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Analysis of Non-imaging Data

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AI and Big Data in Cardiology

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Abstract

Whilst most of this book has focused on imaging data because of the key role it plays in cardiology, non-imaging data also has an important role to play. This chapter reviews some of the most relevant non-imaging data sources and how they can be used by AI to positively impact patient management. Electrophysiology data, electrocardiograms and electronic health records are all discussed in detail and potential and existing applications for artificial intelligence are discussed with practical examples.

Authors’ contribution:

\(\bullet \) Main chapter: ND, OC, RS, AK.

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Notes

  1. 1.

    The query “ECG machine learning” in Pubmed returns 350\(+\) papers for 2021 against around 75 and 20 papers ten and twelve years previously.

  2. 2.

    http://physionetchallenges.github.io/.

References

  1. Corral-Acero J, Margara F, Marciniak M, Rodero C, Loncaric F, Feng Y, Gilbert A, Fernandes JF, Bukhari HA, Wajdan A, Martinez MV, Santos MS, Shamohammdi M, Luo H, Westphal P, Leeson P, DiAchille P, Gurev V, Mayr M, Geris L, Pathmanathan P, Morrison T, Cornelussen R, Prinzen F, Delhaas T, Doltra A, Sitges M, Vigmond EJ, Zacur E, Grau V, Rodriguez B, Remme EW, Niederer S, Mortier P, McLeod K, Potse M, Pueyo E, Bueno-Orovio A, Lamata P. The “Digital Twin” to enable the vision of precision cardiology. Eur Heart J. 2020; 41(48):4556–64.

    Google Scholar 

  2. Trayanova NA, Popescu DM, Shade JK. Machine learning in arrhythmia and electrophysiology. Circ Res. 2021; 128(4):544–66.

    Google Scholar 

  3. Nagarajan VD, Lee S-L, Robertus J-L, Nienaber CA, Trayanova NA, Ernst. Artificial intelligence in the diagnosis and management of arrhythmias. Eur Heart J. 2021; 42(38):3904–16.

    Google Scholar 

  4. Peng GC, Alber M, Buganza Tepole A, Cannon WR, De S, Dura-Bernal S, Garikipati K, Karniadakis G, Lytton WW, Perdikaris P, et al. Multiscale modeling meets machine learning: what can we learn?. Arch Comput Methods Eng. 2021; 28(3):1017–37.

    Google Scholar 

  5. Sánchez de la Nava AM, Atienza F, Bermejo J, Fernández-Avilés F. Artificial intelligence for a personalized diagnosis and treatment of atrial fibrillation. Am J Physiol Heart Circ Physiol. 2021; 320(4):H1337–47.

    Google Scholar 

  6. Chabiniok R, Wang VY, Hadjicharalambous M, Asner L, Lee J, Sermesant M, Kuhl E, Young AA, Moireau P, Nash MP, et al. Multiphysics and multiscale modelling, data-model fusion and integration of organ physiology in the clinic: ventricular cardiac mechanics. Interface Focus. 2016; 6(2):20150083.

    Google Scholar 

  7. Alber M, Buganza Tepole A, Cannon WR, De S, Dura-Bernal S, Garikipati K, Karniadakis G, Lytton WW, Perdikaris P, Petzold L, et al. Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences. NPJ Digit Med. 2019; 2(1):1–11.

    Google Scholar 

  8. Malik A, Peng T, Trew ML. A machine learning approach to reconstruction of heart surface potentials from body surface potentials. In: 40th annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE; 2018. p. 4828–31.

    Google Scholar 

  9. Sahli Costabal F, Yang Y, Perdikaris P, Hurtado DE, Kuhl E. Physics-informed neural networks for cardiac activation mapping. Front Phys. 2020; 8:42.

    Google Scholar 

  10. Bernard O, Lalande A, Zotti C, Cervenansky F, Yang X, Heng P, Cetin I, Lekadir K, Camara O, Ballester MAG, Sanroma G, Napel S, Petersen SE, Tziritas G, Grinias E, Khened M, Varghese A, Krishnamurthi G, Rohé M, Pennec X, Sermesant M, Isensee F, Jaeger P, Maier-Hein KH, Full PM, Wolf I, Engelhardt S, Baumgartner CF, Koch LM, Wolterink JM, Isgum I, Jang Y, Hong Y, Patravali J, Jain S, Humbert O, Jodoin P. Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved? IEEE Trans Med Imaging. 2018; 37(11):2514–25.

    Google Scholar 

  11. Lopez-Perez A, Sebastian R, Ferrero JM. Three-dimensional cardiac computational modelling: methods, features and applications. Biomed Eng Online. 2015; 14(1):1–31.

