Title: Real-time estimation of hospital discharge using fuzzy radial basis function network and electronic health record data

Authors: Ahmed Belderrar; Abdeldjebar Hazzab

Addresses: Laboratoire du Commande, Analyse et Optimisation des Systèmes Electro-énergétiques, Université TAHRI Mohamed de Béchar, BP 417 Route de Kenadsa, Béchar, 08000, Algeria ' Laboratoire du Commande, Analyse et Optimisation des Systèmes Electro-énergétiques, Université TAHRI Mohamed de Béchar, BP 417 Route de Kenadsa, Béchar, 08000, Algeria

Abstract: Hospital resources are scarce and should be properly distributed and justified. Information about how long patients stays in critical intensive care units can provide significant benefits to hospital management resources and optimal admission planning. In this paper, we propose an approach for estimating intensive care unit length of stay using fuzzy radial basis function neural network model. The predictive performance of the model is compared to others using data collected over 13,587 admissions and 54 predictive factors from five critical units with discharges between 2001 and 2012. The proposed model compared to others demonstrated higher accuracy and better estimations. The three most influential factors in predicting length of stay at the early stage of pre-admission were demographic characteristics, admission type, and the first location within the hospital prior to critical unit admission. We have found about 63% of patients with multiple chronic conditions, stayed significantly longer in hospital. Enabling the proposed prediction model in clinical decision support system may serve as reference tools for communicating with patients and hospital managers.

Keywords: data mining; hospital administration; length of stay; machine learning; prediction model.

DOI: 10.1504/IJMEI.2021.111870

International Journal of Medical Engineering and Informatics, 2021 Vol.13 No.1, pp.75 - 83

Received: 13 Jun 2018
Accepted: 24 Feb 2019
Published online: 18 Dec 2020 *

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