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

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

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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PubMed

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

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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PubMed

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Journal of Pain and Symptom Management  

The authors regret that the affiliation of M.H., S.Y., M.H., and A.K. was listed as the Department of Clinical Oncology and Chemotherapy at Nagoya University Hospital in Nagoya, Japan. This should have been listed as the Department of Nursing at Nagoya University Hospital in Nagoya, Japan. The authors would like to apologize for any inconvenience caused.

DOI： 10.1016/j.jpainsymman.2024.05.019

Scopus

PubMed

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 < 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 < 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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PubMed

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

DOI： 10.1016/j.gie.2023.04.1087

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

Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Studies in Health Technology and Informatics  

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

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

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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PubMed

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

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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PubMed

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

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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PubMed

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

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

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

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

Web of Science

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

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PubMed

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

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PubMed

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

DOI： 10.1183/13993003.01346-2020

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PubMed

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

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

Language：Japanese Book type：Scholarly book

Language：Japanese Book type：Scholarly book

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。

Language：Japanese Book type：Scholarly book

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

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  

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  

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  

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

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

CiNii Research

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。

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

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

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

Web of Science

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

J-GLOBAL

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

J-GLOBAL

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

J-GLOBAL

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

J-GLOBAL

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

CiNii Research

Event date： 2022.7

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

Event date： 2022.4

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

Event date： 2022.4

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

Event date： 2021.4

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

Event date： 2024.9

Language：English   Presentation type：Oral presentation (general)  

Event date： 2024.8

Language：English   Presentation type：Poster presentation  

By linking medical real-world data with geographic information, it is possible to evaluate the impact on hospitalization based on these characteristics, such as patient residence information and disease and medical information. In this study, environmental exposure to air pollutants was reported as a risk factor, and predictive models were used to examine factors affecting health. The importance of the characteristics appeared according to the disease, and overall, the patient profile at the time of admission, such as ADL, was shown to be high, but for respiratory diseases, the cumulative concentration of air pollutants NO2, SPM, and NOx for one year before the onset of admission was the top risk factor for long-term hospitalization, suggesting the influence of exposure due to environmental factors.

Event date： 2024.6

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

Event date： 2024.6

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

Event date： 2024.6

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2024.4

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

Event date： 2024.3

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2024

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2023.11

Language：Japanese   Presentation type：Poster presentation  

Event date： 2023.10

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

Event date： 2023.9 - 2023.10

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

Event date： 2023.9 - 2023.10

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2023.9

Language：English   Presentation type：Poster presentation  

Event date： 2023.9

Language：English   Presentation type：Poster presentation  

DOI： 10.1183/13993003.congress2023.PA434

Event date： 2023.9

Language：English   Presentation type：Poster presentation  

DOI： 10.1183/13993003.congress-2023.PA1236

Event date： 2023.9

Language：English   Presentation type：Oral presentation (general)  

DOI： 10.1183/13993003.congress-2023.OA1424

Event date： 2023.6 - 2023.7

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2023.6

Language：Japanese   Presentation type：Poster presentation  

Event date： 2023.6

Language：Japanese  

Event date： 2023.6

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2023.5

Language：English   Presentation type：Poster presentation  

Event date： 2023.5

Language：English   Presentation type：Poster presentation  

Event date： 2023.4

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2023.2

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

Event date： 2023.2

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

Event date： 2023.2

Language：English   Presentation type：Oral presentation (general)  

Event date： 2023.2

Language：English   Presentation type：Oral presentation (general)  

Event date： 2023.2

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

Event date： 2023.2

Language：English   Presentation type：Oral presentation (general)  

Event date： 2023.2

Language：English   Presentation type：Oral presentation (general)  

Event date： 2023.2

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

Event date： 2023.2

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

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

Event date： 2022.12

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2022.11

Language：English   Presentation type：Oral presentation (general)  

Event date： 2022.11

Language：Japanese   Presentation type：Poster presentation  

Event date： 2022.10

Language：Japanese   Presentation type：Poster presentation  

Event date： 2022.9

Language：English   Presentation type：Oral presentation (general)  

DOI： 10.1183/13993003.congress-2022.342

Event date： 2022.6

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2022.6

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2021.11

Presentation type：Oral presentation (general)  

Event date： 2021.11

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

Event date： 2021.5

Language：English   Presentation type：Poster presentation  

Event date： 2021.5

Language：English   Presentation type：Poster presentation  

Event date： 2021.5

Language：English   Presentation type：Poster presentation  

Event date： 2021.5

Language：English   Presentation type：Poster presentation  

Event date： 2021.5

Language：English   Presentation type：Poster presentation  

Event date： 2021.5

Language：English   Presentation type：Poster presentation  

Event date： 2021.5

Language：English   Presentation type：Poster presentation  

Event date： 2021.5

Language：English   Presentation type：Poster presentation  

Event date： 2021.5

Language：English   Presentation type：Poster presentation  

Event date： 2021.4

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2021

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

Event date： 2021

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2021

Language：Japanese   Presentation type：Poster presentation  

Event date： 2021

Language：Japanese   Presentation type：Oral presentation (general)  

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

Language：English   Presentation type：Oral presentation (general)  

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

DOI： 10.15032/jsrcr.34.supplement_81s_1

CiNii Research

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

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

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

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

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

Application no：特願2022-043291  Date applied：2022.3