Automated Process Mining and Learning of Therapeutic Actions in the Intensive Care Unit

Anna Romanov, Yuval Shahar

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this study, we implemented a hybrid approach, incorporating temporal data mining, machine learning, and process mining for modeling and predicting the course of treatment of Intensive Care Unit (ICU) patients. We used process mining algorithms to construct models of management of ICU patients. Then, we extracted the decision points from the mined models and used temporal data mining of the periods preceding the decision points to create temporal-pattern features. We trained classifiers to predict the next actions expected for each point. The methodology was evaluated on medical ICU data from the hypokalemia and hypoglycemia domains. The study's contributions include the representation of medical treatment trajectories of ICU patients using process models, and the integration of Temporal Data Mining and Machine Learning with Process Mining, to predict the next therapeutic actions in the ICU.

Original languageAmerican English
Title of host publicationMEDINFO 2023 - The Future is Accessible
Subtitle of host publicationProceedings of the 19th World Congress on Medical and Health Informatics
EditorsJen Bichel-Findlay, Paula Otero, Philip Scott, Elaine Huesing
PublisherIOS Press BV
Pages825-829
Number of pages5
ISBN (Electronic)9781643684567
DOIs
StatePublished - 25 Jan 2024
Event19th World Congress on Medical and Health Informatics, MedInfo 2023 - Sydney, Australia
Duration: 8 Jul 202312 Jul 2023

Publication series

NameStudies in Health Technology and Informatics
Volume310

Conference

Conference19th World Congress on Medical and Health Informatics, MedInfo 2023
Country/TerritoryAustralia
CitySydney
Period8/07/2312/07/23

Keywords

  • Process mining
  • temporal data mining

All Science Journal Classification (ASJC) codes

  • Health Information Management
  • Health Informatics
  • Biomedical Engineering

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