Examining effects of fit between patient need and social support- A deep learning based multi-label classification approach

Anqi Xu, Xiao Liu, Paul Jen Hwa Hu

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

Abstract

Cancer patients' emotions are important to their health and outcomes. In light of appraisal theory and optimal matching theory, a viable way to create positive changes in patient emotion is through the match between their indicated needs and provided support, as well as the relevance of the support. In this study, we extend extant literature by empirically examining the effects of matched/unmatched patient need and relevant social support on their emotion change. We develop a novel deep learning based multi-label classification method to categorize different types of patient needs and social support. Our proposed method surpasses seven robust benchmark models. The empirical analysis suggests relevant support can increase patient happiness while unmatched patient needs negatively impact happiness. Both the matched needs and relevant support are associated with increased fear, but this effect can be reduced by including more images in replies. This study provides several implications to community operators or facilitators on how to encourage members' participation and to provide the needed support to patients.

Original languageEnglish (US)
Title of host publication40th International Conference on Information Systems, ICIS 2019
PublisherAssociation for Information Systems
ISBN (Electronic)9780996683197
StatePublished - 2020
Event40th International Conference on Information Systems, ICIS 2019 - Munich, Germany
Duration: Dec 15 2019Dec 18 2019

Publication series

Name40th International Conference on Information Systems, ICIS 2019

Conference

Conference40th International Conference on Information Systems, ICIS 2019
CountryGermany
CityMunich
Period12/15/1912/18/19

Keywords

  • Deep learning
  • Multi-label classification
  • Online cancer community
  • Patient emotion
  • Patient need
  • Social support

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems

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