Azad N, Lemay G (2014) Management of chronic heart failure in the older population. J Geriatric Cardiol: JGC 11(4):329

CAS  PubMed  PubMed Central  Google Scholar 

Ammenwerth E, Modre-Osprian R, Fetz B et al (2018) HerzMobil, an integrated and collaborative telemonitoring-based disease management program for patients with heart failure: a feasibility study paving the way to routine care. JMIR Cardio 2(1):e9936

Article  Google Scholar 

Heidenreich PA, Albert NM, Allen LA et al (2013) Forecasting the impact of heart failure in the United States: a policy statement from the American Heart Association. Circ: Heart Failure 6(3):606–619

CAS  PubMed  Google Scholar 

Benjamin EJ, Virani SS, Callaway CW et al (2018) Heart disease and stroke statistics-2018 update: a report from the American Heart Association. Circulation 137(12):e67–e492

Article  PubMed  Google Scholar 

Wang H, Chai K, Du M et al (2021) Prevalence and incidence of heart failure among urban patients in China: a national population-based analysis. Circ Heart Fail 14(10):e008406

Article  PubMed  Google Scholar 

Chen J, Aronowitz P (2022) Congestive Heart Failure. Med Clin North Am 106(3):447–458

Article  PubMed  Google Scholar 

Ponikowski P, Voors AA, Anker SD et al (2016) 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. Kardiol Polska (Polish Heart J) 74(10):1037–1147

Article  Google Scholar 

Bradley J, Schelbert EB, Bonnett LJ et al (2022) Predicting hospitalisation for heart failure and death in patients with, or at risk of, heart failure before first Hospitalisation: a retrospective model development and external validation study. Lancet Digit Health 4(6):e445–e454

Article  CAS  PubMed  PubMed Central  Google Scholar 

Park J, Hwang IC, Yoon YE et al (2022) Predicting long-term mortality in patients with acute heart failure by using machine learning. J Cardiac Fail 28(7):1078–1087

Article  Google Scholar 

Wang Z, Zhu Y, Li D et al (2020) Feature rearrangement based deep learning system for predicting heart failure mortality. Comput Methods Programs Biomed 191:105383

Article  PubMed  Google Scholar 

Peng S, Huang J, Liu X et al (2022) Interpretable machine learning for 28-day all-cause in-hospital mortality prediction in critically ill patients with heart failure combined with hypertension: a retrospective cohort study based on medical information mart for intensive care database-IV and eICU databases. Front Cardiovasc Med 9:994359

Article  PubMed  PubMed Central  Google Scholar 

Gao Z, Liu X, Kang Y et al (2024) Improving the prognostic evaluation precision of hospital outcomes for heart failure using admission notes and clinical tabular data: multimodal deep learning model. J Med Internet Res 26:e54363

Article  PubMed  PubMed Central  Google Scholar 

Li D, Zheng C, Zhao J et al (2023) Diagnosis of heart failure from imbalance datasets using multi-level classification. Biomed Signal Process Control 81:104538

Article  Google Scholar 

Pan D, Pellicori P, Dobbs K et al (2021) Prognostic value of the CXRs in patients hospitalised for heart failure. Clin Res Cardiol 110:1–14

Article  Google Scholar 

Cheng J, Sollee J, Hsieh C et al (2022) COVID-19 mortality prediction in the intensive care unit with deep learning based on longitudinal CXRs and clinical data. Eur Radiol 32(7):4446–4456

Article  CAS  PubMed  PubMed Central  Google Scholar 

Shen B, Hou W, Jiang Z et al (2023) Longitudinal CXRs scores and their relations with clinical variables and outcomes in COVID-19 patients. Diagnostics 13(6):1107

Article  PubMed  PubMed Central  Google Scholar 

Pan D, Pellicori P, Urbinati A et al (2018) P2281 Relationship of the chest x-ray and outcome in patients with hospitalised heart failure. Eur Heart J. https://doi.org/10.1093/eurheartj/ehy565.P2281

