[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128219-en":3,"doc-seo-128219-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128219,2336475104362,"Eden","https://ap-avatar.wpscdn.com/avatar/22000c4c46a41b752dd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786595829695023868",7,"Healthcare","Identifying New Risk Associations Between Chronic Physical Illness and Mental Health Disorders in China - Machine Learning Approach to a Retrospective Population Analysis","The study examines how chronic physical illnesses and mental health disorders influence each other, while addressing gaps in understanding how comorbidity patterns change over time. Using four machine learning models, the research analyzes retrospective population data to model complex mental–physical interactions and to characterize longitudinal trajectories of patients’ health journeys. It identifies five chronic illness categories linked with higher comorbidity risk and quantifies strongest associations, while also assessing how patterns vary by age and gender.","Please cite the Published Version  \nLiang, Lizhong , Liu, Tianci , Ollier, William , Peng, Yonghong , Lu, Yao  and Che, Chao  (2025) Identifying New Risk Associations Between Chronic Physical Illness and Mental Health Disorders in China: Machine Learning Approach to a Retrospective Population Analysis. JMIR AI, 4. e72599 ISSN 2817-1705  \nDOI: [https://doi.org/10.2196/72599](https://doi.org/10.2196/72599)  \nPublisher: JMIR Publications  \nVersion: Published Version  \nDownloaded from: [https://e-space.mmu.ac.uk/641213/](https://e-space.mmu.ac.uk/641213/)  \nUsage rights:  Creative Commons: Attribution 4 .0  \nAdditional Information: This is an open access article published in JMIR AI, by JMIR Publications.  \nData Access Statement: Due to ethical limitations and the potential risk of exposing patient privacy, the dataset used for this study has not been fully disclosed. The author can be contacted for any needs, and all relevant data can be provided upon request and after appropriate ethical review. The processed, anonymized data are provided with the article. The key algorithms have been described in the literature.  \nEnquiries:  \nIf you have questions about this document, contact [openresearch@mmu.ac.uk](openresearch@mmu.ac.uk. Please)[. Please](openresearch@mmu.ac.uk. Please) include the URL of the record in e-space. If you believe that your, or a third party’s rights have been compromised through this document please see our Take Down policy (available from [https://www.mmu.ac.uk/library/using-the-library/policies-and-guidelines](https://www.mmu.ac.uk/library/using-the-library/policies-and-guidelines))  \nJMIR AI Liang et al  \nOriginal Paper  \nIdentifying New Risk Associations Between Chronic Physical Illness and Mental Health Disorders in China: Machine Learning Approach to a Retrospective Population Analysis  \n\n| Lizhong Liang 1,2* , MD; Tianci Liu3* , BS; William Ollier4 , PhD; Yonghong Peng5 , PhD; Yao Lu 1§ , PhD; Chao Che3§ , PhD |\n| --- |\n| 1School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China 2Affiliated Hospital of Guangdong Medical College Hospital, Zhanjiang, China\u003Cbr>3Key Laboratory of Advanced Design and Intelligent Computing, Dalian University, Dalian, China 4Faculty of Science and Engineering, Manchester Metropolitan University, Manchester, United Kingdom 5Faculty of Science and Engineering, Anglia Ruskin University, Cambridge, United Kingdom\u003Cbr>*these authors contributed equally\u003Cbr>§These authors share senior authorship.\u003Cbr>Corresponding Author:\u003Cbr>Chao Che, PhD\u003Cbr>Key Laboratory of Advanced Design and Intelligent Computing Dalian University\u003Cbr>10 Xuefu Street Dalian, 116622 China\u003Cbr>Phone: 86 041187402046\u003Cbr>Email: [chechao@gmail.com](chechao@gmail.com)\u003Cbr>Abstract |\n| Background: The mechanisms underlying the mutual relationships between chronic physical illnesses and mental health disorders, which potentially explain their association, remain unclear. Furthermore, how patterns of this comorbidity evolve over time are significantly underinvestigated.\u003Cbr>Objective: The main aim of this study was to use machine learning models to model and analyze the complex interplay between mental health disorders and chronic physical illnesses. Another aim was to investigate the evolving longitudinal trajectories of patients’“health journeys.” Moreover, the study intended to clarify the variability of comorbidity patterns within the patient population by considering the effects of age and gender in different patient subgroups.\u003Cbr>Methods: Four machine learning models were used to conduct the analysis of the relationship between mental health disorders and chronic physical illnesses.\u003Cbr>Results: Through systematic research and in-depth analysis, we found that 5 categories of chronic physical illnesses exhibit a higher risk of comorbidity with mental health disorders. Further analysis of comorbidity intensity revealed correlations between specific disease combinations, with the strongest associati","cbCaipPDJqOdeMki","https://ap.wps.com/l/cbCaipPDJqOdeMki","pdf",3760879,4,1,18,"English","en",105,"# Abstract\n## Background\n## Objective\n## Methods\n## Results\n## Conclusions\n# Introduction","[{\"question\":\"What is the primary objective of the study?\",\"answer\":\"To use machine learning models to analyze the interplay between mental health disorders and chronic physical illnesses, including longitudinal comorbidity trajectories and subgroup differences by age and gender.\"},{\"question\":\"How did the researchers study the relationship between the two disorder groups?\",\"answer\":\"Four machine learning models were used to analyze associations between mental health disorders and chronic physical illnesses in a retrospective population setting.\"},{\"question\":\"What key results were reported about comorbidity risk?\",\"answer\":\"Five categories of chronic physical illnesses showed higher comorbidity risk with mental health disorders, including a strongest association between prostate diseases and organic mental disorders.\"}]","Identifying New Risk Associations Between Chronic Physical Illness and Mental Health Disorders in China - 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