Process mining the trauma resuscitation patient cohorts

  • Sen Yang
  • , Fei Tao
  • , Jingyuan Li
  • , Dawei Wang
  • , Shuhong Chen
  • , Omar Z. Ahmed
  • , Ivan Marsic
  • , Randall S. Burd

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

5 Scopus citations

Abstract

In this study, we present a framework for analyzing associations between patient cohorts and the trauma resuscitation procedures their patients received. Our framework works by quantifying associations between discovered patient cohorts and treatment patterns. We evaluated our framework on a trauma resuscitation dataset collected in a level 1 trauma center. Our experimental results show that using weights learned by our algorithm improves measurements of patient similarity. Four patient cohorts were then found via clustering, and statistically significant resuscitation patterns were discovered using process mining techniques. Though only tested on the trauma resuscitation process, our framework can be generalized to analyze other medical processes.

Original languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages29-35
Number of pages7
ISBN (Electronic)9781538653777
DOIs
StatePublished - Jul 24 2018
Event6th IEEE International Conference on Healthcare Informatics, ICHI 2018 - New York, United States
Duration: Jun 4 2018Jun 7 2018

Publication series

NameProceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018

Other

Other6th IEEE International Conference on Healthcare Informatics, ICHI 2018
Country/TerritoryUnited States
CityNew York
Period6/4/186/7/18

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Health Informatics

Keywords

  • Medical Workflow Analysis
  • Patient Cohort Analysis
  • Process Mining
  • Trauma Resuscitation

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