Question Processing and Clustering in INDOC: A Biomedical Question Answering System

The exponential growth in the volume of publications in the biomedical domain has made it impossible for an individual to keep pace with the advances. Even though evidence-based medicine has gained wide acceptance, the physicians are unable to access the relevant information in the required time, leaving most of the questions unanswered. This accentuates the need for fast and accurate biomedical question answering systems. In this paper we introduce INDOC—a biomedical question answering system based on novel ideas of indexing and extracting the answer to the questions posed. INDOC displays the results in clusters to help the user arrive the most relevant set of documents quickly. Evaluation was done against the standard OHSUMED test collection. Our system achieves high accuracy and minimizes user effort.

[1]  M. Ebell,et al.  Analysis of questions asked by family doctors regarding patient care , 1999, BMJ.

[2]  G. Bergus,et al.  Does the structure of clinical questions affect the outcome of curbside consultations with specialty colleagues? , 2000, Archives of family medicine.

[3]  Gosse Bouma,et al.  Developing Offline Strategies for Answering Medical Questions , 2005 .

[4]  Sharon E. Strata Bringing evidence to the point of care , 1999, Evidence Based Medicine.

[5]  Olivier Bodenreider,et al.  Aggregating UMLS Semantic Types for Reducing Conceptual Complexity , 2001, MedInfo.

[6]  Stefan Schulz,et al.  Biomedical text retrieval in languages with a complex morphology , 2002, ACL Workshop on Natural Language Processing in the Biomedical Domain.

[7]  P. Gorman,et al.  A taxonomy of generic clinical questions: classification study , 2000, BMJ : British Medical Journal.

[8]  Chris Buckley,et al.  OHSUMED: an interactive retrieval evaluation and new large test collection for research , 1994, SIGIR '94.

[9]  Graeme Hirst,et al.  Analysis of Semantic Classes in Medical Text for Question Answering , 2004 .

[10]  D. Sackett Evidence-Based Medicine: How to Practice and Teach EBM , 2018 .

[11]  M H Ebell,et al.  Putting computer-based evidence in the hands of clinicians. , 1999, JAMA.

[12]  Olivier Bodenreider,et al.  Exploring semantic groups through visual approaches , 2003, J. Biomed. Informatics.

[13]  Rona F Levin,et al.  Bringing Evidence to the Point of Care , 2008, Research and Theory for Nursing Practice.

[14]  Pierre Zweigenbaum,et al.  Towards a Medical Question-Answering System: a Feasibility Study , 2003, MIE.

[15]  Alan R. Aronson,et al.  Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program , 2001, AMIA.

[16]  M. Ebell,et al.  Obstacles to answering doctors' questions about patient care with evidence: qualitative study , 2002, BMJ : British Medical Journal.

[17]  P. Gorman,et al.  Can primary care physicians' questions be answered using the medical journal literature? , 1994, Bulletin of the Medical Library Association.

[18]  J. MacQueen Some methods for classification and analysis of multivariate observations , 1967 .

[19]  Graeme Hirst,et al.  Answering Clinical Questions with Role Identification , 2003, BioNLP@ACL.

[20]  William R. Hersh,et al.  A survey of current work in biomedical text mining , 2005, Briefings Bioinform..

[21]  G. Guyatt,et al.  Practitioners of evidence based care , 2000, BMJ : British Medical Journal.