Denolin H, Kuhn H, Krayenbuehl H, Loogen F, Reale A (1983) The definition of heart failure. Eur Heart J 4(7):445–448
Article CAS PubMed Google Scholar
Gheorghiade M, De Luca L, Fonarow GC, Filippatos G, Metra M, Francis GS (2005) Pathophysiologic targets in the early phase of acute heart failure syndromes. Am J Cardiol 96(6):11–17
Savarese G, Becher PM, Lund LH, Seferovic P, Rosano GMC, Coats AJS (2022) Global burden of heart failure: a comprehensive and updated review of epidemiology. Cardiovasc Res 118(17):3272–3287
Bozkurt B, Ahmad T, Alexander KM, Baker WL, Bosak K, Breathett K et al (2023) Heart failure epidemiology and outcomes statistics a report of the Heart Failure Society of America. J Card Fail 29(10):1412–1451
Article PubMed PubMed Central Google Scholar
Balogh EP, Miller BT, Ball JR (eds) (2015) Improving diagnosis in health care. National Academies Press, Washington, D.C.
Rahimi K, Bennett D, Conrad N, Williams TM, Basu J, Dwight J et al (2014) Risk prediction in patients with heart failure. JACC Heart Fail 2(5):440–6
McKie PM, Cataliotti A, Lahr BD, Martin FL, Redfield MM, Bailey KR et al (2010) The prognostic value of N-terminal pro-B-type natriuretic peptide for death and cardiovascular events in healthy normal and stage A/B heart failure subjects. J Am Coll Cardiol 55(19):2140–7
Article CAS PubMed PubMed Central Google Scholar
Sartipy U, Dahlström U, Edner M, Lund LH (2013) Predicting survival in heart failure: validation of the MAGGIC heart failure risk score in 51 043 patients from the Swedish Heart Failure Registry. Eur J Heart Fail 16(2):173–179
Newaz A, Ahmed N, Shahriyar HF (2021) Survival prediction of heart failure patients using machine learning techniques. Inform Med Unlocked 26:100772
Averbuch T, Sullivan K, Sauer A, Mamas MA, Voors AA, Gale CP et al (2022) Applications of artificial intelligence and machine learning in heart failure. Eur Heart J Digit Health 3(2):311–322
Article PubMed PubMed Central Google Scholar
Choi RY, Coyner AS, Kalpathy-Cramer J, Chiang MF, Campbell JP (2020) Introduction to machine learning, neural networks, and deep learning. Transl Vis Sci Technol 9(2):14–24
PubMed PubMed Central Google Scholar
Kukar M, Grošelj C (2005) Transductive machine learning for reliable medical diagnostics. J Med Syst 29(1):13–32
Levy JJ, O’Malley AJ (2020) Don’t dismiss logistic regression: the case for sensible extraction of interactions in the era of machine learning. BMC Med Res Methodol 20(1):171
Article PubMed PubMed Central Google Scholar
Averbuch T, Lee SF, Mamas MA, Oz UE, Perez R, Connolly SJ et al (2021) Derivation and validation of a two-variable index to predict 30-day outcomes following heart failure hospitalization. ESC Heart Fail 8(4):2690–2697
Article PubMed PubMed Central Google Scholar
Ahmad T, Lund LH, Rao P, Ghosh R, Warier P, Vaccaro B et al (2018) Machine learning methods improve prognostication, identify clinically distinct phenotypes, and detect heterogeneity in response to therapy in a large cohort of heart failure patients. J Am Heart Assoc 7(8):e008081
Article PubMed PubMed Central Google Scholar
Sullivan K, Mamas MA, Van Spall HGC (2019) Machine learning could facilitate optimal titration of guideline-directed medical therapy in heart failure. J Am Coll Cardiol 74(10):1424–1425
Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE et al (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 88:105906
Pettricrew M, Roberts H (2006) Systematic reviews in the social sciences, a practical guide. Blackwell Publishing, Malden, p 38
Malik A, Brito D, Vaqar S, et al (2024) Congestive heart failure. In: StatPearls. Treasure Island (FL): StatPearls Publishing. [Internet, Updated 2023 Nov 5], Available from: https://www.ncbi.nlm.nih.gov/books/NBK430873/
Okada A, Kaneko H, Konishi M, Kamiya K, Sugimoto T, Matsuoka S, Yokota I, Suzuki Y, Yamaguchi S, Itoh H, Fujiu K, Michihata N, Jo T, Matsui H, Fushimi K, Takeda N, Morita H, Yasunaga H, Komuro I (2024) A machine-learning-based prediction of non-home discharge among acute heart failure patients. Clin Res Cardiol. 113(4):522–532. https://doi.org/10.1007/s00392-023-02209-0
