| Project/Area Number |
16K09171
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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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| Research Field |
Medical and hospital managemen
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| Research Institution | Osaka University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
三原 直樹 国立研究開発法人国立がん研究センター, 中央病院, 部長 (20379192)
松村 泰志 大阪大学, 医学系研究科, 教授 (90252642)
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| Research Collaborator |
Shimai Yoshie
Sugimoto Kento
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| Project Period (FY) |
2016-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥3,172,000 (Direct Cost: ¥2,440,000、Indirect Cost: ¥732,000)
Fiscal Year 2018: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2017: ¥351,000 (Direct Cost: ¥270,000、Indirect Cost: ¥81,000)
Fiscal Year 2016: ¥1,521,000 (Direct Cost: ¥1,170,000、Indirect Cost: ¥351,000)
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| Keywords | 薬剤性有害事象 / 間質性肺炎 / テキストマイニング / 機械学習 / 薬剤性間質性肺炎 / 電子カルテ / 網羅的検出 / 薬剤有害事象 / 放射線レポート / 自然言語解析 / 非構造化データ |
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| Outline of Final Research Achievements |
This study aims to detect adverse drug events using electronic medical record data.
Keywords were extracted by text mining from the diagnostic imaging report described in free text, and interstitial pneumonia could be detected with high accuracy from the frequency of keyword appearance. From The incidence rate of drug-induced interstitial pneumonia could be calculated from the chronological relationship between drug administration period and the onset of interstitial pneumonia.
Detection of lung cancer, breast cancer and esophagus cancer reports from imaging diagnostic reports was performed by the same keyword method. However, the accuracy was not high. This is thought to be due to the fact that interstitial pneumonia has characteristic keywords, whereas cancer has less characteristic keywords. To solve this problem, extraction of the findings by machine learning was needed.
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| Academic Significance and Societal Importance of the Research Achievements |
薬剤性有害事象は治験、市販後調査において多額の費用を用いて実施されている。
電子カルテデータを用いて薬剤性有害事象を検出できれば、安価に有害事象の発生率が算出できる。また、リアルワールドデータを解析することで、治験や市販後調査で把握できなかった、新たな副作用を発見できる可能性がある。さらに、継続的に有害事象を検出する仕組みを開発すれば、新薬の有害事象をより早期に発見できる可能性がある。
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