Language:Japanese Book type:Scholarly book

  8. 【最新主要文献とガイドラインでみる呼吸器内科学レビュー 2022-'23】(XIV章)新テクノロジーと肺疾患 間質性肺炎診療と新テクノロジー

    古川 大記

    (株)総合医学社  2021.12  ( ISBN:9784883787470

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    Language:Japanese Book type:Scholarly book

    間質性肺炎(ILD)の正確な診断には、呼吸器内科医、放射線科医、病理医の集学的議論(MDD)、外科的肺生検やクライオバイオプシーが重要であるが、近年、新しいテクノロジーを用いた診療が広がりつつある。機械学習と深層学習を用いたILDの画像所見の抽出、ゲノム評価、診断と治療効果予測について概説した。また、間質性肺炎のAI(人工知能)創薬、遠隔診療(オンラインMDD診断)、在宅モニタリングと呼吸器リハビリテーションについて述べた。

  9. 間質性肺炎のAI診断

    古川大記, 大山慎太郎( Role: Joint author)

    呼吸器ジャーナル  2021.8 

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    Language:Japanese Book type:Scholarly book

  10. 【間質性肺炎 徹底討論!-鳥からは逃げられない過敏性肺炎,放置してよいのかILA】最近の話題 間質性肺炎のAI診断

    古川 大記, 大山 慎太郎( Role: Contributor)

    (株)医学書院  2021.8  ( ISBN:9784260029087

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    Language:Japanese Book type:Scholarly book

    <文献概要>Point ・テクノロジーの発展に伴って医療AIも開発されてきたが,臨床応用は注意が必要である.・間質性肺炎の画像特徴を捉えるAIから,診断・予後予測AIも開発されてきた.・全国規模の間質性肺炎レジストリが進行中であり,様々な研究結果が期待される.

    Other Link: https://search.jamas.or.jp/index.php?module=Default&action=Link&pub_year=2021&ichushi_jid=J06862&link_issn=&doc_id=20210813210020&doc_link_id=10.11477%2Fmf.1437200484&url=https%3A%2F%2Fdoi.org%2F10.11477%2Fmf.1437200484&type=%88%E3%8F%91.jp_%83I%81%5B%83%8B%83A%83N%83Z%83X&icon=https%3A%2F%2Fjk04.jamas.or.jp%2Ficon%2F00024_2.gif

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MISC 18

  1. ロボット支援下手術における患者受療動向と地域医療への影響評価 Reviewed

    佐藤 菊枝, 小林 大介, 古川 大記, 白鳥 義宗

    医療情報学連合大会論文集   Vol. 43回   page: 1172 - 1174   2023.11

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    Language:Japanese   Publishing type:Research paper, summary (national, other academic conference)   Publisher:(一社)日本医療情報学会  

  2. In-Hospital Cancer Mortality Prediction by Multimodal Learning of Non-English Clinical Texts. Reviewed International journal

    Shintaro Oyama, Taiki Furukawa, Shotaro Misawa, Ryuji Kano, Hirokazu Yarimizu, Tomoki Taniguchi, Kohei Onoda, Kikue Sato, Yoshimune Shiratori

    Studies in health technology and informatics   Vol. 302   page: 821 - 822   2023.5

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    Language:English   Publishing type:Article, review, commentary, editorial, etc. (international conference proceedings)  

    Predicting important outcomes in patients with complex medical conditions using multimodal electronic medical records remains challenge. We trained a machine learning model to predict the inpatient prognosis of cancer patients using EMR data with Japanese clinical text records, which has been considered difficult due to its high context. We confirmed high accuracy of the mortality prediction model using clinical text in addition to other clinical data, suggesting applicability of this method to cancer.

    DOI: 10.3233/SHTI230276

    Scopus

    PubMed

  3. Sentence Extraction using Outcome Prediction Model Trained from Clinical Data Reviewed

    MISAWA Shotaro, FURUKAWA Taiki, OYAMA Shintaro, KANO Ryuji, YARIMIZU Hirokazu, TANIGUCHI Tomoki, ONODA Kohei, SATO Kikue, SHIRATORI Yoshimune

    Proceedings of the Annual Conference of JSAI   Vol. JSAI2023 ( 0 ) page: 3Xin404 - 3Xin404   2023

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    Language:Japanese   Publishing type:Research paper, summary (national, other academic conference)   Publisher:The Japanese Society for Artificial Intelligence  

    <p>This study aims to extract clinically important sentences from accumulated medical documents to assist medical workers to search documents. Unsupervised document summarization methods such as LexRank are commonly used in situations where it is difficult to prepare training data. However, these methods are based on a hypothesis that important topics are frequently referred which does not match the medical document. Many previous studies have predicted the length of hospital stay and mortality using clinical data, and we propose these outcomes can be distant labels of clinical importance. Namely, an output from the outcome prediction model becomes high when an input sentence is clinically important. Therefore, in this study, we propose a model to extract clinically important sentences using an outcome prediction model. Experimental results show our text extraction model with an outcome prediction model can summarize more accurately than the conventional models.</p>

    DOI: 10.11517/pjsai.jsai2023.0_3xin404

    CiNii Research

  4. 間質性肺炎のレジストリ研究

    古川 大記

    呼吸器内科   Vol. 42 ( 6 ) page: 654 - 657   2022.12

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    Authorship:Lead author, Last author, Corresponding author   Language:Japanese   Publishing type:Article, review, commentary, editorial, etc. (scientific journal)   Publisher:(有)科学評論社  

  5. 呼吸器内科 間質性肺疾患のAI画像診断の開発状況と今後の展望について 実臨床で使用できるようにAI開発とシステム構築が進められている

    古川 大記, 伊藤 健太郎

    日本医事新報   ( 5136 ) page: 48 - 49   2022.10

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    Authorship:Lead author, Corresponding author   Language:Japanese   Publishing type:Article, review, commentary, editorial, etc. (trade magazine, newspaper, online media)   Publisher:(株)日本医事新報社  

  6. 【びまん性肺疾患における多職種合議(MDD)診断とAI支援の現在と未来】MDD診断へのAI「画像診断」支援の現状と可能性について

    古川 大記, 寺町 涼

    呼吸器内科   Vol. 41 ( 2 ) page: 180 - 184   2022.2

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    Authorship:Lead author, Last author, Corresponding author   Language:Japanese   Publishing type:Article, review, commentary, editorial, etc. (scientific journal)   Publisher:(有)科学評論社  

