Abstract

Objective

Outcomes mentioned on online health communities (OHCs) by patients can serve as a source of evidence for off-label drug usage evaluation, but identifying these outcomes manually is tedious work. We have built a natural language processing model to identify off-label usage of drugs mentioned in these patient posts.

Materials and Methods

Single patient posts from 4 major OHCs were considered for this study. A text classification model was built to classify the posts as either relevant or not relevant based on patient experience. The relevant posts were passed through a spelling correction tool, CSpell, and then medications and indications from these posts were identified using cTAKES (clinical Text Analysis and Knowledge Extraction System), a named entity recognition tool. Drug and indication pairs were identified using a dependency parser. Finally, if the paired indication was not mentioned on the label of the drug approved by U.S. Food and Drug Administration, it was tagged as off-label use of that drug.

Results

Using this algorithm, we identified 289 off-label indications, achieving a recall of 76%.

Conclusions

The method designed in this study identifies and extracts the semantic relationship between drugs and indications from demotic posts in OHCs. The results demonstrate the feasibility of using natural language processing techniques in identifying off-label drug usage across online health forums for a variety of drugs. Understanding patients’ off-label use of drugs may be able to help manufacturers innovate to better address patients’ needs and assist doctors’ prescribing decisions.

中文翻译：

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应用自然语言处理技术来识别来自各种在线健康社区的标签外药物使用

摘要

客观的

患者在在线健康社区 (OHC) 上提到的结果可以作为标签外药物使用评估的证据来源，但手动识别这些结果是一项乏味的工作。我们已经建立了一个自然语言处理模型来识别这些患者帖子中提到的药物的标签外使用。

材料和方法

本研究考虑了来自 4 个主要 OHC 的单个患者帖子。建立了一个文本分类模型，根据患者的经验将帖子分类为相关或不相关。相关帖子通过拼写校正工具 CSpell 传递，然后使用命名实体识别工具 cTAKES（临床文本分析和知识提取系统）识别这些帖子中的药物和适应症。使用依赖解析器识别药物和适应症对。最后，如果美国食品和药物管理局批准的药物标签上未提及配对适应症，则将其标记为该药物的标签外使用。

结果

使用该算法，我们确定了 289 个标签外适应症，召回率为 76%。

结论

本研究设计的方法从 OHC 中的通俗帖子中识别和提取药物和适应症之间的语义关系。结果证明了使用自然语言处理技术在各种药物的在线健康论坛上识别标签外药物使用的可行性。了解患者对药物的标签外使用可能有助于制造商进行创新，以更好地满足患者的需求并协助医生做出处方决定。