Incorporating Statistical Topic Models in the Retrieval of Healthcare Documents

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).