TY - JOUR
T1 - Predictive risk models for COVID-19 patients using the multi-thresholding meta-algorithm
AU - Delgado, Rosario
AU - Fernández-Peláez, Francisco
AU - Pallarés, Natàlia
AU - Diaz-Brito, Vicens
AU - Izquierdo, Elisenda
AU - Oriol, Isabel
AU - Simonetti, Antonella
AU - Tebé, Cristian
AU - Videla, Sebastià
AU - Carratalà, Jordi
N1 - Publisher Copyright:
© The Author(s) 2024.
PY - 2024/11/18
Y1 - 2024/11/18
N2 - This study aims to develop a Machine Learning model to assess the risks faced by COVID-19 patients in a hospital setting, focusing specifically on predicting the complications leading to Intensive Care Unit (ICU) admission or mortality, which are minority classes compared to the majority class of discharged patients. We operate within a multiclass framework comprising three distinct classes, and address the challenge of dataset imbalance, a common source of model bias. To effectively manage this, we introduce the Multi-Thresholding meta-algorithm (MTh), an innovative output-level methodology that extends traditional thresholding from binary to multiclass classification. This methodology dynamically adjusts class probabilities using misclassification costs, making it highly effective in imbalanced datasets. Our approach is further enhanced by integrating the simplicity, transparency, and effectiveness of Bayesian networks to create a robust predictive model. Using patient admission data, the model accurately identifies key risk and protective factors for COVID-19 outcomes. Our findings indicate that certain patient characteristics, such as high Charlson Index and pre-existing conditions, significantly influence the risk of ICU admission and mortality. Moreover, we introduce an explanatory model that elucidates the interrelationships among these factors, demonstrating the influence of therapeutic limits on the overall risk assessment of COVID-19 patients. Overall, our research provides a significant contribution to the field of Machine Learning by offering a novel solution for multiclass classification in the context of imbalanced datasets. This model not only enhances predictive accuracy but also supports critical decision-making processes in healthcare, potentially improving patient outcomes and optimizing clinical resource allocation.
AB - This study aims to develop a Machine Learning model to assess the risks faced by COVID-19 patients in a hospital setting, focusing specifically on predicting the complications leading to Intensive Care Unit (ICU) admission or mortality, which are minority classes compared to the majority class of discharged patients. We operate within a multiclass framework comprising three distinct classes, and address the challenge of dataset imbalance, a common source of model bias. To effectively manage this, we introduce the Multi-Thresholding meta-algorithm (MTh), an innovative output-level methodology that extends traditional thresholding from binary to multiclass classification. This methodology dynamically adjusts class probabilities using misclassification costs, making it highly effective in imbalanced datasets. Our approach is further enhanced by integrating the simplicity, transparency, and effectiveness of Bayesian networks to create a robust predictive model. Using patient admission data, the model accurately identifies key risk and protective factors for COVID-19 outcomes. Our findings indicate that certain patient characteristics, such as high Charlson Index and pre-existing conditions, significantly influence the risk of ICU admission and mortality. Moreover, we introduce an explanatory model that elucidates the interrelationships among these factors, demonstrating the influence of therapeutic limits on the overall risk assessment of COVID-19 patients. Overall, our research provides a significant contribution to the field of Machine Learning by offering a novel solution for multiclass classification in the context of imbalanced datasets. This model not only enhances predictive accuracy but also supports critical decision-making processes in healthcare, potentially improving patient outcomes and optimizing clinical resource allocation.
KW - Bayesian Networks
KW - Cost-sensitive Machine Learning modelling
KW - COVID-19 patient risks assessment
KW - Healthcare decision-making
KW - Multiclass classification thresholding
KW - Intensive Care Units
KW - SARS-CoV-2/isolation & purification
KW - Humans
KW - Middle Aged
KW - Risk Factors
KW - Male
KW - Hospitalization
KW - Machine Learning
KW - Algorithms
KW - COVID-19/epidemiology
KW - Bayes Theorem
KW - Female
KW - Aged
KW - Risk Assessment/methods
UR - http://www.scopus.com/inward/record.url?scp=85209544745&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/bd401089-80ea-311c-85bd-b3fcc1cfe610/
UR - https://portalrecerca.uab.cat/en/publications/e12cfa08-12ef-4b40-ae2e-22e2df31a151
U2 - 10.1038/s41598-024-77386-7
DO - 10.1038/s41598-024-77386-7
M3 - Article
C2 - 39557887
AN - SCOPUS:85209544745
SN - 2045-2322
VL - 14
JO - SCIENTIFIC REPORTS
JF - SCIENTIFIC REPORTS
IS - 1
M1 - 28453
ER -