Improving Intensive Care Unit Early Readmission Prediction Using Optimized and Explainable Machine Learning

José A. González-Nóvoa (), Silvia Campanioni, Laura Busto, José Fariña, Juan J. Rodríguez-Andina, Dolores Vila, Andrés Íñiguez and César Veiga
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José A. González-Nóvoa: Galicia Sur Health Research Institute (IIS Galicia Sur), Álvaro Cunqueiro Hospital, 36310 Vigo, Spain
Silvia Campanioni: Galicia Sur Health Research Institute (IIS Galicia Sur), Álvaro Cunqueiro Hospital, 36310 Vigo, Spain
Laura Busto: Galicia Sur Health Research Institute (IIS Galicia Sur), Álvaro Cunqueiro Hospital, 36310 Vigo, Spain
José Fariña: Department of Electronic Technology, University of Vigo, 36310 Vigo, Spain
Juan J. Rodríguez-Andina: Department of Electronic Technology, University of Vigo, 36310 Vigo, Spain
Dolores Vila: Intensive Care Unit Department, Complexo Hospitalario Universitario de Vigo (SERGAS), Álvaro Cunqueiro Hospital, 36213 Vigo, Spain
Andrés Íñiguez: Cardiology Department, Complexo Hospitalario Universitario de Vigo (SERGAS), Álvaro Cunqueiro Hospital, 36213 Vigo, Spain
César Veiga: Galicia Sur Health Research Institute (IIS Galicia Sur), Álvaro Cunqueiro Hospital, 36310 Vigo, Spain

IJERPH, 2023, vol. 20, issue 4, 1-14

Abstract: It is of great interest to develop and introduce new techniques to automatically and efficiently analyze the enormous amount of data generated in today’s hospitals, using state-of-the-art artificial intelligence methods. Patients readmitted to the ICU in the same hospital stay have a higher risk of mortality, morbidity, longer length of stay, and increased cost. The methodology proposed to predict ICU readmission could improve the patients’ care. The objective of this work is to explore and evaluate the potential improvement of existing models for predicting early ICU patient readmission by using optimized artificial intelligence algorithms and explainability techniques. In this work, XGBoost is used as a predictor model, combined with Bayesian techniques to optimize it. The results obtained predicted early ICU readmission (AUROC of 0.92 ± 0.03) improves state-of-the-art consulted works (whose AUROC oscillate between 0.66 and 0.78). Moreover, we explain the internal functioning of the model by using Shapley Additive Explanation-based techniques, allowing us to understand the model internal performance and to obtain useful information, as patient-specific information, the thresholds from which a feature begins to be critical for a certain group of patients, and the feature importance ranking.

Keywords: artificial intelligence; automated machine learning; Bayesian optimization; explainable machine learning; readmission; intensive care unit; machine learning; MIMIC; SHAP; XGBoost (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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