Analyzing Patient Decision Making in Online Health Communities

Mingda Li, Jinhe Shi, Yi Chen

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

2 Scopus citations

Abstract

In recent years, many users join online health communities (OHC) to obtain information and seek social support. One of the most important type of support that a patient needs is to obtain suggestions and information when they make decisions in their diagnosis and treatments. To understand patient decision making processes, we propose to identify the threads on OHC discussion forum that are about patient decision making, and then analyze the questions that patients have. We use deep learning based model to identify such threads. Experiment results show that the proposed methods achieve good performance in precision, recall, F1 score, accuracy, and AUC. Then we leverage topic modeling techniques to analyze the questions that patients expressed in those threads to get a better understanding of patient decision making.

Original languageEnglish (US)
Title of host publication2019 IEEE International Conference on Healthcare Informatics, ICHI 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538691380
DOIs
StatePublished - Jun 2019
Event7th IEEE International Conference on Healthcare Informatics, ICHI 2019 - Xi'an, China
Duration: Jun 10 2019Jun 13 2019

Publication series

Name2019 IEEE International Conference on Healthcare Informatics, ICHI 2019

Conference

Conference7th IEEE International Conference on Healthcare Informatics, ICHI 2019
CountryChina
CityXi'an
Period6/10/196/13/19

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Health Informatics
  • Biomedical Engineering

Keywords

  • Deep learning
  • Online health community
  • Patient decision making
  • Topic modeling

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