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

重症监护室 排名(信息检索) 计算机科学 人工智能 机器学习 特征(语言学) 接收机工作特性 贝叶斯网络 医学 重症监护医学 语言学 哲学
作者
José A. González-Nóvoa,Silvia Campanioni,Laura Busto,José Fariña,Juan Rodríguez-Andina,Dolores Vila,Andrés Íñiguez,César Veiga
出处
期刊:International Journal of Environmental Research and Public Health [Multidisciplinary Digital Publishing Institute]
卷期号:20 (4): 3455-3455 被引量:4
标识
DOI:10.3390/ijerph20043455
摘要

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.

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