| Project/Area Number |
18K14984
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 47060:Clinical pharmacy-related
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| Research Institution | Kyushu University |
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Principal Investigator |
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| Project Period (FY) |
2018-04-01 – 2022-03-31
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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2019: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2018: ¥3,250,000 (Direct Cost: ¥2,500,000、Indirect Cost: ¥750,000)
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| Keywords | 処方チェックシステム / 機械学習 |
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| Outline of Final Research Achievements |
Overdose prescription errors sometimes cause serious life-threatening adverse drug events, while underdose errors lead to diminished therapeutic effects. Therefore, it is important to detect and prevent these errors. In the present study, we used the one-class support vector machine, one of the most common unsupervised machine learning algorithms for anomaly detection, to identify overdose and underdose prescriptions.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究では，患者の年齢，体重，および薬剤の投与量を基に構築したone-class support vector machine（OCSVM）モデルが，薬剤の過量処方・過少処方の検出において有用であることが示された。また，OCSVMは他の機械学習アルゴリズム（local outlier factor，isolation forest，およびrobust covariance）の中で最も高い検出性能を有していたことからも，OCSVMの活用が，より高精度な薬剤投与量チェックシステムの開発のために有用であることが考えられた。
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