| アイテムタイプ |
文献 / Documents(1) |
| 公開日 |
2026-04-10 |
| アクセス権 |
|
|
アクセス権 |
open access |
|
アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| 出版社版DOI |
|
|
|
関連識別子 |
https://doi.org/10.1016/j.cmpb.2021.106583 |
|
|
関連名称 |
10.1016/j.cmpb.2021.106583 |
| 出版タイプ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| タイトル |
|
|
タイトル |
Machine learning for classification of postoperative patient status using standardized medical data |
| 著者 |
Yamashita, Takanori
若田, 好史
Nakaguma, Hideki
Nohara, Yasunobu
Hato, Shinji
Kawamura, Susumu
Muraoka, Shuko
Sugita, Masatoshi
Okada, Mihoko
Nakashima, Naoki
Soejima, Hidehisa
|
| 抄録 |
|
|
内容記述 |
Background and objective: Real-world evidence is defined as clinical evidence regarding the use and potential benefits or risks of a medical product derived from real-world data analyses. Standardization and structuring of data are necessary to analyze medical real-world data collected from different medical institutions. An electronic message and repository have been developed to link electronic medical records in this research project, which has simplified the data integration. Therefore, this paper proposes an analysis method and learning health systems to determine the priority of clinical intervention by clustering and visualizing time-series and prioritizing patient outcomes and status during hospitalization.
Methods: Common data items for reimbursement (Diagnosis Procedure Combination [DPC]) and clinical pathway data were examined in this project at each participating institution that runs the verification test. Long-term hospitalization data were analyzed using the data stored in the cloud platform of the institutions’ repositories using multiple machine learning methods for classification, visualization, and interpretation.
Results: The ePath platform contributed to integrate the standardized data from multiple institutions. The distribution of DPC items or variances could be confirmed by clustering, temporal tendency through the directed graph, and extracting variables that contributed to the prediction and evaluation of SHapley Additive Explanation effects. Constipation was determined to be the risk factor most strongly related to long-term hospitalization. Drainage management was identified as a factor that can improve long-term hospitalization. These analyses effectively extracted patient status to provide feedback to the learning health system.
Conclusions: We successfully generated evidence of medical processes by gathering patient status, medical purposes, and patient outcomes with high data quality from multiple institutions, which were difficult with conventional electronic medical records. Regarding the significant analysis results, the learning health system will be used on this project to provide feedback to each institution, operate it for a certain period, and analyze and re-evaluate it. |
| キーワード |
|
|
主題 |
Real-world data (RWD) |
| キーワード |
|
|
主題 |
Clinical pathway |
| キーワード |
|
|
主題 |
Learning health system |
| キーワード |
|
|
主題 |
Diagnosis procedure combination (DPC) |
| キーワード |
|
|
主題 |
Machine learning |
| 書誌情報 |
en : Computer Methods and Programs in Biomedicine
巻 214,
p. 106583,
発行日 2021-12-12
|
| 収録物ID |
|
|
収録物識別子タイプ |
EISSN |
|
収録物識別子 |
18727565 |
| 収録物ID |
|
|
収録物識別子タイプ |
PISSN |
|
収録物識別子 |
01692607 |
| 収録物ID |
|
|
収録物識別子タイプ |
NCID |
|
収録物識別子 |
AA11526332 |
| 出版者 |
|
|
出版者 |
Elsevier |
| 権利情報 |
|
|
権利情報Resource |
http://creativecommons.org/licenses/by/4.0/ |
|
権利情報 |
Creative Commons Attribution 4.0 International |
| EID |
|
|
識別子 |
421785 |
| 言語 |
|
|
言語 |
eng |
cmpb_214_106583.pdf (2.2 MB)
.png)
