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

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journal contribution
posted on 19.08.2022, 17:24 authored by M Pishgar, S Harford, J Theis, William GalanterWilliam Galanter, JM Rodríguez-Fernández, Lelia ChaissonLelia Chaisson, Y Zhang, A Trotter, Karl KochendorferKarl Kochendorfer, A Boppana, Houshang DarabiHoushang Darabi
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.

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Citation

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. (2022). A process mining- deep learning approach to predict survival in a cohort of hospitalized COVID‐19 patients. BMC Medical Informatics and Decision Making, 22(1), 194-. https://doi.org/10.1186/s12911-022-01934-2

Publisher

Springer Science and Business Media LLC

Language

en

issn

1472-6947