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

Web of Science

Scopus

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

Web of Science

Scopus

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

Web of Science

Scopus

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

Web of Science

Scopus

PubMed

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

Web of Science

Scopus

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

Web of Science

Scopus

PubMed

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

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

Authorship：Lead author,　Last author,　Corresponding author   Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (scientific journal)  

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。

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

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

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：Article, review, commentary, editorial, etc. (trade magazine, newspaper, online media)  

CiNii Research

Event date： 2022.12

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2022.11

Language：English   Presentation type：Oral presentation (general)  

Event date： 2022.10

Language：Japanese   Presentation type：Poster presentation  

Event date： 2022.10

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

Event date： 2022.9

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

Event date： 2022.9

Language：English   Presentation type：Oral presentation (general)  

Event date： 2022.7

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

Event date： 2022.6

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2022.6

Language：Japanese   Presentation type：Oral presentation (general)  

Event date： 2022.4

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

Event date： 2022.4

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

Event date： 2021.11

Presentation type：Oral presentation (general)  

Event date： 2021.4

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

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)  

DOI： 10.1183/13993003.congress-2022.342

Language：English   Presentation type：Oral presentation (general)  

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