A process mining- deep learning approach to predict survival in a cohort of hospitalized COVID-19 patients.
Background: Various machine learning and artificial intelligence methods have been used to predict outcomes of hospitalized COVID-19 patients. However, process mining has not yet been used for COVID-19 prediction. We developed a process mining/deep learning approach to predict mortality among COVID-...
| Publicado en: | BMC Medical Informatics & Decision Making Vol. 22; no. 1; pp. 1 - 17 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
BioMed Central
7/25/2022
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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=158162745&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158162745 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 7/25/2022 vid: 22 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 158162745 158162745 NLM35879715 10.1186/s12911-022-01934-2 NLM35879715 158162745 ppf: 1 ppct: 16 formats: tig: atl: A process mining- deep learning approach to predict survival in a cohort of hospitalized COVID-19 patients. aug: au: Pishgar, M. Harford, S. Theis, J. Galanter, W. Rodríguez-Fernández, J. M. Chaisson, L. H Zhang, Y. Trotter, A. Kochendorfer, K. M. Boppana, A. Darabi, H. affil: Department of Mechanical and Industrial Engineering, University of Illinois at Chicago (UIC), 842 W Taylor Street, MC 251, 60607, Chicago, IL, USA sug: ab: Background: Various machine learning and artificial intelligence methods have been used to predict outcomes of hospitalized COVID-19 patients. However, process mining has not yet been used for COVID-19 prediction. We developed a process mining/deep learning approach to predict mortality among COVID-19 patients and updated the prediction in 6-h intervals during the first 72 h after hospital admission.Methods: The process mining/deep learning model produced temporal information related to the variables and incorporated demographic and clinical data to predict mortality. The mortality prediction was updated in 6-h intervals during the first 72 h after hospital admission. Moreover, the performance of the model was compared with published and self-developed traditional machine learning models that did not use time as a variable. The performance was compared using the Area Under the Receiver Operator Curve (AUROC), accuracy, sensitivity, and specificity.Results: The proposed process mining/deep learning model outperformed the comparison models in almost all time intervals with a robust AUROC above 80% on a dataset that was imbalanced.Conclusions: Our proposed process mining/deep learning model performed significantly better than commonly used machine learning approaches that ignore time information. Thus, time information should be incorporated in models to predict outcomes more accurately. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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