Automatic classification of doctor-patient questions for a virtual patient record query task

Leonardo Campillos Llanos, Sophie Rosset, Pierre Zweigenbaum


Abstract
We present the work-in-progress of automating the classification of doctor-patient questions in the context of a simulated consultation with a virtual patient. We classify questions according to the computational strategy (rule-based or other) needed for looking up data in the clinical record. We compare ‘traditional’ machine learning methods (Gaussian and Multinomial Naive Bayes, and Support Vector Machines) and a neural network classifier (FastText). We obtained the best results with the SVM using semantic annotations, whereas the neural classifier achieved promising results without it.
Anthology ID:
W17-2343
Volume:
BioNLP 2017
Month:
August
Year:
2017
Address:
Vancouver, Canada,
Venues:
BioNLP | WS
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
333–341
Language:
URL:
https://www.aclweb.org/anthology/W17-2343
DOI:
10.18653/v1/W17-2343
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-2343.pdf