RT Journal Article
T1 Improving intensive care unit early readmission prediction using optimized and explainable machine learning
A1 González Nóvoa, José A.
A1 Campanioni, Silvia
A1 Busto, Laura
A1 Fariña Rodriguez, José
A1 Rodriguez Andina, Juan Jose
A1 Vila, Dolores
A1 Íñiguez, Andrés
A1 Veiga, César
K1 1203.04 Inteligencia Artificial
K1 1203.20 Sistemas de Control Medico
AB 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.
PB International Journal of Environmental Research and Public Health
SN 16604601
YR 2023
FD 2023-02-16
LK http://hdl.handle.net/11093/4663
UL http://hdl.handle.net/11093/4663
LA eng
NO International Journal of Environmental Research and Public Health, 20(4): 3455 (2023)
NO Agencia Gallega de Innovación | Ref. IN845D-2020/29
DS Investigo
RD 05-dic-2023