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このアイテムの引用には次の識別子を使用してください:
http://hdl.handle.net/10119/15476
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| タイトル: | Sequence-based Measure for Assessing Drug-Side Effect Causal Relation from Electronic Medical Records |
| 著者: | Dang, Tran Thai
Ho, Bao Tu |
| キーワード: | Drug-Side Effect
Causal Relation
Electronic Medical Rec |
| 発行日: | 2017-10-17 |
| 出版者: | Springer |
| 誌名: | Communications in Computer and Information Science |
| 巻: | 780 |
| 開始ページ: | 53 |
| 終了ページ: | 65 |
| DOI: | 10.1007/978-981-10-6989-5_5 |
| 抄録: | The recent prevalence of electronic medical records offers a new way to detect the drug-side effect causality. However, this approach faces with the problem of identifying the likely causal relation between drugs and side effects in a huge space of possible relations in which many relations are not causal ones, but frequently observed. In existing work, the likely causal relation is almost detected by using frequency-based measures of drug-side effect pair co-occurrence, but the accuracy is rather low due to the frequent co-occurrence of non-causal pairs. Our key as- sumption on the causality of the drugs and side effects is that the causal- ity occurring at a medication event has an association with the therapy history of this event. The assumption is employed as a constraint in the proposed sequential-based model named Medication Therapy Progress- based Model (MTPM). Experiments show a significant improvement of accuracy from 4% to 9% when comparing MTMP and existing methods, as well as reflect the likelihood of the assumption. |
| Rights: | This is the author-created version of Springer, Tran-Thai Dang, Tu-Bao Ho, Communications in Computer and Information Science, 780, 2017, 53-65. The original publication is available at www.springerlink.com, http://dx.doi.org/10.1007/978-981-10-6989-5_5 |
| URI: | http://hdl.handle.net/10119/15476 |
| 資料タイプ: | author |
| 出現コレクション: | a10-1. 雑誌掲載論文 (Journal Articles)
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このアイテムのファイル:
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記述 |
サイズ | 形式 |
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| 23963.pdf | | 390Kb | Adobe PDF | 見る/開く |
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