Patients often search for information on the web about treatments and diseases after they are discharged from the hospital. However, searching for medical information on the web poses challenges due to related terms and synonymy for the same disease and treatment. In this paper, we present a method that combines Statistical Topics Models, Language Models and Natural Language Processing to retrieve healthcare related documents. In addition, we test if the incorporation of terms extracted from the patient’s discharge summary improves the retrieval performance. We show that the proposed framework outperformed the winner of the retrieval CLEF eHealth 2013 challenge by 68% in the MAP measure (0:5226 vs 0:3108), and by 13% in NDCG (0:5202 vs 0:3637). Compared with standard language models, we obtain an improvement of 92% in MAP (0:2666) and 45% in NDCG. (0:3637)
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机译：患者出院后，通常会在网上搜索有关治疗和疾病的信息。然而，由于相关疾病和相同疾病和治疗的同义词，在网络上搜索医学信息提出了挑战。在本文中，我们提出了一种结合统计主题模型，语言模型和自然语言处理来检索医疗保健相关文档的方法。此外，我们测试合并从患者出院摘要中提取的字词是否会改善检索性能。我们显示，提出的框架在MAP衡量指标（0：5226与0：3108）方面胜过了CLEF eHealth 2013检索挑战的赢家（68：5226 vs 0：3108），而在NDCG中则胜出了13％（0：5202 vs 0：3637）。与标准语言模型相比，我们在MAP（0：2666）和NDCG方面分别提高了92％和45％。 （0：3637）
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