Clinical Performance Evaluation of a Machine Learning System for Predicting Hospital-Acquired Clostridium Difficile Infection

Erin Teeple, Thomas Hartvigsen, Cansu Sen, Kajal Claypool, Elke Rundensteiner, Elke Rundensteiner

2020

Abstract

Clostridium difficile infection (CDI) is a common and often serious hospital-acquired infection. The CDI Risk Estimation System (CREST) was developed to apply machine learning methods to predict a patient’s daily hospital-acquired CDI risk using information from the electronic health record (EHR). In recent years, several systems have been developed to predict patient health risks based on electronic medical record information. How to interpret the outputs of such systems and integrate them with healthcare work processes remains a challenge, however. In this paper, we explore the clinical interpretation of CDI Risk Scores assigned by the CREST framework for an L1-regularized Logistic Regression classifier trained using EHR data from the publicly available MIMIC-III Database. Predicted patient CDI risk is used to calculate classifier system output sensitivity, specificity, positive and negative predictive values, and diagnostic odds ratio using EHR data from five days and one day before diagnosis. We identify features which are strongly predictive of evolving infection by comparing coefficient weights for our trained models and consider system performance in the context of potential clinical applications.

Paper Citation

in Harvard Style

Teeple E., Hartvigsen T., Sen C., Claypool K. and Rundensteiner E. (2020). Clinical Performance Evaluation of a Machine Learning System for Predicting Hospital-Acquired Clostridium Difficile Infection. In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 5: HEALTHINF; ISBN 978-989-758-398-8, SciTePress, pages 656-663. DOI: 10.5220/0009157406560663

in Bibtex Style

@conference{healthinf20,
author={Erin Teeple and Thomas Hartvigsen and Cansu Sen and Kajal Claypool and Elke Rundensteiner},
title={Clinical Performance Evaluation of a Machine Learning System for Predicting Hospital-Acquired Clostridium Difficile Infection},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 5: HEALTHINF},
year={2020},
pages={656-663},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009157406560663},
isbn={978-989-758-398-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 5: HEALTHINF
TI - Clinical Performance Evaluation of a Machine Learning System for Predicting Hospital-Acquired Clostridium Difficile Infection
SN - 978-989-758-398-8
AU - Teeple E.
AU - Hartvigsen T.
AU - Sen C.
AU - Claypool K.
AU - Rundensteiner E.
PY - 2020
SP - 656
EP - 663
DO - 10.5220/0009157406560663
PB - SciTePress