Toward automated classification of consumers' cancer-related questions with a new taxonomy of expected answer types

Health Informatics Journal
Susan McRoyAdam Kurmally

Abstract

This article examines methods for automated question classification applied to cancer-related questions that people have asked on the web. This work is part of a broader effort to provide automated question answering for health education. We created a new corpus of consumer-health questions related to cancer and a new taxonomy for those questions. We then compared the effectiveness of different statistical methods for developing classifiers, including weighted classification and resampling. Basic methods for building classifiers were limited by the high variability in the natural distribution of questions and typical refinement approaches of feature selection and merging categories achieved only small improvements to classifier accuracy. Best performance was achieved using weighted classification and resampling methods, the latter yielding an accuracy of F1 = 0.963. Thus, it would appear that statistical classifiers can be trained on natural data, but only if natural distributions of classes are smoothed. Such classifiers would be useful for automated question answering, for enriching web-based content, or assisting clinical professionals to answer questions.

References

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Citations

Oct 27, 2018·Social Work·Julie A CederbaumRobert H Keefe
Mar 29, 2018·BMC Medical Informatics and Decision Making·Haihong GuoJiao Li

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Methods Mentioned

BETA
medical procedure

Software Mentioned

Structured Query Language ( SQL )
Weka
WordList
EAT Taxonomy
LibSVM

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