  7. 肺線維症の病態に関連する2つの新規マーカー分子:メフリンとミトコンドリアDNA

    阪本 考司, 橋本 直純, 中原 義夫, 古川 大記

    呼吸器内科   Vol. 41 ( 2 ) page: 197 - 201   2022.2

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    Language:Japanese   Publishing type:Article, review, commentary, editorial, etc. (scientific journal)   Publisher:(有)科学評論社  

  8. MDD診断へのAI「画像診断」支援の現状と可能性について. Invited

    古川 大記

    呼吸器内科   Vol. 41(2)   page: 180 - 184   2022

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    Authorship:Lead author, Last author, Corresponding author   Language:Japanese   Publishing type:Article, review, commentary, editorial, etc. (trade magazine, newspaper, online media)  

    CiNii Research

  9. 【最新主要文献とガイドラインでみる呼吸器内科学レビュー 2022-'23】(XIV章)新テクノロジーと肺疾患 間質性肺炎診療と新テクノロジー

    古川 大記

    呼吸器内科学レビュー   Vol. 2022-'23   page: 327 - 332   2021.12

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    Authorship:Lead author, Last author, Corresponding author   Language:Japanese   Publishing type:Article, review, commentary, editorial, etc. (scientific journal)   Publisher:(株)総合医学社  

    間質性肺炎(ILD)の正確な診断には、呼吸器内科医、放射線科医、病理医の集学的議論(MDD)、外科的肺生検やクライオバイオプシーが重要であるが、近年、新しいテクノロジーを用いた診療が広がりつつある。機械学習と深層学習を用いたILDの画像所見の抽出、ゲノム評価、診断と治療効果予測について概説した。また、間質性肺炎のAI(人工知能)創薬、遠隔診療(オンラインMDD診断)、在宅モニタリングと呼吸器リハビリテーションについて述べた。

  10. 東海国立大学機構が実現しようとしているSociety5.0 Reviewed

    白鳥 義宗, 大山 慎太郎, 山下 暁士, 佐藤 菊枝, 小林 大介, 舩田 千秋, 古川 大記, 菅野 亜紀, 森 龍太郎, 矢部 大介

    医療情報学連合大会論文集   Vol. 41回   page: 113 - 115   2021.11

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    Language:Japanese   Publishing type:Research paper, summary (national, other academic conference)   Publisher:(一社)日本医療情報学会  

  11. 特集 間質性肺炎 徹底討論!-鳥からは逃げられない過敏性肺炎,放置してよいのかILA Ⅳ.最近の話題 間質性肺炎のAI診断

    古川 大記, 大山 慎太郎

    呼吸器ジャーナル   Vol. 69 ( 3 ) page: 450 - 457   2021.8

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    Authorship:Lead author, Corresponding author   Language:Japanese   Publishing type:Article, review, commentary, editorial, etc. (trade magazine, newspaper, online media)   Publisher:株式会社 医学書院  

    DOI: 10.11477/mf.1437200484

    CiNii Research

  12. Prognosis in Non-IPF with Progressive Fibrotic Phenotype Results in Similar Prognosis in IPF Reviewed International journal

    Ito T., Takei R., Sasano H., Yamano Y., Yokoyama T., Matsuda T., Kimura T., Furukawa T., Johkoh T., Fukuoka J., Kondoh Y.

    AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE   Vol. 203 ( 9 )   2021.5

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    Language:English   Publishing type:Research paper, summary (international conference)  

    Web of Science

  13. 間質性肺疾患における肺高血圧予測モデルの構築 Reviewed

    佐藤 智則, 古川 大記, 寺町 涼, 山野 泰彦, 横山 俊樹, 松田 俊明, 片岡 健介, 木村 智樹, 近藤 康博

    日本呼吸器学会誌   Vol. 10 ( 増刊 ) page: 225 - 225   2021.4

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    Language:Japanese   Publishing type:Research paper, summary (national, other academic conference)   Publisher:(一社)日本呼吸器学会  

  14. Construction of a data collection platform based on the regional medical care data infrastructure Reviewed

    佐藤菊枝, 小林大介, 小林大介, 山下暁士, 大山慎太郎, 古川大記, 白鳥義宗

    日本医療情報学会春季学術大会プログラム・抄録集   Vol. 25th   2021

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  15. Outcome prediction using Integrated Clinical Database toward Automatic Generation of Clinical Summaries Reviewed

    古川大記, 三沢翔太郎, 大山慎太郎, 佐藤菊枝, 狩野竜示, 鑓水大和, 白鳥義宗

    医療情報学連合大会論文集(CD-ROM)   Vol. 41st   2021

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    Authorship:Lead author, Corresponding author   Language:Japanese   Publishing type:Research paper, summary (national, other academic conference)  

    J-GLOBAL

  16. Patient access analysis in COVID-19 Pandemic using DPC data Reviewed

    佐藤菊枝, 小林大介, 小林大介, 山下暁士, 大山慎太郎, 古川大記, 白鳥義宗

    医療情報学連合大会論文集(CD-ROM)   Vol. 41st   2021

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    Language:Japanese   Publishing type:Research paper, summary (national, other academic conference)  

    J-GLOBAL

  17. スマートホスピタル構想~医療Society5.0におけるDx研究~ Reviewed

    大山慎太郎, 大山慎太郎, 古川大記, 古川大記, 山下暁士, 山下暁士, 原武史, 原武史

    医療情報学連合大会論文集(CD-ROM)   Vol. 41st   2021

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    Language:Japanese   Publishing type:Research paper, summary (national, other academic conference)  

    J-GLOBAL

  18. 間質性肺炎診療と新テクノロジー

    古川 大記

    最新主要文献とガイドラインでみる 呼吸器内科学レビュー 2022-’23   Vol. -   page: 327 - 332   2021

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    Authorship:Lead author, Last author, Corresponding author   Language:Japanese   Publishing type:Book review, literature introduction, etc.  

