Advances in heart failure clinical research based on deep learning_Journal of Biomedical Engineering

Authors：

LEI Yingpeng ¹ , LIU Siru ² , WU Yuxuan ³ ,  LI Chuan ¹ ,  LIU Jialin ^4,5

1. College of Computer Science, Sichuan University, Chengdu 610041, P. R. China;
2. Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, USA;
3. West China Medical School, Sichuan University, Chengdu 610041, P. R. China;
4. Information Center, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
5. Department of Medical Informatics, West China Medical School, Sichuan University, Chengdu 610041, P. R. China;

Corresponding?author：

Keywords：

Heart failure; Deep learning; Diagnoses; Prognosis

DOI：

10.7507/1001-5515.202208060

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Heart failure is a disease that seriously threatens human health and has become a global public health problem. Diagnostic and prognostic analysis of heart failure based on medical imaging and clinical data can reveal the progression of heart failure and reduce the risk of death of patients, which has important research value. The traditional analysis methods based on statistics and machine learning have some problems, such as insufficient model capability, poor accuracy due to prior dependence, and poor model adaptability. In recent years, with the development of artificial intelligence technology, deep learning has been gradually applied to clinical data analysis in the field of heart failure, showing a new perspective. This paper reviews the main progress, application methods and major achievements of deep learning in heart failure diagnosis, heart failure mortality and heart failure readmission, summarizes the existing problems and presents the prospects of related research to promote the clinical application of deep learning in heart failure clinical research.

Citation： LEI Yingpeng, LIU Siru, WU Yuxuan, LI Chuan, LIU Jialin. Advances in heart failure clinical research based on deep learning. Journal of Biomedical Engineering, 2023, 40(2): 373-377, 383. doi: 10.7507/1001-5515.202208060 Copy

