论文标题
通过自然语言处理和无监督的学习从简短的现实医学询问中发现关键主题
Discovering key topics from short, real-world medical inquiries via natural language processing and unsupervised learning
论文作者
论文摘要
每年制药公司都会收到数百万个未经请求的医疗查询。据推测，这些查询代表了信息的宝库，有可能深入了解有关药品和相关医疗治疗的问题。但是，由于查询的批量和专业性质，很难及时进行，经常性和全面的分析。在这里，我们提出了一种基于自然语言处理和无监督学习的机器学习方法，以自动发现客户的现实医学询问中的关键主题。这种方法不需要本体论或注释。根据医学信息专家的判断，发现的主题具有有意义的医学相关性，因此表明未经请求的医疗查询是有价值的客户见解的来源。我们的工作为制药行业的医疗查询进行机器学习驱动的分析铺平了道路，该分析最终旨在改善患者护理。
Millions of unsolicited medical inquiries are received by pharmaceutical companies every year. It has been hypothesized that these inquiries represent a treasure trove of information, potentially giving insight into matters regarding medicinal products and the associated medical treatments. However, due to the large volume and specialized nature of the inquiries, it is difficult to perform timely, recurrent, and comprehensive analyses. Here, we propose a machine learning approach based on natural language processing and unsupervised learning to automatically discover key topics in real-world medical inquiries from customers. This approach does not require ontologies nor annotations. The discovered topics are meaningful and medically relevant, as judged by medical information specialists, thus demonstrating that unsolicited medical inquiries are a source of valuable customer insights. Our work paves the way for the machine-learning-driven analysis of medical inquiries in the pharmaceutical industry, which ultimately aims at improving patient care.