    CiNii Research

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Presentations 54

  1. Nationwide All ILD registry with central MDD in Japan; Providing Multidisciplinary ILD diagnoses (PROMISE) study. International conference

    T. Furukawa, Y. Kondoh, S. Oyama, R. Teramachi, H. Hozumi, T. Suda, T. Fujisawa, R. Egashira, T. Johkoh, J. Fukuoka, K. Kataoka, H. Kitamura, O. Nishiyama, M. Okamoto, N. Koshimizu, N. Ishikawa, S. Okamori, Y. Miyazaki, M. Bando, Y. Inoue, T. Ogura, M. Kuwana, H. Tomioka, Y. Nishioka, H. Chiba, M. Ebina, K. Ichikado, Y. Nakanishi, N. Hashimoto, Y. Shiratori, Y. Hasegawa, M. Ishii, The Providing Multidisciplinary ILD Diagnoses [promise] Study Group

    ERS International Congress 2023  2023.9.10 

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    Event date: 2023.9

    Language:English   Presentation type:Oral presentation (general)  

  2. MDD診断とAI診療支援の現状と今後の展望 Invited

    古川 大記

    日本呼吸器学会  2023.4.28  (一社)日本呼吸器学会

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    Event date: 2023.4

    Language:Japanese   Presentation type:Symposium, workshop panel (nominated)  

  3. 間質性肺炎MDD 診断と予後予測の立場から Invited

    古川大記

    第2回日本びまん性肺疾患研究会  2022.10.2 

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    Event date: 2022.10

    Language:Japanese   Presentation type:Symposium, workshop panel (nominated)  

  4. 間質性肺炎の診断・予後予測アルゴリズム構築と社会実装に向けて Invited

    古川大記

    ARO協議会 第9回学術集会  2022.9.16 

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    Event date: 2022.9

    Language:Japanese   Presentation type:Symposium, workshop panel (nominated)  

  5. Development of AI models to predict mortality and long-term hospitalization in pneumonia International conference

    Taiki Furukawa, Shintaro Oyama, Kikue Sato, Shotaro Misawa, Ryuji Kano, Hirokazu Yarimizu, Yoshimmune Shiratori

    ERS International Congress 2022  2022.9.6 

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    Event date: 2022.9

    Language:English   Presentation type:Oral presentation (general)  

  6. 医療用AIとアルゴリズムの構築 Invited

    古川大記

    第7回日本肺高血圧・肺循環学会学術集会  2022.7.3 

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    Event date: 2022.7

    Language:Japanese   Presentation type:Oral presentation (invited, special)  

  7. IPFのAI診断の現状と問題点 Invited

    古川大記

    第62回日本呼吸器学会学術集会  2022.4.24 

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    Event date: 2022.4

    Language:Japanese   Presentation type:Symposium, workshop panel (nominated)  

  8. びまん性肺疾患MDD診断の為の 双方向性Webプラットフォーム構築と 人工知能診断の社会実装に関する前向き研究 Invited

    古川大記

    第62回日本呼吸器学会学術集会  2022.4.23 

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    Event date: 2022.4

    Language:Japanese   Presentation type:Oral presentation (invited, special)  

  9. Interstitial lung disease and BIG-DATA / AI Invited

    Taiki Furukawa

    The 61st Annual Meeting of The Japanese Respiratory Society  2021.4.24 

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    Event date: 2021.4

    Language:Japanese   Presentation type:Oral presentation (invited, special)  

  10. 間質性肺炎診療から考えるAIの活用と今後 Invited

    古川大記

    第10回日本呼吸ケア・リハビリテ-ション学会東海支部学術集会  2024.4.21 

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    Event date: 2024.4

    Language:Japanese   Presentation type:Symposium, workshop panel (nominated)  

  11. 医療者テキスト情報を活用した機械学習によるICU 患者の包括的予後予測

    古川 大記, 三沢 翔太郎, 大山 慎太郎, 鑓水 大和, 小野田 浩平, 佐藤 菊枝, 白鳥 義宗

    第51回日本集中医療学会学術集会  2024.3.14 

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    Event date: 2024.3

    Language:Japanese   Presentation type:Oral presentation (general)  

  12. 呼吸器領域におけるAIの現在と今後の展望 Invited

    古川大記

    日本内科学会東海地方会 第35回教育セミナー  2023.10.15 

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    Event date: 2023.10

    Language:Japanese   Presentation type:Public lecture, seminar, tutorial, course, or other speech  

  13. PROMISE試験からみたびまん性肺疾患リアルワールドデータの現状と課題 Invited

    古川大記, 寺町涼

    第3回日本びまん性肺疾患研究会  2023.9.30 

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    Event date: 2023.9 - 2023.10

    Language:Japanese   Presentation type:Symposium, workshop panel (nominated)  

  14. びまん性肺疾患の集学的合議(MDD)におけるTime Study

    佐藤大介, 古川大記, 小林大介, 上甲剛, 福岡順也, 近藤康博

    第3回日本びまん性肺疾患研究会  2023.10.1 

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    Event date: 2023.9 - 2023.10

    Language:Japanese   Presentation type:Oral presentation (general)  

  15. Diagnostic Criteria Arrangement in Nationwide All ILD Registry with Central MDD in Japan; PROMISE study International conference

    Y. Kondoh, T. Furukawa, R. Teramachi, H. Hozumi, T. Suda, T. Fujisawa, R. Egashira, T. Johkoh, J. Fukuoka, M. Kuwana, T. Ogura, Y. Miyazaki, M. Bando, Y. Inoue, H. Tomioka, Y. Nishioka, H. Chiba, M. Ebina, K. Ichikado, N. Hashimoto, M. Ishii

    ERS International Congress 2023  2023.9.10 

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    Event date: 2023.9

    Language:English   Presentation type:Poster presentation  

  16. Diagnostic Criteria Arrangement in Nationwide All ILD Registry with Central MDD in Japan; PROMISE study International conference

    Kondoh, Y; Furukawa, T; Teramachi, R; Hozumi, H; Suda, T; Fujisawa, T; Egashira, R; Johkoh, T; Fukuoka, J; Kuwana, M; Ogura, T; Miyazaki, Y; Bando, M; Inoue, Y; Tomioka, H; Nishioka, Y; Chiba, H; Ebina, M; Ichikado, K; Hashimoto, N; Ishii, M

