Joint clustering of hospitals based on their adminission behavior for different diseases using network of networks data model
type: CreativeWork
description: Healthcare analytics is a rapidly growing industry where health organizations integrate data-driven insights into their clinical and operational decisions. One such insight involves clustering hospitals based on similarities in their monthly admission behavior, which can help ensure that the supply of healthcare services meets the population’s needs. Since diagnosis plays a crucial role in understanding variations in hospital admissions, clustering hospitals based on their admission behavior for specific diseases provides a more precise approach than clustering them based on their overall admission patterns across all diagnoses. This study aims to jointly cluster hospitals based on their admission behavior for different diseases. To achieve this, the two-layer Network of Networks (NoN) data model and the Network of Networks Clustering (NoNClus) method are utilized. The NoN data model is constructed using more than 7 million discharge records extracted from the California State Inpatient Database (2009–2011). It represents hospital admission similarities for different diseases as multi-domain disease-specific hospital networks at the bottom layer. Each of these networks is then represented as a node within a disease supernetwork at the top layer. The NoNClus method enables multiple underlying clustering structures across different networks by modeling the clustering structure in the disease supernetwork to guide the clustering of disease-specific hospital networks. The study thoroughly investigates the effect of various disease supernetworks in guiding joint clustering. The first disease network used to guide hospital clustering was derived from the Human Disease Symptoms Network (HDSN), based on the premise that disease symptoms play a crucial role in clinical diagnosis and, consequently, in hospital admission decisions \cite{Albarakati2017}. The HDSN was constructed using an extensive medical bibliographic literature database. The second disease network combines li…
abstract: Healthcare analytics is a rapidly growing industry where health organizations integrate data-driven insights into their clinical and operational decisions. One such insight involves clustering hospitals based on similarities in their monthly admission behavior, which can help ensure that the supply of healthcare services meets the population’s needs. Since diagnosis plays a crucial role in understanding variations in hospital admissions, clustering hospitals based on their admission behavior for specific diseases provides a more precise approach than clustering them based on their overall admission patterns across all diagnoses. This study aims to jointly cluster hospitals based on their admission behavior for different diseases. To achieve this, the two-layer Network of Networks (NoN) data model and the Network of Networks Clustering (NoNClus) method are utilized. The NoN data model is constructed using more than 7 million discharge records extracted from the California State Inpatient Database (2009–2011). It represents hospital admission similarities for different diseases as multi-domain disease-specific hospital networks at the bottom layer. Each of these networks is then represented as a node within a disease supernetwork at the top layer. The NoNClus method enables multiple underlying clustering structures across different networks by modeling the clustering structure in the disease supernetwork to guide the clustering of disease-specific hospital networks. The study thoroughly investigates the effect of various disease supernetworks in guiding joint clustering. The first disease network used to guide hospital clustering was derived from the Human Disease Symptoms Network (HDSN), based on the premise that disease symptoms play a crucial role in clinical diagnosis and, consequently, in hospital admission decisions \cite{Albarakati2017}. The HDSN was constructed using an extensive medical bibliographic literature database. The second disease network combines li…
author: Albarakati, Nouf
language: eng
keywords: Computer science, Computer and information science
license: http://rightsstatements.org/vocab/InC/1.0/
date published: 2025-12
date modified: 2026-07-27T21:20:39Z
provider: Temple University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Temple University. Libraries