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Authors: Thomas Hartvigsen 1 ; Cansu Sen 1 ; Sarah Brownell 2 ; Erin Teeple 1 ; Xiangnan Kong 1 and Elke Rundensteiner 1

Affiliations: 1 Worcester Polytechnic Institute, United States ; 2 Simmons College, United States

Keyword(s): MRSA, Healthcare-associated Infections, Risk Stratification, Machine Learning, Electronic Health Records.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Data Mining ; Databases and Information Systems Integration ; Electronic Health Records and Standards ; Enterprise Information Systems ; Health Information Systems ; Pattern Recognition and Machine Learning ; Physiological Modeling ; Sensor Networks ; Signal Processing ; Soft Computing

Abstract: Despite eradication efforts, Methicillin-resistant Staphylococcus aureus (MRSA) remains a common cause of serious hospital-acquired infections (HAI) in the United States. Electronic Health Record (EHR) systems capture MRSA infection events along with detailed patient information preceding diagnosis. In this work, we design and apply machine learning methods to support early recognition of MRSA infection by estimating risk at several time points during hospitalization. We use EHR data including on-admission and throughout-stay patient information. On-admission features capture clinical and non-clinical information while throughout-stay features include vital signs, medications, laboratory studies, and other clinical assessments. We evaluate prediction accuracy achieved by core Machine Learning methods, namely Logistic Regression, Support Vector Machine, and Random Forest classifiers, when mining these different types of EHR features to detect patterns predictive of MRSA infec tion. We evaluate classification performance using MIMIC III – a critical care data set comprised of 12 years of patient records from the Beth Israel Deaconess Medical Center Intensive Care Unit in Boston, MA. Our methods can achieve near-perfect MRSA prediction accuracies one day before documented clinical diagnosis. Also, they perform well for early MRSA prediction many days in advance of diagnosis. These findings underscore the potential clinical applicability of machine learning techniques. (More)

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Paper citation in several formats:
Hartvigsen, T.; Sen, C.; Brownell, S.; Teeple, E.; Kong, X. and Rundensteiner, E. (2018). Early Prediction of MRSA Infections using Electronic Health Records. In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF, ISBN 978-989-758-281-3; ISSN 2184-4305, pages 156-167. DOI: 10.5220/0006599601560167

@conference{healthinf18,
author={Thomas Hartvigsen. and Cansu Sen. and Sarah Brownell. and Erin Teeple. and Xiangnan Kong. and Elke Rundensteiner.},
title={Early Prediction of MRSA Infections using Electronic Health Records},
booktitle={Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF,},
year={2018},
pages={156-167},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006599601560167},
isbn={978-989-758-281-3},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF,
TI - Early Prediction of MRSA Infections using Electronic Health Records
SN - 978-989-758-281-3
IS - 2184-4305
AU - Hartvigsen, T.
AU - Sen, C.
AU - Brownell, S.
AU - Teeple, E.
AU - Kong, X.
AU - Rundensteiner, E.
PY - 2018
SP - 156
EP - 167
DO - 10.5220/0006599601560167

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