Modeling doctor-patient communication with affective text analysis

Taylan Sen, Mohammad Rafayet Ali, Mohammed Ehsan Hoque, Ronald Epstein, Paul Duberstein

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

Abstract

We present a method of automatic analysis of doctor-patient communication and present findings after applying this methodology in a post hoc study of communication between oncologists and their cancer patients (N=122). We analyzed several features of each participant in the conversation including the number of words spoken, the average positive/negative sentiment expressed, the number of questions asked, and the word diversity (unique word count). We found that the number of words spoken by the doctor is correlated with the highest doctor communication ability ratings made by patients. We additionally found that unsupervised clustering of conversation features into 'styles' identified that certain styles are associated with higher communication ratings. Two well-defined styles emerged when clustering based on doctor word diversity and doctor sentiment: A high word diversity-neutral sentiment style, which was associated with higher ratings, and a low word diversity-positive sentiment style with lower average ratings. Machine learning models were trained to automatically predict whether a doctor-patient interaction will be rated high or not with a best-performing 71% test set accuracy.

Original languageEnglish (US)
Title of host publication2017 7th International Conference on Affective Computing and Intelligent Interaction, ACII 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages170-177
Number of pages8
ISBN (Electronic)9781538605639
DOIs
StatePublished - Jul 2 2017
Externally publishedYes
Event7th International Conference on Affective Computing and Intelligent Interaction, ACII 2017 - San Antonio, United States
Duration: Oct 23 2017Oct 26 2017

Publication series

Name2017 7th International Conference on Affective Computing and Intelligent Interaction, ACII 2017
Volume2018-January

Conference

Conference7th International Conference on Affective Computing and Intelligent Interaction, ACII 2017
Country/TerritoryUnited States
CitySan Antonio
Period10/23/1710/26/17

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Human-Computer Interaction
  • Behavioral Neuroscience
  • Social Psychology

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