Wikipedia-based automatic diagnosis prediction in clinical decision support systems

Zhang, Danchen; He, Daqing; Zhao, Sanqiang; Li, Lei

Permalink

https://hdl.handle.net/2142/96736 Copy

Description

Title

Wikipedia-based automatic diagnosis prediction in clinical decision support systems

Author(s)

• Zhang, Danchen
• He, Daqing
• Zhao, Sanqiang
• Li, Lei

Issue Date

2017

Keyword(s)

• Automatic diagnosis prediction
• Clinical decision support system
• Text mining

Abstract

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.

Publisher

iSchools

Series/Report Name or Number

iConference 2017 Proceedings

Type of Resource

text

Language

en

Permalink

http://hdl.handle.net/2142/96736

Copyright and License Information

Copyright 2017 Danchen Zhang, Daqing He, Sanqiang Zhao, and Lei Li

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