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Identification of Disease-Disease Network Communities in Subpopulations of Patients with Prostate Cancer
Conference proceeding

Identification of Disease-Disease Network Communities in Subpopulations of Patients with Prostate Cancer

Ali Jazayeri, Niusha Jafari, Nikita Nikita, Christopher C Yang and Grace Lu-Yao
2021 IEEE 9th International Conference on Healthcare Informatics (ICHI)
Aug 2021

Abstract

Community Detection Databases Disease-Disease Networks Insurance Medical services Pathology Predictive models Prostate cancer Visualization
Prostate cancer (PCa) is ranked as one of the most common cancer diagnoses among men worldwide. Different research studies show that the current diagnosis and treatment strategies have improved the health condition of patients with PCa. Nonetheless, the number of new cases and the morbidity associated with PCa remain high. In this study, using the Medicare claims of patients identified from the SEER-Medicare database, we analyzed the disease-disease interactions in patients at different stages of PCa. Also, we implemented community detection to identify common co-occurring diseases in different subpopulations, identified by PCa stages. The similarity analysis of complication subgroups was performed to identify the most distinct complication subgroups among subpopulations. The results of the study show that the level of experiencing cooccurring diseases is different among subpopulations. Identifying distinct disease-disease interactions can inform the prediction of hospitalization rate and frequency and mortality rate among different subpopulations and enhance our understanding of the pathological correlations among diseases.

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Collaboration types
Domestic collaboration
Web of Science research areas
Computer Science, Artificial Intelligence
Computer Science, Information Systems
Health Care Sciences & Services
Medical Informatics
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