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- Title
Comparison of machine learning methods with logistic regression analysis in creating predictive models for risk of critical in-hospital events in COVID-19 patients on hospital admission.
- Authors
Sievering, Aaron W.; Wohlmuth, Peter; Geßler, Nele; Gunawardene, Melanie A.; Herrlinger, Klaus; Bein, Berthold; Arnold, Dirk; Bergmann, Martin; Nowak, Lorenz; Gloeckner, Christian; Koch, Ina; Bachmann, Martin; Herborn, Christoph U.; Stang, Axel
- Abstract
<bold>Background: </bold>Machine learning (ML) algorithms have been trained to early predict critical in-hospital events from COVID-19 using patient data at admission, but little is known on how their performance compares with each other and/or with statistical logistic regression (LR). This prospective multicentre cohort study compares the performance of a LR and five ML models on the contribution of influencing predictors and predictor-to-event relationships on prediction model´s performance.<bold>Methods: </bold>We used 25 baseline variables of 490 COVID-19 patients admitted to 8 hospitals in Germany (March-November 2020) to develop and validate (75/25 random-split) 3 linear (L1 and L2 penalty, elastic net [EN]) and 2 non-linear (support vector machine [SVM] with radial kernel, random forest [RF]) ML approaches for predicting critical events defined by intensive care unit transfer, invasive ventilation and/or death (composite end-point: 181 patients). Models were compared for performance (area-under-the-receiver-operating characteristic-curve [AUC], Brier score) and predictor importance (performance-loss metrics, partial-dependence profiles).<bold>Results: </bold>Models performed close with a small benefit for LR (utilizing restricted cubic splines for non-linearity) and RF (AUC means: 0.763-0.731 [RF-L1]); Brier scores: 0.184-0.197 [LR-L1]). Top ranked predictor variables (consistently highest importance: C-reactive protein) were largely identical across models, except creatinine, which exhibited marginal (L1, L2, EN, SVM) or high/non-linear effects (LR, RF) on events.<bold>Conclusions: </bold>Although the LR and ML models analysed showed no strong differences in performance and the most influencing predictors for COVID-19-related event prediction, our results indicate a predictive benefit from taking account for non-linear predictor-to-event relationships and effects. Future efforts should focus on leveraging data-driven ML technologies from static towards dynamic modelling solutions that continuously learn and adapt to changes in data environments during the evolving pandemic.<bold>Trial Registration Number: </bold>NCT04659187.
- Subjects
GERMANY; COVID-19; HOSPITAL admission &; discharge; PREDICTION models; HOSPITAL patients; INTENSIVE care units; INDEPENDENT variables; LOGISTIC regression analysis; MACHINE learning
- Publication
BMC Medical Informatics & Decision Making, 2022, Vol 22, Issue 1, p1
- ISSN
1472-6947
- Publication type
journal article
- DOI
10.1186/s12911-022-02057-4