Prediction of Healthcare Associated Infections in an Intensive Care Unit Using Machine Learning and Big Data Tools

被引：9

作者：

Revuelta-Zamorano, Paz ^{[1
]}

Sanchez, Alberto ^{[2
]}

Luis Rojo-Alvarez, Jose ^{[1
]}

Alvarez-Rodriguez, Joaquin ^{[3
]}

Ramos-Lopez, Javier ^{[1
]}

Soguero-Ruiz, Cristina ^{[1
]}

机构：

[1] Rey Juan Carlos Univ, Signal Theory & Commun Telemat & Comp, Madrid, Spain

[2] Rey Juan Carlos Univ, Dept Comp Sci & Stat, Madrid, Spain

[3] Univ Hosp Fuenlabrada, Intens Care Unit, Madrid, Spain

来源：

XIV MEDITERRANEAN CONFERENCE ON MEDICAL AND BIOLOGICAL ENGINEERING AND COMPUTING 2016 | 2016年 / 57卷

关键词：

Healthcare Associated Infections; Intensive Unit Care; Risk Factors; Machine Learning; Big Data;

D O I：

10.1007/978-3-319-32703-7_162

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Healthcare associated infections (HAIS) can be acquired by patients during their stay in a hospital. HAIS are very endemic, causing a huge burden for the patients and for the health care system. We propose a machine learning approach to predict HAIS in an intensive care unit (ICU), combining heterogeneous data from longitudinal electronic health records and from microbiology laboratory. A NoSQL database, mongoDB, was developed to consider a big data environment. Results show that the fusion of these heterogeneous data sources provides 82% accuracy when a random forest algorithm was considered. In this study, the age, the length of stay, the bed where the patient stayed, and the admission month, are the most relevant risk factors to predict HAIS in the ICU.

引用

页码：834 / 839

页数：6

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