Zhao Y, Atun R, Oldenburg B, et al. Physical multimorbidity, health service use, and catastrophic health expenditure by socioeconomic groups in China: An analysis of population-based panel data. Lancet Global Health. 2020;8(6):840–9. https://doi.org/10.1016/S2214-109X(20)30127-3.

Article  Google Scholar 

Barnett K, Mercer SW, Norbury M, et al. Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. Lancet. 2012;380(9836):37–43. https://doi.org/10.1016/S0140-6736(12)60240-2.

Article  Google Scholar 

Giannoula A, Centeno E, Mayer M-A, et al. A system-level analysis of patient disease trajectories based on clinical, phenotypic and molecular similarities. Bioinformatics. 2021;37(10):1435–43. https://doi.org/10.1093/bioinformatics/btaa964.

Article  Google Scholar 

La DTV, Zhao Y, Arokiasamy P, et al. Multimorbidity and out-of-pocket expenditure for medicines in China and India. BMJ Global Health. 2022;7(11): 007724. https://doi.org/10.1136/bmjgh-2021-007724.

Article  Google Scholar 

Monchka BA, Leung CK, Nickel NC, et al. The effect of disease co-occurrence measurement on multimorbidity networks: a population-based study. BMC Med Res Methodol. 2022;22(1):165. https://doi.org/10.1186/s12874-022-01607-8.

Article  Google Scholar 

Rayman G, Akpan A, Cowie M, et al. Managing patients with comorbidities: future models of care. Fut Healthc J. 2022;9(2):101–5. https://doi.org/10.7861/fhj.2022-0029.

Article  Google Scholar 

Lai HJ, Tan TH, Lin CS, Chen YF, Lin HH. Designing a clinical decision support system to predict readmissions for patients admitted with all-cause conditions. J Ambient Intell Human Comput. 2020;8:1–10.

Google Scholar 

Fan J, Sun Z, Yu C, et al. Multimorbidity patterns and association with mortality in 0.5 million Chinese adults. Chin Med J. 2022;135(6):648–57. https://doi.org/10.1097/CM9.0000000000001985.

Article  Google Scholar 

Vetrano DL, Roso-Llorach A, Fernández S, et al. Twelve-year clinical trajectories of multimorbidity in a population of older adults. Nat Commun. 2020;11(1):3223. https://doi.org/10.1038/s41467-020-16780-x.

Article  Google Scholar 

Khan A, Uddin S, Srinivasan U. Comorbidity network for chronic disease: a novel approach to understand type 2 diabetes progression. Int J Med Inform. 2018;115:1–9. https://doi.org/10.1016/j.ijmedinf.2018.04.001.

Article  Google Scholar 

Khan A, Uddin S, Srinivasan U. Chronic disease prediction using administrative data and graph theory: the case of type 2 diabetes. Expert Syst Appl. 2019;136:230–41. https://doi.org/10.1016/j.eswa.2019.05.048.

Article  Google Scholar 

Liang Y, Guo C. Heart failure disease prediction and stratification with temporal electronic health records data using patient representation. Biocybern Biomed Eng. 2023;43(1):124–41. https://doi.org/10.1016/j.bbe.2022.12.008.

Article  Google Scholar 

Lu H, Uddin S. Embedding-based link predictions to explore latent comorbidity of chronic diseases. Health Inform Sci Syst. 2023;11(2):1–11. https://doi.org/10.1007/s13755-022-00206-7.

Article  Google Scholar 

Wang L, Qiu H, Luo L, et al. Age- and sex-specific differences in multimorbidity patterns and temporal trends on assessing hospital discharge records in southwest China: Network-based study. J Med Internet Res. 2022;24(2):27146. https://doi.org/10.2196/27146.

Article  Google Scholar 

Wang T, Qiu RG, Yu M, et al. Directed disease networks to facilitate multiple-disease risk assessment modeling. Decis Support Syst. 2020;129: 113171. https://doi.org/10.1016/j.dss.2019.113171.

Article  Google Scholar 

Kalgotra P, Sharda R. When will I get out of the hospital? Modeling length of stay using comorbidity networks. J Manag Inform Syst. 2021;38(4):1150–84. https://doi.org/10.1080/07421222.2021.1990618.

Article  Google Scholar 

Yang P, Qiu H, Wang L, et al. Early prediction of high-cost inpatients with ischemic heart disease using network analytics and machine learning. Expert Syst Appl. 2022;210: 118541. https://doi.org/10.1016/j.eswa.2022.118541.

Article  Google Scholar 

Mei H, Jia R, Qiao G, et al. Human disease clinical treatment network for the elderly: analysis of the medicare inpatient length of stay and readmission data. Biometrics. 2023;79(1):404–16. https://doi.org/10.1111/biom.13549.

Article  MathSciNet  Google Scholar 

Hossain ME, Uddin S, Khan A. Network analytics and machine learning for predictive risk modelling of cardiovascular disease in patients with type 2 diabetes. Expert Syst Appl. 2021;164: 113918. https://doi.org/10.1016/j.eswa.2020.113918.

Article  Google Scholar 

Xu Z, Zhang Q, Yip PSF. Predicting post-discharge self-harm incidents using disease comorbidity networks: a retrospective machine learning study. J Affect Disord. 2020;277:402–9. https://doi.org/10.1016/j.jad.2020.08.044.