1.	Li M, Gao X, Wang H, et al. Phosphoglycerate mutase 2 is elevated in serum of patients with heart failure and correlates with the disease severity and patient's prognosis. Open Medicine, 2021, 16(1): 1134-1142.
2.	Zaharova S, Litwack K, Gopalakrishnan S, et al. Self-management in heart failure: the importance of self-regulation but not complexity of condition. West J Nurs Res, 2022, 44(4): 375-382.
3.	Groenewegen A, Rutten F H, Mosterd A, et al. Epidemiology of heart failure. European Journal of Heart Failure, 2020, 22(8): 1342–1356.
4.	Park L G, Dracup K, Whooley M A, et al. Sedentary lifestyle associated with mortality in rural patients with heart failure. Eur J Cardiovasc Nurs, 2019, 18(4): 318-324.
5.	Ahmad F S, Luo Y, Wehbe R M, et al. Advances in machine learning approaches to heart failure with preserved ejection fraction. Heart Fail Clin, 2022, 18(2): 287-300.
6.	Baashar Y, Alkawsi G, Alhussian H, et al. Effectiveness of artificial intelligence models for cardiovascular disease prediction: network meta-analysis. Comput Intell Neurosci, 2022, 2022: 5849995.
7.	中國醫療保健國際交流促進會急診醫學分會, 中華醫學會急診醫學分會, 中國醫師協會急診醫師分會, 等. 急性心力衰竭中國急診管理指南(2022). 中國急救醫學, 2022, 42(8): 648-670.
8.	Penso M, Solbiati S, Moccia S, et al. Decision support systems in HF based on deep learning technologies. Curr Heart Fail Rep, 2022, 19(2): 38–51.
9.	Kwon J M, Kim K H, Jeon K H, et al. Development and validation of deep-learning algorithm for electrocardiography-based heart failure identification. Korean Circ J, 2019, 49(7): 629-639.
10.	Acharya U R, Fujita H, Oh S L, et al. Deep convolutional neural network for the automated diagnosis of congestive heart failure using ECG signals. Applied Intelligence, 2019, 49:16–27.
11.	Lih O S, Jahmunah V, San T R, et al. Comprehensive electrocardiographic diagnosis based on deep learning. Artif Intell Med, 2020, 103: 101789.
12.	Lei M, Li J, Li M, et al. An improved UNet++ model for congestive heart failure diagnosis using short-term RR intervals. Diagnostics (Basel), 2021, 11(3):534.
13.	國家老年醫學中心國家老年疾病臨床醫學研究中心, 中國老年醫學學會心血管病分會, 北京醫學會心血管病學會影像學組. 中國成人心力衰竭超聲心動圖規范化檢查專家共識. 中國循環雜志, 2019, 34(5): 422-436.
14.	Ouyang D, He B, Ghorbani A, et al. Video-based AI for beat-to-beat assessment of cardiac function. Nature, 2020, 580(7802): 252–256.
15.	Tromp J, Seekings P J, Hung C L, et al. Automated interpretation of systolic and diastolic function on the echocardiogram: a multicohort study. The Lancet Digit Health, 2022, 4(1): e46-e54.
16.	沈文茜, 杜國慶. 機器學習在超聲心動圖中的應用進展. 心血管病學進展, 2021, 42(1): 43-46.
17.	Seah J C Y, Tang J S N, Kitchen A, et al. Chest radiographs in congestive heart failure: visualizing neural network learning. Radiology, 2019, 290(2): 514-522.
18.	Matsumoto T, Kodera S, Shinohara H, et al. Diagnosing heart failure from chest X-ray images using deep learning. Int Heart J, 2020, 61(4):781-786.
19.	Zarvani M, Saberi S, Azmi R, et al. Residual learning: a new paradigm to improve deep learning-based segmentation of the left ventricle in magnetic resonance imaging cardiac images. J Med Signals Sens, 2021, 11(3):159-168.
20.	Choi E, Schuetz A, Stewart W F, et al. Using recurrent neural network models for early detection of heart failure onset. Journal of the American Medical Informatics Association, 2017, 24(2):361–370.
21.	Rasmy L, Wu Y, Wang N, et al. A study of generalizability of recurrent neural network-based predictive models for heart failure onset risk using a large and heterogeneous EHR dataset. J Biomed Inform, 2018, 84:11–16.
22.	Madanan M , Zulkefli N , Velayudhan N C. Designing a hybrid artificial intelligent clinical decision support system using artificial neural network and artificial bee colony for predicting heart failure rate// 2021 International Conference on Computer Communication and Informatics (ICCCI -2021), Coimbatore: IEEE, 2021.
23.	Kwon B C, Choi M J, Kim J T, et al. RetainVis: visual analytics with interpretable and interactive recurrent neural networks on electronic medical records. IEEE Transactions on Visualization and Computer Graphics, 2019, 25(1):299-309.
24.	Bello G A, Dawes T, Duan J, et al. Deep learning cardiac motion analysis for human survival prediction. Nature machine intelligence, 2019, 1: 95–104.
25.	Kwon J M, Kim K H, Jeon K H, et al. Deep learning for predicting in-hospital mortality among heart disease patients based on echocardiography. Echocardiography, 2019, 36(2):213-218.
26.	Wang Z, Zhu Y, Li D, et al. Feature rearrangement based deep learning system for predicting heart failure mortality. Comput Methods Programs Biomed, 2020, 191:105383.
27.	付健. 基于MIMIC-Ⅲ數據庫的心衰患者死亡率預測模型研究. 太原: 太原理工大學, 2021.
28.	Chun S, Tu J V, Wijeysundera H C, et al. Lifetime analysis of hospitalizations and survival of patients newly admitted with heart failure. Circ Heart Fail, 2012, 5(4): 414-421.
29.	Xiao C, Ma T, Dieng A B, et al. Readmission prediction via deep contextual embedding of clinical concepts. PLoS ONE, 2018, 13(4): e0195024.
30.	Golas S B, Shibahara T, Agboola S, et al. A machine learning model to predict the risk of 30-day readmissions in patients with heart failure:a retrospective analysis of electronic medical records data. BMC Med Inform Decis Mak, 2018, 18(1): 44.
31.	Allam A, Nagy M, Thoma G, et al. Neural networks versus Logistic regression for 30?days all-cause readmission prediction. Sci Rep, 2019, 9(1): 9277.
32.	Ashfaq A, Sant'Anna A, Lingman M, et al. Readmission prediction using deep learning on electronic health records. J Biomed Inform, 2019, 97: 103256.
33.	達婧瑋, 顏嘉麒, 鄧三鴻, 等. 基于深度學習的重復住院預測模型研究——以心臟病為例. 數據分析與知識發現, 2020, 4(11): 63-73.
34.	Pishgar M, Theis J, Del Rios M, et al. Prediction of unplanned 30-day readmission for ICU patients with heart failure. BMC Med Inform Decis Mak, 2022, 22(1): 117.
35.	Nakamura T, Sasano T. Artificial intelligence and cardiology: current status and perspective. Journal of Cardiology, 2022, 79(3): 326-333.
36.	Ribeiro M T, Singh S, Guestrin C. “Why should I trust you?”: explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016: 1135–1144.
37.	Van den Eynde J, Lachmann M, Laugwitz K L, et al. Successfully implemented artificial intelligence and machine learning applications in cardiology: state-of-the-art review. Trends Cardiovasc Med, 2022. DOI: 10.1016/j.tcm.2022.01.010.
38.	Wang F, Kaushal R, Khullar D. Should health care demand interpretable artificial intelligence or accept “black box” medicine?. Ann Intern Med, 2020, 172(1): 59–60.
39.	Lundberg S M, Lee S I. A unified approach to interpreting model predictions//In Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017: 4768–4777.

