A Hybrid Approach to Clinical Question Answering

Abstract

In this paper, we describe our clinical question answering system developed and submitted for the Text Retrieval Conference (TREC 2014) Clinical Decision Support (CDS) track. The task for this track was to retrieve relevant biomedical articles to answer generic clinical questions about medical case reports. As part of our maiden participation in TREC, we submitted a single run using a hybrid Natural Language Processing (NLP)-driven approach to accomplish the given task. Evaluation results showed that our clinical question answering system achieved the best scores in two of eight dual-judged topics: #5 and 27, and performed relatively better compared to the median scores for topics: #13, 18, 19, 22, and 23.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 2014
Accession Number
ADA618772

Entities

People

  • Joey Liu
  • Oladimeji Farri
  • Sadid A. Hasan
  • Xianshu Zhu
  • Yao Dong

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Clinical Medicine
  • Computational Linguistics
  • Information Exchange
  • Information Retrieval
  • Knowledge Management
  • Language
  • Linguistics
  • Literature Surveys
  • Natural Language Processing
  • Natural Languages
  • North America
  • Ontologies
  • Patient Care
  • Precision
  • Standards
  • Test And Evaluation

Readers

  • Computational Linguistics
  • Information Retrieval
  • Technical Research and Report Writing.

Technology Areas

  • AI & ML
  • AI & ML - Information Retrieval
  • Biotechnology