Title: A discrete Bayesian network for analysing hospital discharge data

Authors: Jinhang Jiang; Karthik Srinivasan

Addresses: Walmart Inc., 702 SW 8th St. Bentonville, AR, 72716, USA ' School of Business, University of Kansas, 1654 Naismith Dr Lawrence, KS, 66045, USA

Abstract: Exploratory research requires models that can explain the underlying phenomena of interest in new research areas. We present the design and application of discrete Bayesian networks (DBN) for knowledge discovery in a hospital discharge dataset. For the learning phase of the network, the automated learning methods are preceded by customising the initial network. Structural learning is done using three state-of-the-art algorithms and is inter-validated. A new method is suggested for drawing selective inferences from the posterior conditional probability tables (CPT). As an illustration, functional inferences are drawn on length of stay and treatment charges for three disease groups using the developed method. Our analysis shows that for longer hospital stays, hospital visits involving mental disorders cost less than visits with other types of health conditions. This study contributes to data science research by demonstrating the application of Bayesian networks, evaluating different structure learning methods for given contexts, and developing a measure for selective inference using the CPT of the network.

Keywords: Bayes-net; Bayesian belief networks; exploratory analysis; structural learning; hospital discharge data; selective inference.

DOI: 10.1504/IJDS.2024.135946

International Journal of Data Science, 2024 Vol.9 No.1, pp.1 - 18

Received: 23 Sep 2022
Accepted: 01 Jun 2023

Published online: 10 Jan 2024 *

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