1. Li M, Gao X, Wang H, et al. Phosphoglycerate mutase 2 is elevated in serum of patients with heart failure and correlates with the disease severity and patient's prognosis. Open Medicine, 2021, 16(1): 1134-1142.
2. Zaharova S, Litwack K, Gopalakrishnan S, et al. Self-management in heart failure: the importance of self-regulation but not complexity of condition. West J Nurs Res, 2022, 44(4): 375-382.
3. Groenewegen A, Rutten F H, Mosterd A, et al. Epidemiology of heart failure. European Journal of Heart Failure, 2020, 22(8): 1342–1356.
4. Park L G, Dracup K, Whooley M A, et al. Sedentary lifestyle associated with mortality in rural patients with heart failure. Eur J Cardiovasc Nurs, 2019, 18(4): 318-324.
5. Ahmad F S, Luo Y, Wehbe R M, et al. Advances in machine learning approaches to heart failure with preserved ejection fraction. Heart Fail Clin, 2022, 18(2): 287-300.
6. Baashar Y, Alkawsi G, Alhussian H, et al. Effectiveness of artificial intelligence models for cardiovascular disease prediction: network meta-analysis. Comput Intell Neurosci, 2022, 2022: 5849995.
7. 中國醫療保健國際交流促進會急診醫學分會, 中華醫學會急診醫學分會, 中國醫師協會急診醫師分會, 等. 急性心力衰竭中國急診管理指南(2022). 中國急救醫學, 2022, 42(8): 648-670.
8. Penso M, Solbiati S, Moccia S, et al. Decision support systems in HF based on deep learning technologies. Curr Heart Fail Rep, 2022, 19(2): 38–51.
9. Kwon J M, Kim K H, Jeon K H, et al. Development and validation of deep-learning algorithm for electrocardiography-based heart failure identification. Korean Circ J, 2019, 49(7): 629-639.
10. Acharya U R, Fujita H, Oh S L, et al. Deep convolutional neural network for the automated diagnosis of congestive heart failure using ECG signals. Applied Intelligence, 2019, 49:16–27.
11. Lih O S, Jahmunah V, San T R, et al. Comprehensive electrocardiographic diagnosis based on deep learning. Artif Intell Med, 2020, 103: 101789.
12. Lei M, Li J, Li M, et al. An improved UNet++ model for congestive heart failure diagnosis using short-term RR intervals. Diagnostics (Basel), 2021, 11(3):534.
13. 國家老年醫學中心國家老年疾病臨床醫學研究中心, 中國老年醫學學會心血管病分會, 北京醫學會心血管病學會影像學組. 中國成人心力衰竭超聲心動圖規范化檢查專家共識. 中國循環雜志, 2019, 34(5): 422-436.
14. Ouyang D, He B, Ghorbani A, et al. Video-based AI for beat-to-beat assessment of cardiac function. Nature, 2020, 580(7802): 252–256.
15. Tromp J, Seekings P J, Hung C L, et al. Automated interpretation of systolic and diastolic function on the echocardiogram: a multicohort study. The Lancet Digit Health, 2022, 4(1): e46-e54.
16. 沈文茜, 杜國慶. 機器學習在超聲心動圖中的應用進展. 心血管病學進展, 2021, 42(1): 43-46.
17. Seah J C Y, Tang J S N, Kitchen A, et al. Chest radiographs in congestive heart failure: visualizing neural network learning. Radiology, 2019, 290(2): 514-522.
18. Matsumoto T, Kodera S, Shinohara H, et al. Diagnosing heart failure from chest X-ray images using deep learning. Int Heart J, 2020, 61(4):781-786.
19. Zarvani M, Saberi S, Azmi R, et al. Residual learning: a new paradigm to improve deep learning-based segmentation of the left ventricle in magnetic resonance imaging cardiac images. J Med Signals Sens, 2021, 11(3):159-168.
20. Choi E, Schuetz A, Stewart W F, et al. Using recurrent neural network models for early detection of heart failure onset. Journal of the American Medical Informatics Association, 2017, 24(2):361–370.
