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PHARMACOVIGILANCE SYSTEMS AND METHODS UTILIZING CASCADING FILTERS AND MACHINE LEARNING MODELS TO CLASSIFY AND DISCERN PHARMACEUTICAL TRENDS FROM SOCIAL MEDIA POSTS
PHARMACOVIGILANCE SYSTEMS AND METHODS UTILIZING CASCADING FILTERS AND MACHINE LEARNING MODELS TO CLASSIFY AND DISCERN PHARMACEUTICAL TRENDS FROM SOCIAL MEDIA POSTS
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机译:利用级联过滤器和机器学习模型对社交媒体帖子中的药物趋势进行分类和区分的药物监控系统和方法
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摘要
Systems and methods for utilizing filters to reduce an incoming stream of textual messages to a smaller subset of potentially relevant textual messages, and using trained machine learning models to analyze and classify the content of such textual messages. Analyzed messages that belong to a relevant class as determined by the machine learning model are stored in a database, giving users the ability to determine and analyze trends from the subset of messages, such as adverse side effects caused by pharmaceuticals or the efficacy of pharmaceuticals. Relationships between the side effects caused by different pharmaceuticals can be used to predict potential candidates for drug repositioning.
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