Comparison of bio-inspired algorithms applied to the hospital mortality risk stratification

The construction of patient classification (or risk adjustment) systems allows comparison of the effectiveness and quality of hospitals and hospital services, providing useful information for management decision making and management of hospitals. Risk adjustment systems to stratify patients’ severity in a clinical outcome are generally constructed from care variables and using statistical techniques based on logistic regression (RL). The objective of this investigation is to compare the hospital mortality prediction capacity of an artificial neural network (RNA) with other methods already known.

• Autores: Silva, Jesús; Herazo-Beltrán, Yaneth; Marín-González, Freddy; Varela Izquierdo, Noel; Pineda, Omar; Palencia-Domínguez, Pablo; Vargas Mercado, Carlos; Marín González, Freddy
• Tipo de recurso: Article of journal
• Fecha de publicación: 2020
• Institución: Corporación Universidad de la Costa
• Repositorio: REDICUC - Repositorio CUC
• Idioma: eng
• OAI Identifier: oai:repositorio.cuc.edu.co:11323/7741
• Acceso en línea: https://hdl.handle.net/11323/7741; https://doi.org/10.1007/978-981-15-4875-8_16; https://repositorio.cuc.edu.co/
• Palabra clave: Hospital mortality; Risk stratification; Intensive care unit; Artificial neural networks; Bootstrap
• Rights: openAccess
• License: Attribution-NonCommercial-NoDerivatives 4.0 International