    EUROPEAN RESPIRATORY JOURNAL  2023.9.9 

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    Event date: 2023.9

    Language:English   Presentation type:Poster presentation  

    DOI: 10.1183/13993003.congress2023.PA434

  17. Nationwide All ILD registry with central MDD in Japan; Providing Multidisciplinary ILD diagnoses (PROMISE) study. International conference

    Furukawa, T; Kondoh, Y; Oyama, S; Teramachi, R; Hozumi, H; Suda, T; Fujisawa, T; Egashira, R; Johkoh, T; Fukuoka, J; Kataoka, K; Kitamura, H; Nishiyama, O; Okamoto, M; Koshimizu, N; Ishikawa, N; Okamori, S; Miyazaki, Y; Bando, M; Inoue, Y; Ogura, T; Kuwana, M; Tomioka, H; Nishioka, Y; Chiba, H; Ebina, M; Ichikado, K; Nakanishi, Y; Hashimoto, N; Shiratori, Y; Hasegawa, Y; Ishii, M

    EUROPEAN RESPIRATORY JOURNAL  2023.9.9 

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    Event date: 2023.9

    Language:English   Presentation type:Oral presentation (general)  

    DOI: 10.1183/13993003.congress-2023.OA1424

  18. mtDNA in bronchoalveolar lavage fluid is a prognostic factor for IPF and AE-IPF International conference

    Fukihara, J; Sakamoto, K; Furukawa, T; Teramachi, R; Ikeyama, Y; Kataoka, K; Kondoh, Y; Hashimoto, N; Ishii, M

    EUROPEAN RESPIRATORY JOURNAL  2023.9.9 

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    Event date: 2023.9

    Language:English   Presentation type:Poster presentation  

    DOI: 10.1183/13993003.congress-2023.PA1236

  19. 機械学習による院内死亡予測モデルの特性分析

    古川大記, 三沢翔太郎, 大山慎太郎, 狩野竜示, 鑓水大和, 谷口友紀, 小野田浩平, 佐藤菊枝, 白鳥義宗

    第27回 日本医療情報学会春季学術大会  2023.7.1 

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    Event date: 2023.6 - 2023.7

    Language:Japanese   Presentation type:Oral presentation (general)  

  20. 診療データを用いた予測モデルによる文抽出

    三沢翔太郎, 古川大記, 大山慎太郎, 狩野竜示, 鑓水大和, 谷口友紀, 小野田浩平, 佐藤菊枝, 白鳥義宗

    第37回人工知能学会全国大会  2023.6.9 

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    Event date: 2023.6

    Language:Japanese   Presentation type:Poster presentation  

  21. 入院時プロファイルと大気汚染物質による予後予測因子

    佐藤 菊枝, 古川 大記, 小林 大介, 山下 暁士, 白鳥 義宗

    日本医療情報学会春季学術大会  2023.6  (一社)日本医療情報学会

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    Event date: 2023.6

    Language:Japanese  

  22. 機械学習による院内死亡予測モデルの特性分析

    古川 大記, 三沢 翔太郎, 大山 慎太郎, 狩野 竜示, 鑓水 大和, 谷口 友紀, 小野田 浩平, 佐藤 菊枝, 白鳥 義宗

    日本医療情報学会春季学術大会プログラム・抄録集  2023.6  (一社)日本医療情報学会

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    Event date: 2023.6

    Language:Japanese   Presentation type:Oral presentation (general)  

  23. Prognostic factors for Covid-19 on admission profile and air pollutants International conference

    Kikue Sato, Taiki Furukawa, Satoshi Yamashita, Daisuke Kobayashi, Shintaro Oyama, Yoshimune Shiratori

    Medical Informatics Europe 2023  2023.5.22 

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    Event date: 2023.5

    Language:English   Presentation type:Poster presentation  

  24. In-Hospital Mortality Prediction by Multimodal Learning of non-English Clinical Texts

    Shintaro Oyama, Taiki Furukawa, Shotaro Misawa, Ryuji Kano, Hirokazu Yarimizu, Tomoki Taniguchi, Kohei Onoda, Kikue Sato, Yoshimune Shiratori

    Medical Informatics Europe 2023  2023.5.22 

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    Event date: 2023.5

    Language:English   Presentation type:Poster presentation  

  25. 機械学習を活用した抗PD-1/PD-L1抗体治療の生存予測バイオマーカー構築

    神山 潤二, 森瀬 昌宏, 古川 大記, 阪本 考司, 松下 明弘, 松尾 正樹, 浅野 周一, 田中 太郎, 島 浩一郎, 木村 智樹, 近藤 康博, 石井 誠

    第63回日本呼吸器学会学術集会  2023.4.28 

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    Event date: 2023.4

    Language:Japanese   Presentation type:Oral presentation (general)  

  26. Artificial intelligence-based personalized survival prediction using clinical and radiomics features in patients with advanced non-small cell lung cancer International conference

    Koyama, J; Morise, M; Furukawa, T; Oyama, S; Matsuzawa, R; Tanaka, I; Wakahara, K; Yokota, H; Kimura, T; Shiratori, Y; Kondoh, Y; Hashimoto, N

    RESPIROLOGY  2023.2 

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    Event date: 2023.2

    Language:English   Presentation type:Oral presentation (invited, special)  

  27. Artificial intelligence-based personalized survival prediction using clinical and radiomics features in patients with advanced non-small cell lung cancer International conference

    Koyama, J; Morise, M; Furukawa, T; Oyama, S; Matsuzawa, R; Tanaka, I; Wakahara, K; Yokota, H; Kimura, T; Shiratori, Y; Kondoh, Y; Hashimoto, N

    RESPIROLOGY  2023.2 

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    Event date: 2023.2

    Language:English   Presentation type:Oral presentation (general)  

  28. Artificial intelligence-based personalized survival prediction using clinical and radiomics features in patients with advanced non-small cell lung cancer International conference

    Koyama, J; Morise, M; Furukawa, T; Oyama, S; Matsuzawa, R; Tanaka, I; Wakahara, K; Yokota, H; Kimura, T; Shiratori, Y; Kondoh, Y; Hashimoto, N

    RESPIROLOGY  2023.2 

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    Event date: 2023.2

    Language:English   Presentation type:Oral presentation (general)  

  29. Artificial intelligence-based personalized survival prediction using clinical and radiomics features in patients with advanced non-small cell lung cancer International conference

