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Juried Poster Session 1 of 2
Time: 23/Mar/2017: 5:00pm-6:30pm · Location: Westin Grand Ballroom / Westin III

Wikipedia Based Automatic Diagnosis Prediction in Clinical Decision Support System

Danchen Zhang1, Daqing He1, Sanqiang Zhao1, Lei Li2

1University of Pittsburgh, United States of America; 2Nanjing University of Science and Technology, China

When making clinical decisions, physicians often consult biomedical literatures for reference. In this case, an effective clinical decision support system, provided with a patient’s health information, should be able to generate accurate queries and return to the physicians with useful articles. Related works in the Clinical Decision Support (CDS) track of TREC 2015 demonstrated the usefulness of knowing patients’ diagnosis information for supporting more effective retrieval, but the diagnosis information is often missing in most cases. Furthermore, it is still a great challenge to perform large-scale automatic diagnosis prediction. This motivates us to propose an automatic diagnosis prediction method to enhance the retrieval in a clinical decision support system, where the evidence for the prediction is extracted from Wikipedia. Through the evaluation conducted on 2014 CDS tasks, our method reaches the best performance among all submitted runs. In the next step, graph structured evidence will be integrated to make the prediction more accurate.

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