Automated Process Mining and Learning of Therapeutic Actions in the Intensive Care Unit...19th World Congress on Medical and Health Informatics, July 8-12, 2023, New South Wales, Australia
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. Th...
| Publicado en: | Studies in Health Technology & Informatics Vol. 310; pp. 825 - 830 |
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| Autores principales: | , |
| Formato: | proceedings research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=175248890&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 175248890 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2023 vid: 310 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 175248890 175248890 175248890 10.3233/SHTI231080 175248890 ppf: 825 ppct: 5 formats: tig: atl: Automated Process Mining and Learning of Therapeutic Actions in the Intensive Care Unit...19th World Congress on Medical and Health Informatics, July 8-12, 2023, New South Wales, Australia aug: au: ROMANOV, Anna SHAHAR, Yuval affil: Department of Software and Information Systems Engineering, Israel sug: subj: Automation Critical Care Intensive Care Units Data Mining Machine Learning Algorithms Prediction Models Decision Making, Clinical Decision Support Techniques Congresses and Conferences New South Wales New South Wales Human Critically Ill Patients Hypokalemia Therapy Hypoglycemia Therapy Descriptive Statistics Electronic Health Records Funding Source ab: 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. pubtype: Academic Journal doctype: proceedings research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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