Literature DB >> 22874183

CliniQA : highly reliable clinical question answering system.

Yuan Ni1, Huijia Zhu, Peng Cai, Lei Zhang, Zhaoming Qui, Feng Cao.   

Abstract

Evidence-based medicine (EBM) aims to apply the best available evidences gained from scientific method to clinical decision making. From the computer science point of view, the current bottleneck of applying EBM by a decision maker (either a patient or a physician) is the time-consuming manual retrieval, appraisal, and interpretation of scientific evidences from large volume of and rapidly increasing medical research reports. Patients do not have the expertise to do it. For physicians, study has shown that they usually have insufficient time to conduct the task. CliniQA tries to shift the burden of time and expertise from the decision maker to the computer system. Given a single clinical foreground question, the CliniQA will return a highly reliable answer based on existing medical research reports. Besides this, the CliniQA will also return the analyzed information from the research report to help users appraise the medical evidences more efficiently.

Entities:  

Mesh:

Year:  2012        PMID: 22874183

Source DB:  PubMed          Journal:  Stud Health Technol Inform        ISSN: 0926-9630


  3 in total

1.  Combining Open-domain and Biomedical Knowledge for Topic Recognition in Consumer Health Questions.

Authors:  Yassine Mrabet; Halil Kilicoglu; Kirk Roberts; Dina Demner-Fushman
Journal:  AMIA Annu Symp Proc       Date:  2017-02-10

2.  An Ensemble Learning Strategy for Eligibility Criteria Text Classification for Clinical Trial Recruitment: Algorithm Development and Validation.

Authors:  Kun Zeng; Zhiwei Pan; Yibin Xu; Yingying Qu
Journal:  JMIR Med Inform       Date:  2020-07-01

3.  Qcorp: an annotated classification corpus of Chinese health questions.

Authors:  Haihong Guo; Xu Na; Jiao Li
Journal:  BMC Med Inform Decis Mak       Date:  2018-03-22       Impact factor: 2.796

  3 in total

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