Development and validation of an interpretable 3 day intensive care unit readmission prediction model using explainable boosting machines

Stefan Hegselmann; Christian Ertmer; Thomas Volkert; Antje Gottschalk; Martin Dugas; Julian Varghese

doi:10.3389/fmed.2022.960296

Development and validation of an interpretable 3 day intensive care unit readmission prediction model using explainable boosting machines

Front Med (Lausanne). 2022 Aug 23:9:960296. doi: 10.3389/fmed.2022.960296. eCollection 2022.

Authors

Stefan Hegselmann¹, Christian Ertmer², Thomas Volkert², Antje Gottschalk², Martin Dugas³, Julian Varghese¹

Affiliations

¹ Institute of Medical Informatics, University of Münster, Münster, Germany.
² Department of Anesthesiology, Intensive Care and Pain Medicine, University Hospital Münster, Münster, Germany.
³ Institute of Medical Informatics, Heidelberg University Hospital, Heidelberg, Germany.

Abstract

Background: Intensive care unit (ICU) readmissions are associated with mortality and poor outcomes. To improve discharge decisions, machine learning (ML) could help to identify patients at risk of ICU readmission. However, as many models are black boxes, dangerous properties may remain unnoticed. Widely used post hoc explanation methods also have inherent limitations. Few studies are evaluating inherently interpretable ML models for health care and involve clinicians in inspecting the trained model.

Methods: An inherently interpretable model for the prediction of 3 day ICU readmission was developed. We used explainable boosting machines that learn modular risk functions and which have already been shown to be suitable for the health care domain. We created a retrospective cohort of 15,589 ICU stays and 169 variables collected between 2006 and 2019 from the University Hospital Münster. A team of physicians inspected the model, checked the plausibility of each risk function, and removed problematic ones. We collected qualitative feedback during this process and analyzed the reasons for removing risk functions. The performance of the final explainable boosting machine was compared with a validated clinical score and three commonly used ML models. External validation was performed on the widely used Medical Information Mart for Intensive Care version IV database.

Results: The developed explainable boosting machine used 67 features and showed an area under the precision-recall curve of 0.119 ± 0.020 and an area under the receiver operating characteristic curve of 0.680 ± 0.025. It performed on par with state-of-the-art gradient boosting machines (0.123 ± 0.016, 0.665 ± 0.036) and outperformed the Simplified Acute Physiology Score II (0.084 ± 0.025, 0.607 ± 0.019), logistic regression (0.092 ± 0.026, 0.587 ± 0.016), and recurrent neural networks (0.095 ± 0.008, 0.594 ± 0.027). External validation confirmed that explainable boosting machines (0.221 ± 0.023, 0.760 ± 0.010) performed similarly to gradient boosting machines (0.232 ± 0.029, 0.772 ± 0.018). Evaluation of the model inspection showed that explainable boosting machines can be useful to detect and remove problematic risk functions.

Conclusions: We developed an inherently interpretable ML model for 3 day ICU readmission prediction that reached the state-of-the-art performance of black box models. Our results suggest that for low- to medium-dimensional datasets that are common in health care, it is feasible to develop ML models that allow a high level of human control without sacrificing performance.

Keywords: artificial intelligence; doctor-in-the-loop; explainable AI; human evaluation; intensive care unit; interpretable machine learning; machine learning; readmission.