    Koyama, J; Morise, M; Furukawa, T; Oyama, S; Matsuzawa, R; Tanaka, I; Wakahara, K; Yokota, H; Kimura, T; Shiratori, Y; Kondoh, Y; Hashimoto, N

    RESPIROLOGY  2023.2 

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    Event date: 2023.2

    Language:English   Presentation type:Symposium, workshop panel (nominated)  

  30. Artificial intelligence-based personalized survival prediction using clinical and radiomics features in patients with advanced non-small cell lung cancer International conference

    Koyama, J; Morise, M; Furukawa, T; Oyama, S; Matsuzawa, R; Tanaka, I; Wakahara, K; Yokota, H; Kimura, T; Shiratori, Y; Kondoh, Y; Hashimoto, N

    RESPIROLOGY  2023.2 

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    Event date: 2023.2

    Language:English   Presentation type:Oral presentation (general)  

  31. Artificial intelligence-based personalized survival prediction using clinical and radiomics features in patients with advanced non-small cell lung cancer International conference

    Koyama, J; Morise, M; Furukawa, T; Oyama, S; Matsuzawa, R; Tanaka, I; Wakahara, K; Yokota, H; Kimura, T; Shiratori, Y; Kondoh, Y; Hashimoto, N

    RESPIROLOGY  2023.2 

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    Event date: 2023.2

    Language:English   Presentation type:Oral presentation (general)  

  32. In-Hospital Cancer Mortality Prediction by Multimodal Learning of Non-English Clinical Texts. International conference

    Shintaro Oyama, Taiki Furukawa, Shotaro Misawa, Ryuji Kano, Hirokazu Yarimizu, Tomoki Taniguchi, Kohei Onoda, Kikue Sato, Yoshimune Shiratori

    Studies in health technology and informatics  2023 

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    Event date: 2023

    Language:English   Presentation type:Poster presentation  

    Predicting important outcomes in patients with complex medical conditions using multimodal electronic medical records remains challenge. We trained a machine learning model to predict the inpatient prognosis of cancer patients using EMR data with Japanese clinical text records, which has been considered difficult due to its high context. We confirmed high accuracy of the mortality prediction model using clinical text in addition to other clinical data, suggesting applicability of this method to cancer.

    DOI: 10.3233/SHTI230276

    Scopus

    PubMed

  33. 進行非小細胞肺癌における臨床および画像特徴量を用いた機械学習による個別化生存予測モデルの構築

    神山潤二, 森瀬昌宏, 古川大記, 松澤令子, 田中一大, 横田秀夫, 木村智樹, 近藤康博, 橋本直純

    第63回日本肺癌学会学術集会  2022.12.3 

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    Event date: 2022.12

    Language:Japanese   Presentation type:Oral presentation (general)  

  34. Artificial intelligence-based personalized survival prediction using clinical and radiomics features in patients with advanced non-small cell lung cancer International conference

    Junji Koyama, Masahiro Morise, Taiki Furukawa, Shintaro Oyama, Reiko Matsuzawa, Ichidai Tanaka, Keiko Wakahara, Hideo Yokota, Tomoki Kimura, Yoshimune Shiratori, Yasuhiro Kondoh, Naozumi Hashimoto

    The 26th Congress of the Asian Pacific Society of Respirology  2022.11.18 

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    Event date: 2022.11

    Language:English   Presentation type:Oral presentation (general)  

  35. 新型コロナウイルス感染症(COVID-19)患者の入院時プロファイルからの予後予測

    佐藤 菊枝, 古川 大記, 小林 大介, 山下 暁士, 白鳥 義宗

    第42回医療情報学連合大会  2022.11.20  (一社)日本医療情報学会

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    Event date: 2022.11

    Language:Japanese   Presentation type:Poster presentation  

  36. 機械学習を用いたERCP後膵炎リスク予測モデルの構築

    高橋秀和, 古川大記, 大野栄三郎, 石川卓哉, 水谷泰之, 飯田忠, 鈴木貴久, 川嶋啓揮

    Japan Digestive Disease Week 2022  2022.10.28 

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    Event date: 2022.10

    Language:Japanese   Presentation type:Poster presentation  

  37. Development of AI models to predict mortality and long-term hospitalization in pneumonia International conference

    Furukawa, T; Oyama, S; Sato, K; Misawa, S; Kano, R; Yarimizu, H; Shiratori, Y

    EUROPEAN RESPIRATORY JOURNAL  2022.9.4 

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    Event date: 2022.9

    Language:English   Presentation type:Oral presentation (general)  

    DOI: 10.1183/13993003.congress-2022.342

  38. 時系列情報から間質性肺炎急性増悪発症及び予後を予測する深層学習モデルの構築

    寺町 涼, 古川大記, 大山慎太郎, 近藤康博, 烏山昌幸, 片岡健介, 白鳥義宗

    第4回日本メディカルAI学会学術集会  2022.6.10 

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    Event date: 2022.6

    Language:Japanese   Presentation type:Oral presentation (general)  

  39. 進行非小細胞肺癌に対する薬物療法後の個別化生存予測における機械学習の活用

    神山潤二, 森瀬昌宏, 古川大記, 大山慎太郎, 松澤令子, 田中一大, 若原恵子, 横田秀夫, 木村智樹, 白鳥義宗, 近藤康博, 橋本直純

    第4回日本メディカルAI学会学術集会  2022.6.10 

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    Event date: 2022.6

    Language:Japanese   Presentation type:Oral presentation (general)  

  40. 統合診療データを用いたAIによるアウトカム予測と 診療サマリ生成に向けた検討

    古川 大記, 三沢 翔太郎, 大山 慎太郎, 佐藤 菊枝, 狩野 竜示, 鑓水 大和, 白鳥 義宗

    第41回医療情報学連合大会  2021.11.20 

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    Event date: 2021.11

    Presentation type:Oral presentation (general)  

  41. 東海国立大学機構が実現しようとしているSociety5.0

    白鳥 義宗, 大山 慎太郎, 山下 暁士, 佐藤 菊枝, 小林 大介, 舩田 千秋, 古川 大記, 菅野 亜紀, 森 龍太郎, 矢部 大介

    医療情報学連合大会論文集  2021.11  (一社)日本医療情報学会

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    Event date: 2021.11

    Language:Japanese   Presentation type:Symposium, workshop panel (nominated)  

