Title: 利用藥物化學特徵和統計學習建構藥物負向事件預測模型
Prediction for Adverse Drug Events by Chemical Descriptors and Statistical Learning
Authors: 吳宜靜
盧鴻興
Lu, Horng-Shing Henry
統計學研究所
Keywords: 藥物負向事件;adverse drug event
Issue Date: 2011
Abstract: 當藥物成分進入人體後,產生複雜的擾動效應稱為藥效,而藥效可分為主要治療藥效和額外的效果,而藥物不良反應事件是額外效果的一部分。每個藥物成分都是一種化合物,由其化學式可以得到化學資訊特徵。基於藥物在身體系統中產生的生物擾動與其化學結構有關的假設,我們檢視上市藥物的藥物不良反應事件與其化學資訊特徵之間的關聯。在本研究中,我們使用決策樹方法指認出與1384個藥物不良反應事件的相關化學資訊特徵。並設計一套自動分析流程,針對我們選定的35個有興趣的藥物不良反應事件得到模型十折交叉驗證正確率高於80%。例如: 糖尿病(87.1%),急性腎功能衰竭(91.0%)和腎功能不全(94.6%)。
Additional to the medicine treatment effect, side effects are complex undesired phenomena due to the bio-activity of pharmaceutical compound. For each compound, the chemistry informatics can delineate its intrinsic chemical formula into chemistry informatics features. Based on the assumption that the chemical structure is critical to the biological perturbation in the human system, we investigate different adverse drug events with associated chemistry informatics features of marketed drugs. In this paper, we identify 1384 ADEs with corresponding associated chemistry informatics features by decision tree. With an automatic analysis workflow, we can obtain a concordant drug subset with satisfying 10-fold cross-validation accuracy. The test experiment about selected 35 ADEs of interest results in accuracy higher than 80%. For example, there are three ADEs of interest and their accuracy: Diabetes Mellitus (0.871), Renal Failure Acute (0. 910) and Renal Impairment (0. 946).
URI: http://140.113.39.130/cdrfb3/record/nctu/#GT079926503
http://hdl.handle.net/11536/49915
Appears in Collections:Thesis


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