Jang SY, Park JJ, Adler E, Eshraghian E, Ahmad FS, Campagnari C, Yagil A, Greenberg B (2023) Mortality prediction in patients with or without heart failure using a machine learning model. JACC Adv 2(7):100554. https://doi.org/10.1016/j.jacadv.2023.100554.PMID:38939487;PMCID:PMC11198694
Article PubMed PubMed Central Google Scholar
Yang H, Tian J, Meng B, Wang K, Zheng C, Liu Y, Yan J, Han Q, Zhang Y (2021) Application of extreme learning machine in the survival analysis of chronic heart failure patients with high percentage of censored survival time. Front Cardiovasc Med 29(8):726516. https://doi.org/10.3389/fcvm.2021.726516.PMID:34778396;PMCID:PMC8586069
Chicco D, Jurman G (2020) Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC Med Inform Decis Mak 20(1):16. https://doi.org/10.1186/s12911-020-1023-5.PMID:32013925;PMCID:PMC6998201
Article PubMed PubMed Central Google Scholar
Kyodo A, Kanaoka K, Keshi A, Nogi M, Nogi K, Ishihara S, Kamon D, Hashimoto Y, Nakada Y, Ueda T, Seno A, Nishida T, Onoue K, Soeda T, Kawakami R, Watanabe M, Nagai T, Anzai T, Saito Y (2023) Heart failure with preserved ejection fraction phenogroup classification using machine learning. ESC Heart Fail 10(3):2019–2030. https://doi.org/10.1002/ehf2.14368
Article PubMed PubMed Central Google Scholar
Moreno-Sánchez PA (2023) Improvement of a prediction model for heart failure survival through explainable artificial intelligence. Front Cardiovasc Med 1(10):1219586. https://doi.org/10.3389/fcvm.2023.1219586.PMID:37600061;PMCID:PMC10434534
Sun R, Wang X, Jiang H, Yan Y, Dong Y, Yan W, Luo X, Miu H, Qi L, Huang Z (2022) Prediction of 30-day mortality in heart failure patients with hypoxic hepatitis: development and external validation of an interpretable machine learning model. Front Cardiovasc Med 28(9):1035675. https://doi.org/10.3389/fcvm.2022.1035675.PMID:36386374;PMCID:PMC9649827
Ishaq A, Sadiq S, Umer M, Ullah S, Mirjalili S, Rupapara V et al (2021) Improving the prediction of heart failure patients’ survival using SMOTE and effective data mining techniques. IEEE Access 9:39707–39716
Almazroi AA (2022) Survival prediction among heart patients using machine learning techniques. Math Biosci Eng 19(1):134–145. https://doi.org/10.3934/mbe.2022007
Awan SE, Bennamoun M, Sohel F, Sanfilippo FM, Dwivedi G (2019) Machine learning-based prediction of heart failure readmission or death: implications of choosing the right model and the right metrics. ESC Heart Fail 6(2):428–435. https://doi.org/10.1002/ehf2.12419
Article PubMed PubMed Central Google Scholar
What is Random Forest? [Beginner’s Guide + Examples]. careerfoundry.com. Available from: https://careerfoundry.com/en/blog/data-analytics/what-is-random-forest/#:~:text=Random%20Forest%20grows%20multiple%20decision. Accessed 2 Sept 2024
Koulaouzidis G, Iakovidis DK, Clark AL (2016) Telemonitoring predicts in advance heart failure admissions. Int J Cardiol 216:78–84
Article CAS PubMed Google Scholar
Mortazavi BJ, Downing NS, Bucholz EM, Dharmarajan K, Manhapra A, Li S-X, Negahban SN, Krumholz HM (2016) Analysis of machine learning techniques for heart failure readmissions. Circ Cardiovasc Qual Outcomes 9:629–640
Article PubMed PubMed Central Google Scholar
Okada A, Hashimoto Y, Goto T et al (2022) A machine learning-based predictive model to identify patients who failed to attend a follow-up visit for diabetes care after recommendations from a national screening program. Diabetes Care
Shin S, Austin PC, Ross HJ, Abdel-Qadir H, Freitas C, Tomlinson G et al (2020) Machine learning vs. conventional statistical models for predicting heart failure readmission and mortality. ESC Heart Fail 8(1):106–15
Article PubMed PubMed Central Google Scholar
Washida K, Kato T, Ozasa N et al (2021) Risk factors and clinical outcomes of nonhome discharge in patients with acute decompensated heart failure: an observational study. J Am Heart Assoc 10:e020292
留言 (0)