MEDCOD: A Medically-Accurate, Emotive, Diverse, and Controllable Dialog System

Rhys Compton, Ilya Valmianski, Li Deng, Costa Huang, Namit Katariya, Xavier Amatriain, Anitha Kannan
Proceedings of Machine Learning for Health, PMLR 158:110-129, 2021.

Abstract

We present MEDCOD, a Medically-Accurate, Emotive, Diverse, and Controllable Dialog system with a unique approach to the natural language generator module. MEDCOD has been developed and evaluated specifically for the history taking task. It integrates the advantage of a traditional modular approach to incorporate (medical) domain knowledge with modern deep learning techniques to generate flexible, human-like natural language expressions. Two key aspects of MEDCOD’s natural language output are described in detail. First, the generated sentences are emotive and empathetic, similar to how a doctor would communicate to the patient. Second, the generated sentence structures and phrasings are varied and diverse while maintaining medical consistency with the desired medical concept (provided by the dialogue manager module of MEDCOD). Experimental results demonstrate the effectiveness of our approach in creating a human-like medical dialogue system. Relevant code is available at https://github.com/curai/curai-research/tree/main/MEDCOD.

Cite this Paper

BibTeX
@InProceedings{pmlr-v158-compton21a, title = {MEDCOD: A Medically-Accurate, Emotive, Diverse, and Controllable Dialog System}, author = {Compton, Rhys and Valmianski, Ilya and Deng, Li and Huang, Costa and Katariya, Namit and Amatriain, Xavier and Kannan, Anitha}, booktitle = {Proceedings of Machine Learning for Health}, pages = {110--129}, year = {2021}, editor = {Roy, Subhrajit and Pfohl, Stephen and Rocheteau, Emma and Tadesse, Girmaw Abebe and Oala, Luis and Falck, Fabian and Zhou, Yuyin and Shen, Liyue and Zamzmi, Ghada and Mugambi, Purity and Zirikly, Ayah and McDermott, Matthew B. A. and Alsentzer, Emily}, volume = {158}, series = {Proceedings of Machine Learning Research}, month = {04 Dec}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v158/compton21a/compton21a.pdf}, url = {https://proceedings.mlr.press/v158/compton21a.html}, abstract = {We present MEDCOD, a Medically-Accurate, Emotive, Diverse, and Controllable Dialog system with a unique approach to the natural language generator module. MEDCOD has been developed and evaluated specifically for the history taking task. It integrates the advantage of a traditional modular approach to incorporate (medical) domain knowledge with modern deep learning techniques to generate flexible, human-like natural language expressions. Two key aspects of MEDCOD’s natural language output are described in detail. First, the generated sentences are emotive and empathetic, similar to how a doctor would communicate to the patient. Second, the generated sentence structures and phrasings are varied and diverse while maintaining medical consistency with the desired medical concept (provided by the dialogue manager module of MEDCOD). Experimental results demonstrate the effectiveness of our approach in creating a human-like medical dialogue system. Relevant code is available at https://github.com/curai/curai-research/tree/main/MEDCOD.} }
Endnote
%0 Conference Paper %T MEDCOD: A Medically-Accurate, Emotive, Diverse, and Controllable Dialog System %A Rhys Compton %A Ilya Valmianski %A Li Deng %A Costa Huang %A Namit Katariya %A Xavier Amatriain %A Anitha Kannan %B Proceedings of Machine Learning for Health %C Proceedings of Machine Learning Research %D 2021 %E Subhrajit Roy %E Stephen Pfohl %E Emma Rocheteau %E Girmaw Abebe Tadesse %E Luis Oala %E Fabian Falck %E Yuyin Zhou %E Liyue Shen %E Ghada Zamzmi %E Purity Mugambi %E Ayah Zirikly %E Matthew B. A. McDermott %E Emily Alsentzer %F pmlr-v158-compton21a %I PMLR %P 110--129 %U https://proceedings.mlr.press/v158/compton21a.html %V 158 %X We present MEDCOD, a Medically-Accurate, Emotive, Diverse, and Controllable Dialog system with a unique approach to the natural language generator module. MEDCOD has been developed and evaluated specifically for the history taking task. It integrates the advantage of a traditional modular approach to incorporate (medical) domain knowledge with modern deep learning techniques to generate flexible, human-like natural language expressions. Two key aspects of MEDCOD’s natural language output are described in detail. First, the generated sentences are emotive and empathetic, similar to how a doctor would communicate to the patient. Second, the generated sentence structures and phrasings are varied and diverse while maintaining medical consistency with the desired medical concept (provided by the dialogue manager module of MEDCOD). Experimental results demonstrate the effectiveness of our approach in creating a human-like medical dialogue system. Relevant code is available at https://github.com/curai/curai-research/tree/main/MEDCOD.
APA
Compton, R., Valmianski, I., Deng, L., Huang, C., Katariya, N., Amatriain, X. & Kannan, A.. (2021). MEDCOD: A Medically-Accurate, Emotive, Diverse, and Controllable Dialog System. Proceedings of Machine Learning for Health, in Proceedings of Machine Learning Research 158:110-129 Available from https://proceedings.mlr.press/v158/compton21a.html.

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