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Improving Intensive Care Unit Early Readmission Prediction Using Optimized and Explainable Machine Learning
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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9960143/ https://www.ncbi.nlm.nih.gov/pubmed/36834150 http://dx.doi.org/10.3390/ijerph20043455 |
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author | González-Nóvoa, José A. Campanioni, Silvia Busto, Laura Fariña, José Rodríguez-Andina, Juan J. Vila, Dolores Íñiguez, Andrés Veiga, César |
author_facet | González-Nóvoa, José A. Campanioni, Silvia Busto, Laura Fariña, José Rodríguez-Andina, Juan J. Vila, Dolores Íñiguez, Andrés Veiga, César |
author_sort | González-Nóvoa, José A. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9960143 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99601432023-02-26 Improving Intensive Care Unit Early Readmission Prediction Using Optimized and Explainable Machine Learning González-Nóvoa, José A. Campanioni, Silvia Busto, Laura Fariña, José Rodríguez-Andina, Juan J. Vila, Dolores Íñiguez, Andrés Veiga, César Int J Environ Res Public Health Article 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. MDPI 2023-02-16 /pmc/articles/PMC9960143/ /pubmed/36834150 http://dx.doi.org/10.3390/ijerph20043455 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article González-Nóvoa, José A. Campanioni, Silvia Busto, Laura Fariña, José Rodríguez-Andina, Juan J. Vila, Dolores Íñiguez, Andrés Veiga, César Improving Intensive Care Unit Early Readmission Prediction Using Optimized and Explainable Machine Learning |
title | Improving Intensive Care Unit Early Readmission Prediction Using Optimized and Explainable Machine Learning |
title_full | Improving Intensive Care Unit Early Readmission Prediction Using Optimized and Explainable Machine Learning |
title_fullStr | Improving Intensive Care Unit Early Readmission Prediction Using Optimized and Explainable Machine Learning |
title_full_unstemmed | Improving Intensive Care Unit Early Readmission Prediction Using Optimized and Explainable Machine Learning |
title_short | Improving Intensive Care Unit Early Readmission Prediction Using Optimized and Explainable Machine Learning |
title_sort | improving intensive care unit early readmission prediction using optimized and explainable machine learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9960143/ https://www.ncbi.nlm.nih.gov/pubmed/36834150 http://dx.doi.org/10.3390/ijerph20043455 |
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