This paper is in the following e-collection/theme issue:

Data Science (357) Clinical Informatics (1325) Big Data (307) Chronic Conditions (434) Epidemiology (196) Descriptive Epidemiology and Population Size Estimates (140) Ontologies, Classifications, and Coding (324) Secondary Use of Clinical Data for Research and Surveillance (474) Statistical Methods for Surveillance and Population Health (188)

Published on in Vol 24, No 2 (2022): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/27146, first published .
Age- and Sex-Specific Differences in Multimorbidity Patterns and Temporal Trends on Assessing Hospital Discharge Records in Southwest China: Network-Based Study

Age- and Sex-Specific Differences in Multimorbidity Patterns and Temporal Trends on Assessing Hospital Discharge Records in Southwest China: Network-Based Study

Age- and Sex-Specific Differences in Multimorbidity Patterns and Temporal Trends on Assessing Hospital Discharge Records in Southwest China: Network-Based Study

Authors of this article:

Liya Wang1 Author Orcid Image ;   Hang Qiu1, 2 Author Orcid Image ;   Li Luo3 Author Orcid Image ;   Li Zhou4 Author Orcid Image
Article Authors Cited by (26) Tweetations (2) Metrics

Journals

  1. Yang P, Qiu H, Wang L, Zhou L. Early prediction of high-cost inpatients with ischemic heart disease using network analytics and machine learning. Expert Systems with Applications 2022;210:118541 View
  2. Wang L, Jin Y, Zhou J, Pang C, Wang Y, Zhang S. Phenotypic Disease Network-Based Multimorbidity Analysis in Idiopathic Cardiomyopathy Patients with Hospital Discharge Records. Journal of Clinical Medicine 2022;11(23):6965 View
  3. Sharma S, Nambiar D, Ghosh A. Sex differences in non-communicable disease multimorbidity among adults aged 45 years or older in India. BMJ Open 2023;13(3):e067994 View
  4. Zhou D, Qiu H, Wang L, Shen M. Risk prediction of heart failure in patients with ischemic heart disease using network analytics and stacking ensemble learning. BMC Medical Informatics and Decision Making 2023;23(1) View
  5. Qiu H, Wang L, Zhou L, Wang X. Comorbidity Patterns in Patients Newly Diagnosed With Colorectal Cancer: Network-Based Study. JMIR Public Health and Surveillance 2023;9:e41999 View
  6. Chen Y, Pan M, He Y, Dong X, Hu Z, Hou J, Bao Y, Yang J, Yuchi Y, Li R, Zhu L, Kang N, Liao W, Li S, Wang C, Zhang L. Disease Burden and the Accumulation of Multimorbidity of Noncommunicable Diseases in a Rural Population in Henan, China: Cross-sectional Study. JMIR Public Health and Surveillance 2023;9:e43381 View
  7. Zhang Z, He P, Yao H, Jing R, Sun W, Lu P, Xue Y, Qi J, Cui B, Cao M, Ning G. A network-based study reveals multimorbidity patterns in people with type 2 diabetes. iScience 2023;26(10):107979 View
  8. Bao Y, Lu P, Wang M, Zhang X, Song A, Gu X, Ma T, Su S, Wang L, Shang X, Zhu Z, Zhai Y, He M, Li Z, Liu H, Fairley C, Yang J, Zhang L. Exploring multimorbidity profiles in middle-aged inpatients: a network-based comparative study of China and the United Kingdom. BMC Medicine 2023;21(1) View
  9. Naik H, Murray T, Khan M, Daly-Grafstein D, Liu G, Kassen B, Onrot J, Sutherland J, Staples J. Population-Based Trends in Complexity of Hospital Inpatients. JAMA Internal Medicine 2024;184(2):183 View
  10. Wang H, Xia Q, Dong Z, Guo W, Deng W, Zhang L, Kuang W, Li T. Emotional distress and multimorbidity patterns in Chinese Han patients with osteoporosis: a network analysis. Frontiers in Public Health 2024;11 View
  11. Yang P, Qiu H, Yang X, Wang L, Wang X. SAGL: A self-attention-based graph learning framework for predicting survival of colorectal cancer patients. Computer Methods and Programs in Biomedicine 2024;249:108159 View
