Title: Predicting hospital length of stay using neural networks

Authors: Thanos Gentimis; Ala' J. Alnaser; Alex Durante; Kyle Cook; Robert Steele

Addresses: Department of Experimental Statistics, Louisiana State University, Baton Rouge, LA, USA ' Florida Polytechnic University, Lakeland, Florida, USA ' Florida Polytechnic University, Lakeland, Florida, USA ' Florida Polytechnic University, Lakeland, Florida, USA ' Florida Polytechnic University, Lakeland, Florida, USA

Abstract: Accurate prediction of hospital length of stay can provide benefits for hospital resource planning and quality-of-care. We describe the utilisation of neural networks for predicting the length of hospital stay for patients with various diagnoses based on selected administrative and clinical attributes. An all-condition neural network, that can be applied to all patients and not limited to a specific diagnosis, is trained to predict whether patient stay will be long or short in terms of the median length of stay as the cut-off between long and short, and predicted at the time the patient leaves the intensive care unit. In addition, neural networks are trained to predict whether patients of 14 specific common primary diagnoses will have a long or short stay, as defined as greater than or less than or equal to the median length of stay for that particular condition. Our dataset is drawn from the MIMIC III database. Our prediction accuracy is approximately 80% for the all-condition neural network and the neural networks for specific conditions generally demonstrated higher accuracy and all clearly out-performed any linear model.

Keywords: length of stay; health analytics; neural networks; MIMIC III.

DOI: 10.1504/IJBDI.2019.100893

International Journal of Big Data Intelligence, 2019 Vol.6 No.3/4, pp.297 - 306

Received: 21 Dec 2017
Accepted: 27 Jun 2018

Published online: 04 Jun 2019 *

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