2023 Fiscal Year Final Research Report
Development of interactive electronic medical record's diagnosis accuracy verification system using AI
| Project/Area Number |
19K12867
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 90140:Medical technology assessment-related
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| Research Institution | Osaka Dental University (2022-2023)
Wakayama Medical University (2019-2021) |
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Principal Investigator |
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| Project Period (FY) |
2019-04-01 – 2024-03-31
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| Keywords | リアルワールドエビデンス / リウマチ / 教師なし学習 / 人工知能 / 医療ビッグデータ |
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| Outline of Final Research Achievements |
We profiled each patient’s state transitions during treatment using energy landscape analysis and time-series clustering. Energy landscape analysis divided state transitions into two patterns: “good stability leading to remission” and “poor stability leading to treatment dead-end.” The number of patients whose disease status improved increased markedly until approximately 6 months after treatment initiation and then plateaued after 1 year. Time-series clustering grouped patients into three clusters: “toward good stability,” “toward poor stability,” and “unstable.” Patients in the “unstable” cluster are considered to have clinical courses that are difficult to predict; therefore, these patients should be treated with more care. The evaluation of state multistability enables us to understand a patient’s current state in the context of overall state transitions related to rheumatoid arthritis drug treatment and to predict future state transitions.
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| Free Research Field |
臨床研究情報学
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| Academic Significance and Societal Importance of the Research Achievements |
我々の提案する解析手法は、医療における多次元時系列データの可視化手法として有用であり、RA以外の疾患にも応用可能である。本研究で、日々の診療の中で、患者の状態に加え、状態変動性を評価することで、治療経過全体にわたる治療計画の最適化が可能となりうることが示唆された。本研究がリアルワールドデータ活用による個別化医療の発展に寄与することが期待される。
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