  12. Wang Y, Zhang Y, Guo T, Han J, Fu G. Knowledge level and health information-seeking behavior of people with diabetes in rural areas: a multicenter cross-sectional study. Frontiers in Public Health 2024;12 View
  13. Qiu H, Yang P, Wang L. DCNeT: A disease comorbidity network-based temporal deep learning framework to predict cardiovascular risk in patients with mental disorders. Expert Systems with Applications 2024;254:124312 View
  14. Liang Y, Guo C, Li H. Comorbidity progression analysis: patient stratification and comorbidity prediction using temporal comorbidity network. Health Information Science and Systems 2024;12(1) View
  15. Wang J, Qi Z, Liu X, Li X, Cao Z, Zeng D, Wang H. Population and Co-Occurrence Characteristics of Diagnoses and Comorbidities in Coronary Artery Disease Patients: A Case Study from a Hospital in Guangxi, China. Bioengineering 2024;11(12):1284 View
  16. Quan S, Monchka B, St. John P, Doupe M, Turgeon M, Lix L. Network Analyses to Explore Comorbidities Among Older Adults Living With Dementia. Journal of the American Geriatrics Society 2025;73(4):1168 View
  17. Liang L, Liu T, Ollier W, Peng Y, Lu Y, Che C. New Risk Associations Between Chronic Physical Illness and Mental Health Disorders revealed by Machine Learning: A Chinese Population Study (Preprint). JMIR AI 2025 View
  18. Zhang S, Yu C, Zhou J, Wang Y, Jin Y, Zhang S. Multimorbidity Patterns in Patients with Pulmonary Arterial Hypertension Identified Through Hospital Discharge Records: A Network-Based Analysis. Cardiovascular Innovations and Applications 2025;10(1) View
  19. Xu J, Li Q, Deng L, Xiong J, Cheng Z, Ye C. Global, regional, and national epidemiology of congenital heart disease in children from 1990 to 2021. Frontiers in Cardiovascular Medicine 2025;12 View
  20. Xue X, Chen N, Zhao K, Qi Y, Zhao M, Shi L, Li Y, Yu J. Prevalence, Analytical Methods, and Influencing Factors of Multimorbidity in China: A Scoping Review. Journal of Evidence-Based Medicine 2025;18(2) View
  21. Mao D, Mu J, Li Y, He L, Chai Q, Zhao X, Ren X, Cheng H. Network-based machine learning reveals cardiometabolic multimorbidity patterns and modifiable lifestyle factors: a community-focused analysis of NHANES 2015–2018. BMC Public Health 2025;25(1) View
  22. Liu L, Wang X, Gui M, Ju F, Cao L, Bi B. Investigation of multimorbidity patterns and association rules in patients with type 2 diabetes mellitus using association rules mining algorithm. Scientific Reports 2025;15(1) View
  23. Tang S, Li Y, Li R, Yang H, Hao T, Tian M, Zhang X, Feng X, Li Z, Elhadi Y. Prevalence and patterns of multimorbidity among adults in rural Shanxi Province, China: A post-hoc exploratory subgroup analysis of a cross-sectional study. PLOS One 2025;20(9):e0330935 View
  24. Bao Y, Sun Y, Wang M, K·Fairley C, Lu P, O’Brien T, Su S, Liu X, Wang L, Liu H, Zhang X, Shang X, Zhu Z, Lu Q, Li Z, Lai H, Wang J, Ma T, Wang L, Xie X, Wang W, Wu W, Yang J, Zhang L. Sex and age-specific multimorbidity profiles among working-age inpatients in China: a comparative network analysis. BMC Public Health 2025;25(1) View
  25. Bao Y, Liu H, Lu Q, Sun Y, Wang L, Su S, Lu P, Wang M, Ma T, Xie X, Wang W, Wang L, Zhai Y, Lu F, Wei Y, Li R, Ding M, Yan Y, Jia S, Zhang X, Yang J, Zhang L, Putnam M. Sex- and age-specific multimorbidity networks in middle-aged inpatients: a network-based comparative study between China and the United Kingdom. Innovation in Aging 2025;9(10) View
  26. Zeb H, Younas A, Bassi E, Dal Molin A, Durante A. “Preserving Social Manliness”: A Narrative Inquiry of Hegemonic Masculinity and Self-Management of Multimorbidity Among Pakistani Men. The Journal of Men’s Studies 2025 View