A process mining- deep learning approach to predict survival in a cohort of hospitalized COVID‐19 patients

M. Pishgar, S. Harford, J. Theis, W. Galanter, J. M. Rodríguez-Fernández, L. H. Chaisson, Y. Zhang, A. Trotter, K. M. Kochendorfer, A. Boppana, H. Darabi

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number194
JournalBMC Medical Informatics and Decision Making
Volume22
Issue number1
DOIs
StatePublished - Dec 2022
Externally publishedYes

Keywords

  • COVID-19 prediction
  • Deep learning
  • Machine learning
  • Mortality prediction
  • Process mining
  • SARS-CoV-2

ASJC Scopus subject areas

  • Health Policy
  • Health Informatics
  • Computer Science Applications

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