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
- 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)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/W17-2343.pdf
- Data
- Doctor-patient questions (French)