  42. Prognosis in Non-IPF with Progressive Fibrotic Phenotype Results in Similar Prognosis in IPF International conference

    Ito, T; Takei, R; Sasano, H; Yamano, Y; Yokoyama, T; Matsuda, T; Kimura, T; Furukawa, T; Johkoh, T; Fukuoka, J; Kondoh, Y

    AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE  2021.5.1 

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    Event date: 2021.5

    Language:English   Presentation type:Poster presentation  

  43. Prognosis in Non-IPF with Progressive Fibrotic Phenotype Results in Similar Prognosis in IPF International conference

    Ito, T; Takei, R; Sasano, H; Yamano, Y; Yokoyama, T; Matsuda, T; Kimura, T; Furukawa, T; Johkoh, T; Fukuoka, J; Kondoh, Y

    AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE  2021.5.1 

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    Event date: 2021.5

    Language:English   Presentation type:Poster presentation  

  44. Prognosis in Non-IPF with Progressive Fibrotic Phenotype Results in Similar Prognosis in IPF International conference

    Ito, T; Takei, R; Sasano, H; Yamano, Y; Yokoyama, T; Matsuda, T; Kimura, T; Furukawa, T; Johkoh, T; Fukuoka, J; Kondoh, Y

    AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE  2021.5.1 

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    Event date: 2021.5

    Language:English   Presentation type:Poster presentation  

  45. Prognosis in Non-IPF with Progressive Fibrotic Phenotype Results in Similar Prognosis in IPF International conference

    Ito, T; Takei, R; Sasano, H; Yamano, Y; Yokoyama, T; Matsuda, T; Kimura, T; Furukawa, T; Johkoh, T; Fukuoka, J; Kondoh, Y

    AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE  2021.5.1 

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    Event date: 2021.5

    Language:English   Presentation type:Poster presentation  

  46. Prognosis in Non-IPF with Progressive Fibrotic Phenotype Results in Similar Prognosis in IPF International conference

    Ito, T; Takei, R; Sasano, H; Yamano, Y; Yokoyama, T; Matsuda, T; Kimura, T; Furukawa, T; Johkoh, T; Fukuoka, J; Kondoh, Y

    AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE  2021.5.1 

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    Event date: 2021.5

    Language:English   Presentation type:Poster presentation  

  47. Prognosis in Non-IPF with Progressive Fibrotic Phenotype Results in Similar Prognosis in IPF International conference

    Ito, T; Takei, R; Sasano, H; Yamano, Y; Yokoyama, T; Matsuda, T; Kimura, T; Furukawa, T; Johkoh, T; Fukuoka, J; Kondoh, Y

    AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE  2021.5.1 

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    Event date: 2021.5

    Language:English   Presentation type:Poster presentation  

  48. 間質性肺疾患における肺高血圧予測モデルの構築

    佐藤 智則, 古川 大記, 寺町 涼, 山野 泰彦, 横山 俊樹, 松田 俊明, 片岡 健介, 木村 智樹, 近藤 康博

    日本呼吸器学会  2021.4  (一社)日本呼吸器学会

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    Event date: 2021.4

    Language:Japanese   Presentation type:Oral presentation (general)  

  49. スマートホスピタル構想~医療Society5.0におけるDx研究~

    大山慎太郎, 大山慎太郎, 古川大記, 古川大記, 山下暁士, 山下暁士, 原武史, 原武史

    医療情報学連合大会  2021 

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    Event date: 2021

    Language:Japanese   Presentation type:Symposium, workshop panel (nominated)  

  50. Outcome prediction using Integrated Clinical Database toward Automatic Generation of Clinical Summaries

    古川大記, 三沢翔太郎, 大山慎太郎, 佐藤菊枝, 狩野竜示, 鑓水大和, 白鳥義宗

    2021 

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    Event date: 2021

    Language:Japanese   Presentation type:Oral presentation (general)  

  51. Patient access analysis in COVID-19 Pandemic using DPC data

    佐藤菊枝, 小林大介, 小林大介, 山下暁士, 大山慎太郎, 古川大記, 白鳥義宗

    医療情報学連合大会  2021 

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    Event date: 2021

    Language:Japanese   Presentation type:Poster presentation  

  52. Construction of a data collection platform based on the regional medical care data infrastructure

    佐藤菊枝, 小林大介, 小林大介, 山下暁士, 大山慎太郎, 古川大記, 白鳥義宗

    2021 

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    Event date: 2021

    Language:Japanese   Presentation type:Oral presentation (general)  

  53. Expanding Features of Outcome Prediction using Estimated Hospitalization Progress.

    MISAWA Shotaro, FURUKAWA Taiki, OYAMA Shintaro, SATO Kikue, KANO Ryuji, YARIMIZU Hirokazu, TANIGUCHI Tomoki, ONODA Kohei, OHKUMA Tomoko, SHIRATORI Yoshimune

    Proceedings of the Annual Conference of JSAI  2022  The Japanese Society for Artificial Intelligence

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    Language:Japanese   Presentation type:Poster presentation  

    <p>Outcome prediction using clinical data such as mortality prediction, length-of-stay prediction is applicable to acute change prediction, early treatment, and prediction of treatment effects. However, it is difficult to predict the long-term future status of patients. To improve the performance of the prediction model, we first estimate the short-term future and leverage the estimated value to predict the long-term future status of patients. Such hospitalization progress in the short-term future can be estimated by constructing another estimation model. In this study, we propose the feature expansion using estimated hospitalization progress for the outcome prediction model. We conduct experiments on clinical data of pneumonia cases aggregated in "CITA Clinical Finder", the integrated medical support platform. The result shows our model can predict more accurately than the model without feature expansion.</p>

    DOI: 10.11517/pjsai.jsai2022.0_3yin228

    CiNii Research

  54. Artificial intelligence-based personalized survival prediction using clinical and radiomics features in patients with advanced non-small cell lung cancer International conference

    Koyama Junji, Morise Masahiro, Furukawa Taiki, Oyama Shintaro, Matsuzawa Reiko, Tanaka Ichidai, Wakahara Keiko, Yokota Hideo, Kimura Tomoki, Shiratori Yoshimune, Kondoh Yasuhiro, Hashimoto Naozumi

