Comparison between logistic regression and neural networks to predict death in patients with suspected sepsis in the emergency room

ABSTRACT: Introduction Neural networks are new methodological tools based on nonlinear models. They appear to be better at prediction and classification in biological systems than do traditional strategies such as logistic regression. This paper provides a practical example that contrasts both approaches within the setting of suspected sepsis in the emergency room. Methods The study population comprised patients with suspected bacterial infection as their main diagnosis for admission to the emergency room at two University-based hospitals. Mortality within the first 28 days from admission was predicted using logistic regression with the following variables: age, immunosuppressive systemic disease, general systemic disease, Shock Index, temperature, respiratory rate, Glasgow Coma Scale score, leucocyte counts, platelet counts and creatinine. Also, with the same input and output variables, a probabilistic neural network was trained with an adaptive genetic algorithm. The network had three neurone layers: 10 neurones in the input layer, 368 in the hidden layer and two in the output layer. Calibration was measured using the Hosmer-Lemeshow goodness-of-fit test and discrimination was determined using receiver operating characteristic curves. Results A total of 533 patients were recruited and overall 28-day mortality was 19%. The factors chosen by logistic regression (with their score in parentheses) were as follows: immunosuppressive systemic disease or general systemic disease (2), respiratory rate 24–33 breaths/min (1), respiratory rate ≥ 34 breaths/min (3), Glasgow Come Scale score ≤12 (3), Shock Index ≥ 1.5 (2) and temperature

• Autores: Jaimes Barragán, Fabián Alberto; Farbiarz, Jorge; Álvarez Castro, Diego Fernando; Martínez, Carlos Eli
• Tipo de recurso: Article of investigation
• Fecha de publicación: 2005
• Institución: Universidad de Antioquia
• Repositorio: Repositorio UdeA
• Idioma: eng
• OAI Identifier: oai:bibliotecadigital.udea.edu.co:10495/31668
• Acceso en línea: https://hdl.handle.net/10495/31668
• Palabra clave: Escala de Coma de Glasgow; Glasgow Coma Scale; Unidades de Cuidados Intensivos; Intensive Care Units; Tiempo de Internación; Length of Stay; Modelos Logísticos; Logistic Models; Estudios Longitudinales; Longitudinal Studies; Redes Neurales de la Computación; Neural Networks, Computer; Curva ROC; ROC Curve; Sepsis - mortalidad; Sepsis - mortality
• Rights: openAccess
• License: https://creativecommons.org/licenses/by/4.0/