Summarizing multiple networks based on their underlying clustering structure to guide joint clustering of hospitals admissions

Abstract

Healthcare services planning and regulation involve finding patterns in hospitals admission to detect their needs in a timely manner. Admission patterns for certain diseases are more precise than a general pattern including all diseases. Towards the objective of clustering hospitals based on their monthly admission behavior for different diseases, this study investigates the similarity among multiple disease-specific hospital networks to guide a joint clustering of hospitals. In this paper, the disease super network is generated from health records data using graph matching instead of relying on biomedical literature that is used in the previous work. The health records-based disease network is constructed using more than 7 million discharge records that are extracted from the California State Inpatient Database between 2009 and 2011. Comparison of the disease network results obtained using health records of different years shows consistency in clustering structure despite temporal changes in admission data. We show that the joint clustering guided by the health records-based similarity improves clustering group homogeneity measures as compared to the clustering guided by literature-based similarity (average homogeneity 53% vs 41%, respectively). The code used to conduct this work is available at https://github.com/Nouf-Barakati/JointCLusteringofHospitals.