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,
Editors:
Kevin Bretonnel Cohen, Dina Demner-Fushman, Sophia Ananiadou, Junichi Tsujii
Venue:
BioNLP
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
333–341
Language:
URL:
https://aclanthology.org/W17-2343
DOI:
10.18653/v1/W17-2343
Bibkey:
Cite (ACL):
Leonardo Campillos Llanos, Sophie Rosset, and Pierre Zweigenbaum. 2017. Automatic classification of doctor-patient questions for a virtual patient record query task. In BioNLP 2017, pages 333–341, Vancouver, Canada,. Association for Computational Linguistics.
Cite (Informal):
Automatic classification of doctor-patient questions for a virtual patient record query task (Campillos Llanos et al., BioNLP 2017)
Copy Citation:
PDF:
https://aclanthology.org/W17-2343.pdf
Data
Doctor-patient questions (French)