    RESPIROLOGY  2023.2 

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    Language:English   Presentation type:Oral presentation (general)  

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Research Project for Joint Research, Competitive Funding, etc. 8

  1. All Japan大規模レジストリデータを背景とした間質性肺炎の治療プログラム及びデバイスの開発

    2022.4 - 2026.3

    医療機器等研究成果展開事業 

    中澤 公貴, 五十嵐亮レオナルド, 大山慎太郎

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    Authorship:Principal investigator  Grant type:Competitive

  2. 間質性肺炎に対する多施設共同前向き観察研究

    2020.3 - 2025.12

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    Authorship:Coinvestigator(s)  Grant type:Collaborative (industry/university)

  3. 特発性肺線維症の運動能力向上と生命予後の改善を目指した、機械学習による最適な呼吸リハビリテーション予測モデルの開発

    2023.12 - 2025.3

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    Authorship:Principal investigator  Grant type:Competitive

  4. All Japan大規模レジストリデータを背景とした間質性肺炎の遠隔診断と、治療プログラム及びデバイスの事業化検証

    2022.8 - 2023.3

    大学発新産業創出プログラム(START)大学エコシステム推進型GAPファンドプログラム 

    古川大記, 大山慎太郎, 五十嵐亮レオナルド

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    Authorship:Principal investigator  Grant type:Competitive

  5. 医師の業務効率化を支援するアルゴリズムの機械学習

    2021.11 - 2023.10

    白鳥義宗, 佐藤菊枝, 大山慎太郎, 古川大記

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    Authorship:Coinvestigator(s)  Grant type:Collaborative (industry/university)

  6. 特発性間質性肺炎の前向きレジストリの構築とインタラクティブMDD診断システムを用いた診断標準化に基づく疫学データの創出―人工知能(AI)診断システムと新規バイオマーカーの開発―

    2020.4 - 2022.3

    難治性疾患等実用化研究事業 

    須田 隆文, 井上 義一, 横田 秀夫, 宮崎 泰成, 近藤 康博, 古川 大記, 坂東 政司, 小倉 高志, 上甲 剛, 長谷川 好規, 白鳥 義宗, 福岡 順也, 本間 栄

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    Authorship:Coinvestigator(s)  Grant type:Competitive

  7. びまん性肺疾患MDD診断のための双方向性Webプラットフォーム構築と人工知能診断の社会実装に関する前向き研究

    2019.4 - 2022.3

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    Authorship:Principal investigator  Grant type:Competitive

  8. びまん性肺疾患診断の臨床画像クラウド型統合データベースの基盤構築と機械学習による診断・予後予測アルゴリズム構築に関する研究

    2019.4 - 2022.3

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    Authorship:Principal investigator  Grant type:Competitive

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KAKENHI (Grants-in-Aid for Scientific Research) 7

  1. 人工衛星による大気汚染情報を活用した間質性肺炎プレシジョンメディシンの実現

    Grant number:23K27610  2024.2 - 2026.3

    科学研究費助成事業  基盤研究(B)

    古川 大記

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    Authorship:Principal investigator  Grant type:Competitive

    Grant amount:\5720000 ( Direct Cost: \4400000 、 Indirect Cost:\1320000 )

  2. 人工衛星による大気汚染情報を活用した間質性肺炎プレシジョンメディシンの実現

    Grant number:23H02919  2023.4 - 2026.3

    日本学術振興会  科学研究費助成事業 基盤研究(B)  基盤研究(B)

    古川 大記, 大山 慎太郎, 横田 秀夫, 横田 秀夫, 大山 慎太郎

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    Authorship:Principal investigator  Grant type:Competitive

    Grant amount:\19370000 ( Direct Cost: \14900000 、 Indirect Cost:\4470000 )

    間質性肺炎は、肺の間質が侵される肺疾患の総称で、多数の診断群と多様な経過をたどる。中でも最も予後不良である特発性肺線維症(IPF)は、正確に診断できる専門医が少ない。同様に予後不良である進行性線維性間質性肺炎(PF-ILD)は、病状が悪化した後にPF-ILD と判断されるため治療導入が遅れてしまうが、診断は困難である。
    このため、本研究では前向き・後ろ向きリアルワールド疾患データと大気汚染情報を統合し、データ駆動型研究により疾患の本質を導き出す。

  3. Development of a quantitative evaluation method for medical resource optimization and construction of a regional medical data platform

    Grant number:23K09547  2023.4 - 2026.3

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research  Grant-in-Aid for Scientific Research (C)

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    Authorship:Coinvestigator(s)  Grant type:Competitive

    Grant amount:\100000 ( Direct Cost: \100000 )

  4. Immune-pathological diagnostic artificial intelligence development research for pulmonary fibrosis using fibrotic foci-specific enhanced micro-CT

    Grant number:20K21599  2020.7 - 2023.3

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research Grant-in-Aid for Challenging Research (Exploratory)  Grant-in-Aid for Challenging Research (Exploratory)

    Hashimoto Naozumi

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    Authorship:Coinvestigator(s)  Grant type:Competitive

    Although evaluation by conventional contrast-enhanced CT with X-ray contrast agents provides useful diagnostic information in clinical setting, the aim of this study is to identify lung disease-specific molecules using high-resolution and high-resolution radiographic images derived from contrast-enhanced micro-CT scans with gold nanoparticles as X-ray contrast agents. In this study, we were able to set micro-CT imaging to detect gold nanoparticle-bound lung-specific antibodies in the lung tissue by producing gold nanoparticle-bound lung-specific antibodies and intravenously administering them to living mice. In the future, administration of adjusted dose of gold nanoparticle-bound lung-specific antibody and adequate detection setting of gold nanoparticles bound to lung tissue by micro-CT might give micro-CT immunopathological diagnostic ability.

