Machine Learning Models for Early Prediction of Sepsis on Large Healthcare Datasets

Electronics

Isis Bonet¹  Javier Enrique Camacho-Cogollo¹  Bladimir Gil²  Ernesto Iadanza³ 

[1] Biomedical Engineering Department, EIA University, Calle 23 AA Sur Nro. 5-200, Km 2 + 200 Via Al Aeropuerto, Envigado 55428, Colombia;Clínica Las Américas, Diagonal 75B N. 2A-80/140, Medellin 50025, Colombia;Department of Medical Biotechnologies, University of Siena, Via Aldo Moro 2, 53100 Siena, Italy;

关键词: artificial intelligence;    machine learning;    CDSS;    ICU;    sepsis;   

DOI  :  10.3390/electronics11091507

来源: DOAJ

【 摘 要 】

Sepsis is a highly lethal syndrome with heterogeneous clinical manifestation that can be hard to identify and treat. Early diagnosis and appropriate treatment are critical to reduce mortality and promote survival in suspected cases and improve the outcomes. Several screening prediction systems have been proposed for evaluating the early detection of patient deterioration, but the efficacy is still limited at individual level. The increasing amount and the versatility of healthcare data suggest implementing machine learning techniques to develop models for predicting sepsis. This work presents an experimental study of some machine-learning-based models for sepsis prediction considering vital signs, laboratory test results, and demographics using Medical Information Mart for Intensive Care III (MIMIC-III) (v1.4), a publicly available dataset. The experimental results demonstrate an overall higher performance of machine learning models over the commonly used Sequential Organ Failure Assessment (SOFA) and Quick SOFA (qSOFA) scoring systems at the time of sepsis onset.

【 授权许可】

Unknown