Preclude: Conflict detection in textual health advice
Preclude: Conflict detection in textual health advice
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DOI：
10.1109/percom.2017.7917875
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发表时间：
2017-03
期刊：
影响因子：
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通讯作者：
S. Preum;M. A. S. Mondol;Meiyi Ma;Hongning Wang;J. Stankovic
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作者：
S. Preum;M. A. S. Mondol;Meiyi Ma;Hongning Wang;J. Stankovic
随着卫生部门的快速数字化，人们经常转向移动的应用程序和在线健康网站寻求健康建议。从不同来源产生的健康建议可能相互冲突，因为它们涉及健康的不同方面（例如，体重减轻、饮食、疾病）或由于他们不知道用户的背景（例如，年龄、性别、生理状况）。冲突可能由于词汇特征（例如否定、反义词或数字不匹配）而发生，也可能取决于时间和/或生理状态。我们制定的问题，找到相互矛盾的健康建议，并制定了一个全面的分类冲突。虽然自然语言处理领域的一个类似研究领域探索了文本矛盾识别的问题，但在健康建议中发现冲突带来了自己独特的词汇和语义挑战。这些包括文本和假设对之间的大的结构变化，发现建议对之间的概念重叠，以及对建议的语义的推断（即，做什么，为什么，怎么做）。因此，我们开发了一种新的语义规则为基础的解决方案，以检测冲突的健康建议，利用语言规则和外部知识库的异构来源。由于我们的解决方案是可解释的和全面的，它也可以指导用户解决冲突。我们使用1156个真实的建议声明来评估Preclude，这些建议声明涵盖了从智能手机健康应用程序和流行的健康网站收集的8个重要健康主题。Preclude的准确率为90%，分别比基线方法的准确率和F1得分高出约1.5倍和3倍。
With the rapid digitalization of the health sector, people often turn to mobile apps and online health websites for health advice. Health advice generated from different sources can be conflicting as they address different aspects of health (e.g., weight loss, diet, disease) or as they are unaware of the context of a user (e.g., age, gender, physiological condition). Conflicts can occur due to lexical features, (such as, negation, antonyms, or numerical mismatch) or can be conditioned upon time and/or physiological status. We formulate the problem of finding conflicting health advice and develop a comprehensive taxonomy of conflicts. While a similar research area in the natural language processing domain explores the problem of textual contradiction identification, finding conflicts in health advice poses its own unique lexical and semantic challenges. These include large structural variation between text and hypothesis pairs, finding conceptual overlap between pairs of advice, and inference of the semantics of an advice (i.e., what to do, why and how). Hence, we develop Preclude, a novel semantic rule-based solution to detect conflicting health advice derived from heterogeneous sources utilizing linguistic rules and external knowledge bases. As our solution is interpretable and comprehensive, it can guide users towards conflict resolution too. We evaluate Preclude using 1156 real advice statements covering 8 important health topics that are collected from smart phone health apps and popular health websites. Preclude results in 90% accuracy and outperforms the accuracy and F1 score of the baseline approach by about 1.5 times and 3 times, respectively.