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

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 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