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-19 patients and updated the prediction in 6h 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.

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Recommended citation: M. Pishgar, S. Harford, J. Theis, W. Galanter, J. M. Rodríguez-Fernández, L. H Chaisson, Y. Zhang, A. Trotter, K. M. Kochendorfer, A. Boppana, and H. Darabi, "A process mining- deep learning approach to predict survival in a cohort of hospitalized COVID‐19 patients," BMC Medical Informatics and Decision Making 22, Article number: 194 (2022). doi: https://doi.org/10.1186/s12911-022-01934-2