Paper Info
Title
A process mining- deep learning approach to predict survival in a cohort of hospitalized COVID‐19 patients
Abstract
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. 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. 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. 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.
Year
DOI
Venue
2022
10.1186/s12911-022-01934-2
BMC Medical Informatics and Decision Making
Keywords
DocType
Volume
Mortality prediction, Process mining, Deep learning, COVID-19 prediction, Machine learning, SARS-CoV-2
Journal
22
Issue
ISSN
Citations 
1
1472-6947
0
PageRank 
References 
Authors
0.34
6
11
Name
Order
Citations
PageRank
M Pishgar100.34
S Harford200.34
J Theis300.34
W Galanter400.34
J M Rodríguez-Fernández500.34
L H Chaisson600.34
Yingze Zhang721.71
A Trotter800.34
K M Kochendorfer900.34
A Boppana1000.34
H Darabi1100.34