从在线医疗论坛中挖掘药物副作用

Hariprasad Sampathkumar, Bo Luo, Xue-wen Chen
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引用次数: 15

摘要

用于预防、治疗或治愈疾病的药物可能会产生不良反应或副作用,导致进一步的健康并发症,有时甚至导致死亡。制造商报告的大多数常见药物副作用都是基于临床试验。然而,并不是所有可能的副作用都被识别出来,因为它们的检测受到试验参与者数量和多样性的限制。在网上医疗帮助论坛上,患者自愿提供他们所服用药物的反馈,这为识别未报告的药物副作用提供了一个极好的来源。挖掘这些副作用将有助于患者就药物是否适合其治疗作出知情决定,也有助于卫生当局对药品生产商采取适当行动。本文提出了一种基于隐马尔可夫模型的文本挖掘系统,该系统可用于从在线医疗论坛中提取药物的不良副作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mining Adverse Drug Side-Effects from Online Medical Forums
Pharmaceutical drugs prescribed for the prevention, treatment or cure of diseases can have adverse reactions or side-effects that lead to further health complications or sometimes even death. Most of the common side-effects of drugs, reported by their manufacturer, are based on clinical trials. However, not all possible side-effects are identified, as their detection is limited by the extent of the number and diversity of the participants in the trials. Online medical help forums where patients voluntarily provide feedback on the drugs they take, provide an excellent source for identifying the unreported side-effects of drugs. Mining for these side-effects would help patients make informed decisions about the suitability of a drug for their treatment and also for health authorities to take appropriate action against drug manufacturers. In this paper we present a Hidden Markov Model based text mining system that can be used to extract adverse side-effects of drugs from online medical forums.
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