@inproceedings{campillos-llanos-etal-2017-automatic,
title = "Automatic classification of doctor-patient questions for a virtual patient record query task",
author = "Campillos Llanos, Leonardo and
Rosset, Sophie and
Zweigenbaum, Pierre",
editor = "Cohen, Kevin Bretonnel and
Demner-Fushman, Dina and
Ananiadou, Sophia and
Tsujii, Junichi",
booktitle = "{B}io{NLP} 2017",
month = aug,
year = "2017",
address = "Vancouver, Canada,",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-2343",
doi = "10.18653/v1/W17-2343",
pages = "333--341",
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.",
}
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%0 Conference Proceedings
%T Automatic classification of doctor-patient questions for a virtual patient record query task
%A Campillos Llanos, Leonardo
%A Rosset, Sophie
%A Zweigenbaum, Pierre
%Y Cohen, Kevin Bretonnel
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S BioNLP 2017
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, Canada,
%F campillos-llanos-etal-2017-automatic
%X 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.
%R 10.18653/v1/W17-2343
%U https://aclanthology.org/W17-2343
%U https://doi.org/10.18653/v1/W17-2343
%P 333-341
Markdown (Informal)
[Automatic classification of doctor-patient questions for a virtual patient record query task](https://aclanthology.org/W17-2343) (Campillos Llanos et al., BioNLP 2017)
ACL