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Authors: Michael A. Pratt and Henry Chu

Affiliation: University of Louisiana at Lafayette, United States

ISBN: 978-989-758-276-9

Keyword(s): Predictive Analytics, Health System Analytics, Classifiers, Support Vector Machine, Random Forest.

Related Ontology Subjects/Areas/Topics: Applications ; Classification ; Economics, Business and Forecasting Applications ; Pattern Recognition ; Theory and Methods

Abstract: To make healthcare more cost effective, the current trend in the U.S. is towards a hospital value-based purchasing program. In this program, a hospital’s performance is measured in the safety, patient experience of care, clinical care, and efficiency and cost reduction domains. We investigate the efficacy of predicting the safety measures using the patient experience of care measures. We compare four classifiers in the prediction tasks and concluded that random forest and support vector machine provided the best performance.

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Paper citation in several formats:
Pratt, M.; Pratt, M. and Chu, H. (2018). Predicting Hospital Safety Measures using Patient Experience of Care Responses.In Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-276-9, pages 371-378. DOI: 10.5220/0006588403710378

@conference{icpram18,
author={Michael A. Pratt. and Michael A. Pratt. and Henry Chu.},
title={Predicting Hospital Safety Measures using Patient Experience of Care Responses},
booktitle={Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2018},
pages={371-378},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006588403710378},
isbn={978-989-758-276-9},
}

TY - CONF

JO - Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Predicting Hospital Safety Measures using Patient Experience of Care Responses
SN - 978-989-758-276-9
AU - Pratt, M.
AU - Pratt, M.
AU - Chu, H.
PY - 2018
SP - 371
EP - 378
DO - 10.5220/0006588403710378

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