Comorbidity network analysis using graphical models for electronic health records

Frontiers in Big Data

Big Data

Gen Zhu¹  Bo Zhao¹  Xi Luo¹  Sarah Huepenbecker²  Kayo Fujimoto³  Suja S. Rajan⁴ 

[1] Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center, Houston, TX, United States;Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, United States;Department of Health Promotion and Behavioral Sciences, School of Public Health, The University of Texas Health Science Center, Houston, TX, United States;Department of Management, Policy and Community Health, School of Public Health, The University of Texas Health Science Center, Houston, TX, United States;

关键词: comorbidity network analysis;    graphic modeling method;    machine learning;    electronic health records;    critical care unit;   

DOI  :  10.3389/fdata.2023.846202

 received in 2021-12-30, accepted in 2023-07-25,  发布年份 2023

来源: Frontiers

【 摘 要 】

ImportanceThe comorbidity network represents multiple diseases and their relationships in a graph. Understanding comorbidity networks among critical care unit (CCU) patients can help doctors diagnose patients faster, minimize missed diagnoses, and potentially decrease morbidity and mortality.ObjectiveThe main objective of this study was to identify the comorbidity network among CCU patients using a novel application of a machine learning method (graphical modeling method). The second objective was to compare the machine learning method with a traditional pairwise method in simulation.MethodThis cross-sectional study used CCU patients' data from Medical Information Mart for the Intensive Care-3 (MIMIC-3) dataset, an electronic health record (EHR) of patients with CCU hospitalizations within Beth Israel Deaconess Hospital from 2001 to 2012. A machine learning method (graphical modeling method) was applied to identify the comorbidity network of 654 diagnosis categories among 46,511 patients.ResultsOut of the 654 diagnosis categories, the graphical modeling method identified a comorbidity network of 2,806 associations in 510 diagnosis categories. Two medical professionals reviewed the comorbidity network and confirmed that the associations were consistent with current medical understanding. Moreover, the strongest association in our network was between “poisoning by psychotropic agents” and “accidental poisoning by tranquilizers” (logOR 8.16), and the most connected diagnosis was “disorders of fluid, electrolyte, and acid–base balance” (63 associated diagnosis categories). Our method outperformed traditional pairwise comorbidity network methods in simulation studies. Some strongest associations between diagnosis categories were also identified, for example, “diagnoses of mitral and aortic valve” and “other rheumatic heart disease” (logOR: 5.15). Furthermore, our method identified diagnosis categories that were connected with most other diagnosis categories, for example, “disorders of fluid, electrolyte, and acid–base balance” was associated with 63 other diagnosis categories. Additionally, using a data-driven approach, our method partitioned the diagnosis categories into 14 modularity classes.Conclusion and relevanceOur graphical modeling method inferred a logical comorbidity network whose associations were consistent with current medical understanding and outperformed traditional network methods in simulation. Our comorbidity network method can potentially assist CCU doctors in diagnosing patients faster and minimizing missed diagnoses.

【 授权许可】

Unknown   
Copyright © 2023 Zhao, Huepenbecker, Zhu, Rajan, Fujimoto and Luo.