从在线健康论坛检测药物相互作用(DDI)的双次模块优化(BSMO)。

IF 5.4 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Journal of healthcare informatics research Pub Date : 2018-08-30 eCollection Date: 2019-03-01 DOI:10.1007/s41666-018-0032-y
Yan Hu, Rui Wang, Feng Chen
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引用次数: 0

摘要

在线健康论坛作为信息交流库,被不同的患者群体用来分享经验和寻求建议。近十年来,随着移动互联网的迅猛发展,这些论坛的可访问性大大增加。在众多热门话题中,"药物相互作用"(DDIs)论坛包含了大量患者经历过但未公布的 DDIs 危害。在本文中,我们打算从在线论坛中发现潜在的 DDIs,并将这一任务表述为一个子图检测问题,即共同提及的药物和症状被建模为顶点,同时出现的情况被建模为加权边。因此,由症状和药物顶点组成的连通子图揭示了 DDIs 的发生。然后,我们提出了一种新的双子模块化函数来描述连通子图中出现 DDI 的可能性,并应用近似算法来解决双子模块化优化问题(BSMO)。该算法的复杂度接近线性。我们的大量实验证明了所提方法的有效性和效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bi-submodular Optimization (BSMO) for Detecting Drug-Drug Interactions (DDIs) from On-line Health Forums.

Online health discussion forums as information exchange repository are used by different patient groups for sharing experience and seeking advice. Their accessibility is tremendously expanded in the last decade with the rapid growth of mobile internet. Among many popular topics, "drug-drug interactions" (DDIs) forum embeds a large number of DDIs hazards patient experienced however not published. In this paper, we intend to uncover the potential DDIs from the online forums and formulate the task as a sub-graph detection problem, such that co-mentioned drugs and symptoms are modeled as vertices, along with the occurrences are modeled as weighted edges. Therefore, a connected sub-graph consisting of both symptoms and drug vertices reveals DDIs occurrence. We then propose a novel bi-submodular function to characterize the likelihood of DDI occurrence within a connected sub-graph and apply an approximated algorithm to resolve the bi-submodular optimization (BSMO). The complexity of the algorithm is nearly linear. Our extensive experiments demonstrate the effectiveness and efficiency of the proposed approach.

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