Article  Google Scholar 

He K, Zhang X, Ren S, et al (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 770–778

Huang G, Liu Z, Van Der Maaten L, et al (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 4700–4708

Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 7132–7141

Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. Adv Neural Inform Process Syst, 30

Dosovitskiy A, Beyer L, Kolesnikov A, et al (2020) An image is worth 16x16 words: transformers for image recognition at scale, arXiv preprintarXiv:2010.11929

Tarasiou M, Chavez E, Zafeiriou S (2023) Vits for sits: vision transformers for satellite image time series. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp 10418–10428

Bi L, Wally O, Hu G et al (2023) A transformer-based approach for early prediction of soybean yield using time-series images. Front Plant Sci 14:1173036

Article  PubMed  PubMed Central  Google Scholar 

Ma Y, Wang R (2024) Relative-position embedding based spatially and temporally decoupled Transformer for action recognition. Pattern Recogn 142:109604

Google Scholar 

Kong T, Chen Y, Sun F, et al (2022) FasterVIT: Fast vision transformer with hierarchical attention. In: IEEE transactions on neural networks and learning systems

Arnab A, Dehghani M, Heigold G, et al (2021) Vivit: a video vision transformer. In:Proceedings of the IEEE/CVF international conference on computer vision. pp 6836–6846

Neimark D, Bar O, Zohar M, et al (2021) Video transformer network. In: Proceedings of the IEEE/CVF international conference on computer vision. pp 3163–3172

Wang Y, Xu Z, Wang X, et al (2021) End-to-end video instance segmentation with transformers. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp 8741–8750

Wu C, Wu F, Huang Y (2020) Da-transformer: distance-aware transformer, arXiv preprintarXiv:2010.06925

Li TZ, Xu K, Gao R et al (2023) Time-distance vision transformers in lung cancer diagnosis from longitudinal computed tomography, Medical Imaging 2023: Image Processing. SPIE 12464:221–230

Google Scholar 

Johnson AEW, Bulgarelli L, Shen L et al (2023) MIMIC-IV, a freely accessible electronic health record dataset. Sci Data 10(1):1

Article  CAS  PubMed  PubMed Central  Google Scholar 

Goldberger AL, Amaral LAN, Glass L et al (2000) PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101(23):e215–e220

Article  CAS  PubMed  Google Scholar 

Johnson AEW, Pollard TJ, Berkowitz SJ et al (2019) MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports. Sci Data 6(1):317

Article  PubMed  PubMed Central  Google Scholar 

Ba J L, Kiros J R, Hinton G E (2016) Layer normalization, arXiv preprintarXiv:1607.06450

Irvin J, Rajpurkar P, Ko M et al (2019) Chexpert: a large CXRs dataset with uncertainty labels and expert comparison. Proc AAAI Conf Artif Intell 33(01):590–597

Google Scholar 

Guo X, Yin Y, Dong C et al (2008) On the class imbalance problem, 2008 Fourth international conference on natural computation. IEEE 4:192–201

Google Scholar 

He H, Garcia EA (2009) Learning from imbalanced data. IEEE Trans Knowl Data Eng 21(9):1263–1284

Article  Google Scholar 

Batista GE, Prati RC, Monard MC (2004) A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explor Newsl 6(1):20–29

Article  Google Scholar 

Chawla NV, Bowyer KW, Hall LO et al (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321–357

Article  Google Scholar 

He H, Bai Y, Garcia EA, et al (2008) ADASYN: Adaptive synthetic sampling approach for imbalanced learning. In: 2008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence). IEEE, pp 1322–1328

Khushi M, Shaukat K, Alam TM et al (2021) A comparative performance analysis of data resampling methods on imbalance medical data. IEEE Access 9:109960–109975

Article  Google Scholar 

Sabouri M, Rajabi AB, Hajianfar G et al (2023) Machine learning based readmission and mortality prediction in heart failure patients. Sci Rep 13:18671. https://doi.org/10.1038/s41598-023-45925-3

Article  CAS  PubMed  PubMed Central  Google Scholar