Article  Google Scholar 

Lu H, Uddin S. A disease network-based recommender system framework for predictive risk modelling of chronic diseases and their comorbidities. Appl Intell. 2022;52(9):10330–40. https://doi.org/10.1007/s10489-021-02963-6.

Article  Google Scholar 

Hu Z, Qiu H, Wang L, et al. Network analytics and machine learning for predicting length of stay in elderly patients with chronic diseases at point of admission. BMC Med Inform Decis Mak. 2022;22(1):62. https://doi.org/10.1186/s12911-022-01802-z.

Article  Google Scholar 

Choudhary GI, Fränti P. Predicting onset of disease progression using temporal disease occurrence networks. Int J Med Inform. 2023;175: 105068. https://doi.org/10.1016/j.ijmedinf.2023.105068.

Article  Google Scholar 

Yan Q, Lin X-Y, Peng C-W, Zheng W-J, et al. Network-based analysis between SARS-CoV-2 receptor ACE2 and common host factors in COVID-19 and asthma: potential mechanistic insights. Biomed Signal Process Control. 2024;87: 105502. https://doi.org/10.1016/j.bspc.2023.105502.

Article  Google Scholar 

Lakshmi K, Vadivu G. A novel approach for disease comorbidity prediction using weighted association rule mining. J Ambient Intell Human Comput. 2019;89:1–8. https://doi.org/10.1007/s12652-019-01217-1.

Article  Google Scholar 

Xu Z, Zhang J, Zhang Q, Xuan Q, Yip PSF. A comorbidity knowledge-aware model for disease prognostic prediction. IEEE Trans Cybernet. 2021;52(9):9809–19. https://doi.org/10.1109/TCYB.2021.3070227.

Article  Google Scholar 

Caruana A, Bandara M, Musial K, Catchpoole D, Kennedy PJ. Machine learning for administrative health records: a systematic review of techniques and applications. Artif Intell Med. 2023;48:102642. https://doi.org/10.1016/j.artmed.2023.102642.

Article  Google Scholar 

Kindig D, Stoddart G. What is population health? Am J Public Health. 2003;93(3):380–3. https://doi.org/10.2105/ajph.93.3.380.

Article  Google Scholar 

Soysaler C-A, Andrei CL, Ceban O, Sinescu C-J. Comorbidity patterns in patients at cardiovascular hospital admission. Medicines. 2023;10(4):26. https://doi.org/10.3390/medicines10040026.

Article  Google Scholar 

Rashid J, Batool S, Kim J, Wasif Nisar M, Hussain A, Juneja S, Kushwaha R. An augmented artificial intelligence approach for chronic diseases prediction. Front Public Health. 2022;10: 860396. https://doi.org/10.3389/fpubh.2022.860396.

Article  Google Scholar 

Han M, Wu H, Chen Z, Li M, Zhang X. A survey of multi-label classification based on supervised and semi-supervised learning. Int J Mach Learn Cybernet. 2023;14(3):697–724. https://doi.org/10.1007/s13042-022-01658-9.

Article  Google Scholar 

Zhang Y, Golbus JR, Wittrup E, Aaronson KD, Najarian K. Enhancing heart failure treatment decisions: interpretable machine learning models for advanced therapy eligibility prediction using ehr data. BMC Med Inform Decis Mak. 2024;24(1):53. https://doi.org/10.1186/s12911-024-02453-y.

Article  Google Scholar 

Duan H, Sun Z, Dong W, He K, Huang Z. On clinical event prediction in patient treatment trajectory using longitudinal electronic health records. IEEE J Biomed Health Inform. 2020;24(7):2053–63. https://doi.org/10.1109/JBHI.2019.2962079.

Article  Google Scholar 

Yashudas A, Gupta D, Prashant G, Dua A, AlQahtani D, Reddy ASK. DEEP-CARDIO: recommendation system for cardiovascular disease prediction using iot network. IEEE Sens J. 2024;24:9. https://doi.org/10.1109/JSEN.2024.3373429.

Article  Google Scholar 

Heidenreich PA, Bozkurt B, Aguilar D, et al. 2022 AHA/ACC/HFSA Guideline for the management of heart failure. J Am Coll Cardiol. 2022;79(17):263–421. https://doi.org/10.1016/j.jacc.2021.12.012.

Article  Google Scholar 

Li D, Zheng C, Zhao J, Liu Y. Diagnosis of heart failure from imbalance datasets using multi-level classification. Biomed Signal Process Control. 2023;81: 104538. https://doi.org/10.1016/j.bspc.2022.104538.

Article  Google Scholar 

Manemann SM, Chamberlain AM, Boyd CM, et al. Multimorbidity in heart failure: effect on outcomes. J Am Geriatr Soc. 2016;64(7):1469–74. https://doi.org/10.1111/jgs.14206.

Article  Google Scholar 

Gupta V, Mittal M, Mittal M, Mittal V, Chaturvedi Y. Detection of r-peaks using fractional fourier transform and principal component analysis. J Ambient Intell Human Comput. 2022;7:1–12. https://doi.org/10.1007/s12652-021-03484-3.

Article  Google Scholar 

Drozd M, Relton SD, Walker AMN, et al. Association of heart failure and its comorbidities with loss of life expectancy. Heart. 2021;107(17):1417–21.