ArticleBMC medical informatics and decision making2022
A process mining- deep learning approach to predict survival in a cohort of hospitalized COVID-19 patients.
Article in BMC medical informatics and decision making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Predictive Performance of Machine Learning Models for Heart Failure Readmission: A Systematic Review.Biomedicines · 2025Review
- Using a Neural Network Architecture for the Prediction of Neurologic Outcome for Out-of-Hospital Cardiac Arrests Using Hospital Level Variables and Novel Physiologic Markers.Bioengineering (Basel, Switzerland) · 2025Article
- Toward value-based care using cost mining: cost aggregation and visualization across the entire colorectal cancer patient pathway.BMC medical research methodology · 2024Article
- Prediction of 30-day mortality for ICU patients with Sepsis-3.BMC medical informatics and decision making · 2024Article
- Comprehensive analysis of clinical data for COVID-19 outcome estimation with machine learning models.Biomedical signal processing and control · 2023Article
- Lessons learned: Development of COVID-19 clinical staging models at a large urban research institution.Journal of clinical and translational science · 2023Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
backgroundVarious 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.
methodsThe 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.
resultsThe 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.
conclusionsOur 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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