  5. 特発性間質性肺炎の前向きレジストリの構築とインタラクティブMDD診断システムを用いた診断標準化に基づく疫学データの創出―人工知能(AI)診断システムと新規バイオマーカーの開発―

    2020.4 - 2022.3

    日本医療研究開発機構(AMED)  難治性疾患等実用化研究事業 

    須田 隆文, 井上 義一, 横田 秀夫, 宮崎 泰成, 近藤 康博, 古川 大記, 坂東 政司, 小倉 高志, 上甲 剛, 長谷川 好規, 白鳥 義宗, 福岡 順也, 本間 栄

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    Authorship:Coinvestigator(s) 

  6. Orality and Narrative Technique in Pain Clinic

    Grant number:19KT0027  2019.7 - 2022.3

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research Grant-in-Aid for Scientific Research (C)  Grant-in-Aid for Scientific Research (C)

    OYAMA Shintaro

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    Authorship:Coinvestigator(s)  Grant type:Competitive

    The goal of this study was to develop a medical care learning tool to systematize orality techniques for physicians in the treatment of chronic pain patients. The latter was possible to implement using existing libraries, but the accuracy of facial expressions could not be improved due to the principle of wearing a mask in the examination room. Based on the existing machine learning model, we conducted a dialogue (clinical) test, conducted reinforcement learning with the data, and constructed a model that adapted the Softmax function to the five classes of emotional parameter outputs. The results were reported at a conference.

  7. A Study Development of Machine Learning Algorithm for Diagnosis and Prognosis Prediction of Diffuse Lung Disease

    Grant number:19K17633  2019.4 - 2022.3

    Japan Society for the Promotion of Science  Grants-in-Aid for Scientific Research Grant-in-Aid for Early-Career Scientists  Grant-in-Aid for Early-Career Scientists

    Furukawa Taiki

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    Authorship:Principal investigator  Grant type:Competitive

    Grant amount:\4290000 ( Direct Cost: \3300000 、 Indirect Cost:\990000 )

    In this study, we constructed a large database and developed each artificial intelligence (AI) model for diagnosis and prognosis prediction of interstitial lung diseases.
    First, systematic disease data was collected from nationwide specialized hospitals and transformed into a form suitable for AI diagnosis, resulting in a highly accurate diagnostic and prognostic AI. Moreover, we analyzed to make the models generally usable, and reconstructed the AI in order to make the data accessible to non-specialized facilities. A Platform as a Service (PaaS) compatible prototype was built to enable the constructed model to run publicly available applications. With these results, we achieved our research goal of " constructing a database of interstitial lung diseases and developing highly accurate AI diagnostic and prognostic predictions.

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Industrial property rights 11

  1. 情報処理装置、情報処理方法、および、コンピュータプログラム

    寺町涼, 古川大記, 烏山昌幸, 横田秀夫

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    Applicant:国立大学法人東海国立大学機構

    Application no:特願2022-175512  Date applied:2022.11

  2. Identifying device, learning device, method, and storage medium

    Taiki FURUKAWA, Hideo Yokota, Shintaro OYAMA, Yoshinori Hasegawa, Yoshimune SHIRATORI

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    Application no:特願US20200372650  Date applied:2020.5

    Date announced:2020.11

    Patent/Registration no:特許US11,361,443 B2  Date registered:2022.6 

  3. 判別装置、学習装置、方法、プログラム、学習済みモデルおよび記憶媒体

    古川 大記, 横田 秀夫, 大山 慎太郎, 長谷川 好規, 白鳥 義宗

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    Applicant:国立研究開発法人理化学研究所

    Application no:特願2019-094757  Date applied:2019.5

    Announcement no:特開2020-188872  Date announced:2020.11

    Patent/Registration no:特許第7334900号  Date registered:2023.8 

    J-GLOBAL

  4. 文書作成支援装置、文書作成支援方法、及び文書作成支援プログラム

    古川大記, 大山慎太郎, 他

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    Applicant:国立大学法人東海国立大学機構

    Application no:特願2023-166471  Date applied:2023.7

  5. Identifying device, learning device, method, and storage medium

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    Applicant:国立研究開発法人理化学研究所

    Application no:特願2023-109008  Date applied:2023.7

  6. Identifying device, learning device, method, and storage medium

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    Applicant:国立研究開発法人理化学研究所

    Application no:特願2023-109002  Date applied:2023.7

  7. 情報処理装置、学習装置、情報処理システム、情報処理方法、学習方法、情報処理プログラム、及び学習プログラム

    古川 大記, 三沢 翔太郎, 鑓水 大和, 狩野 竜示, 谷口 友紀, 大熊 智子, 小野田 浩平

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    Applicant:富士フイルム株式会社

    Application no:特願2023-030561  Date applied:2023.2

    Announcement no:特開2024-035034  Date announced:2024.3

    J-GLOBAL

  8. 情報処理装置、情報処理方法、及び情報処理プログラム

    古川 大記, 三沢 翔太郎, 狩野 竜示, 鑓水 大和, 谷口 友紀, 大熊 智子, 小野田 浩平

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    Applicant:富士フイルム株式会社

    Application no:特願2022-138808  Date applied:2022.8

    Announcement no:特開2024-034529  Date announced:2024.3

    J-GLOBAL

  9. 情報処理装置

    古川大記

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    Applicant:国立大学法人東海国立大学機構名古屋大学

    Application no:特願2022-1388807  Date applied:2022.8

  10. 情報処理装置

    古川大記

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    Applicant:国立大学法人東海国立大学機構名古屋大学

    Application no:特願2022-1388808  Date applied:2022.8

  11. 情報処理装置、情報処理方法、および、コンピュータプログラム

    神山 潤二, 古川 大記, 森瀬 昌宏, 横田 秀夫

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    Application no:特願2022-043291  Date applied:2022.3

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Teaching Experience (On-campus) 4

  1. 生命医療データ学

    2023

  2. 医療情報学

    2023

  3. 生命医療データ学

    2022

  4. 医療情報学

    2022

Teaching Experience (Off-campus) 1

  1. 医療情報学

    2021.4

 

Media Coverage 3

  1. 間質性肺炎 高齢化で増 息切れ、せき 見逃さずに 予後悪く 原因別で多様な種類 Newspaper, magazine

    中日新聞、東京新聞、北陸中日新聞  2024.4

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    Author:Other 

  2. 難病・特発性肺線維症 名大と理研が高精度診断AI開発 Newspaper, magazine

    中日新聞、東京新聞、北陸中日新聞  2023.10

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    Author:Other 

  3. 難病・特発性肺線維症  名大と理研が高精度診断AI開発 Newspaper, magazine

    中日新聞  https://www.chunichi.co.jp/article/794814  2023.10

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    Author:Other