21. Rasmy L, Wu Y, Wang N, et al. A study of generalizability of recurrent neural network-based predictive models for heart failure onset risk using a large and heterogeneous EHR dataset. J Biomed Inform, 2018, 84:11–16.
22. Madanan M , Zulkefli N , Velayudhan N C. Designing a hybrid artificial intelligent clinical decision support system using artificial neural network and artificial bee colony for predicting heart failure rate// 2021 International Conference on Computer Communication and Informatics (ICCCI -2021), Coimbatore: IEEE, 2021.
23. Kwon B C, Choi M J, Kim J T, et al. RetainVis: visual analytics with interpretable and interactive recurrent neural networks on electronic medical records. IEEE Transactions on Visualization and Computer Graphics, 2019, 25(1):299-309.
24. Bello G A, Dawes T, Duan J, et al. Deep learning cardiac motion analysis for human survival prediction. Nature machine intelligence, 2019, 1: 95–104.
25. Kwon J M, Kim K H, Jeon K H, et al. Deep learning for predicting in-hospital mortality among heart disease patients based on echocardiography. Echocardiography, 2019, 36(2):213-218.
26. Wang Z, Zhu Y, Li D, et al. Feature rearrangement based deep learning system for predicting heart failure mortality. Comput Methods Programs Biomed, 2020, 191:105383.
27. 付健. 基于MIMIC-Ⅲ數據庫的心衰患者死亡率預測模型研究. 太原: 太原理工大學, 2021.
28. Chun S, Tu J V, Wijeysundera H C, et al. Lifetime analysis of hospitalizations and survival of patients newly admitted with heart failure. Circ Heart Fail, 2012, 5(4): 414-421.
29. Xiao C, Ma T, Dieng A B, et al. Readmission prediction via deep contextual embedding of clinical concepts. PLoS ONE, 2018, 13(4): e0195024.
30. Golas S B, Shibahara T, Agboola S, et al. A machine learning model to predict the risk of 30-day readmissions in patients with heart failure:a retrospective analysis of electronic medical records data. BMC Med Inform Decis Mak, 2018, 18(1): 44.
31. Allam A, Nagy M, Thoma G, et al. Neural networks versus Logistic regression for 30?days all-cause readmission prediction. Sci Rep, 2019, 9(1): 9277.
32. Ashfaq A, Sant'Anna A, Lingman M, et al. Readmission prediction using deep learning on electronic health records. J Biomed Inform, 2019, 97: 103256.
33. 達婧瑋, 顏嘉麒, 鄧三鴻, 等. 基于深度學習的重復住院預測模型研究——以心臟病為例. 數據分析與知識發現, 2020, 4(11): 63-73.
34. Pishgar M, Theis J, Del Rios M, et al. Prediction of unplanned 30-day readmission for ICU patients with heart failure. BMC Med Inform Decis Mak, 2022, 22(1): 117.
35. Nakamura T, Sasano T. Artificial intelligence and cardiology: current status and perspective. Journal of Cardiology, 2022, 79(3): 326-333.
36. Ribeiro M T, Singh S, Guestrin C. “Why should I trust you?”: explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016: 1135–1144.
37. Van den Eynde J, Lachmann M, Laugwitz K L, et al. Successfully implemented artificial intelligence and machine learning applications in cardiology: state-of-the-art review. Trends Cardiovasc Med, 2022. DOI: 10.1016/j.tcm.2022.01.010.
38. Wang F, Kaushal R, Khullar D. Should health care demand interpretable artificial intelligence or accept “black box” medicine?. Ann Intern Med, 2020, 172(1): 59–60.
39. Lundberg S M, Lee S I. A unified approach to interpreting model predictions//In Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017: 4768–4777.

Journal of Biomedical Engineering

Advances in heart failure clinical research based on deep learning

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content