    Google Scholar 

  12. Ruiz Herrera C, Grandits T, Plank G, Perdikaris P, Sahli Costabal F, Pezzuto S. Physics-informed neural networks to learn cardiac fiber orientation from multiple electroanatomical maps. Eng Comput. 2022; 38:3957–73.

    Google Scholar 

  13. Raissi M, Perdikaris P, Karniadakis GE. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J Comput Phys. 2019; 378:686–707.

    Google Scholar 

  14. Giffard-Roisin S, Jackson T, Fovargue L, Lee J, Delingette H, Razavi R, Ayache N, Sermesant M. Noninvasive personalization of a cardiac electrophysiology model from body surface potential mapping. IEEE Trans Biomed Eng. 2017; 64(9):2206–18.

    Google Scholar 

  15. Prakosa A, Sermesant M, Allain P, Villain N, Rinaldi CA, Rhode K, Razavi R, Delingette H, Ayache N. Cardiac electrophysiological activation pattern estimation from images using a patient-specific database of synthetic image sequences. IEEE Trans Biomed Eng. 2013; 61(2):235–45.

    Google Scholar 

  16. Ferrer-Albero A, Godoy EJ, Lozano M, Martínez-Mateu L, Atienza F, Saiz J, Sebastian R. Non-invasive localization of atrial ectopic beats by using simulated body surface P-wave integral maps. PLoS One. 2017;12(7): e0181263.

    Google Scholar 

  17. Costabal FS, Matsuno K, Yao J, Perdikaris P, Kuhl E. Machine learning in drug development: characterizing the effect of 30 drugs on the QT interval using Gaussian process regression, sensitivity analysis, and uncertainty quantification. Comput Methods Appl Mech Eng. 2019; 348:313–33.

    Google Scholar 

  18. Godoy EJ, Lozano M, García-Fernández I, Ferrer-Albero A, MacLeod R, Saiz J, Sebastian R. Atrial fibrosis hampers non-invasive localization of atrial ectopic foci from multi-electrode signals: a 3D simulation study. Front Physiol. 2018; 9:404.

    Google Scholar 

  19. Doste R, Sebastian R, Gomez JF, Soto-Iglesias D, Alcaine A, Mont L, Berruezo A, Penela D, Camara O. In silico pace-mapping: prediction of left vs. right outflow tract origin in idiopathic ventricular arrhythmias with patient-specific electrophysiological simulations. EP Eur. 2020; 22(9):1419–30.

    Google Scholar 

  20. Prakosa A, Sermesant M, Delingette H, Saloux E, Allain P, Cathier P, Etyngier P, Villain N, Ayache N. Non-invasive activation times estimation using 3D echocardiography. In: International workshop on statistical atlases and computational models of the heart. Springer; 2010. p. 212–21.

    Google Scholar 

  21. Giffard-Roisin S, Delingette H, Jackson T, Webb J, Fovargue L, Lee J, Rinaldi CA, Razavi R, Ayache N, Sermesant M. Transfer learning from simulations on a reference anatomy for ECGI in personalized cardiac resynchronization therapy. IEEE Trans Biomed Eng. 2018; 66(2):343–53.

    Google Scholar 

  22. Jiang M, Lv J, Wang C, Huang W, Xia L, Shou G. A hybrid model of maximum margin clustering method and support vector regression for solving the inverse ECG problem. In: Computing in cardiology. IEEE; 2011. p. 457–60.

    Google Scholar 

  23. Clerx M, Heijman J, Collins P, Volders PGA. Predicting changes to INa from missense mutations in human SCN5A. Sci Rep. 2018; 8(1):12797.

    Google Scholar 

  24. Li B, Gallin WJ. Computational identification of residues that modulate voltage sensitivity of voltage-gated potassium channels. BMC Struct Biol. 2005; 5:16.

    Google Scholar 

  25. Lawson BA, Burrage K, Burrage P, Drovandi CC, Bueno-Orovio A. Slow recovery of excitability increases ventricular fibrillation risk as identified by emulation. Front Physiol. 2018; 9:1114.

    Google Scholar 

  26. Wacker S, Noskov SY. Performance of machine learning algorithms for qualitative and quantitative prediction drug blockade of hERG1 channel. Comput Toxicol. 2018; 6:55–63.

    Google Scholar 

  27. Mulimani MK, Alageshan JK, Pandit R. Deep-learning-assisted detection and termination of spiral and broken-spiral waves in mathematical models for cardiac tissue. Phys Rev Res. 2020; 2(2): 023155.

    Google Scholar 

  28. Zahid S, Cochet H, Boyle PM, Schwarz EL, Whyte KN, Vigmond EJ, Dubois R, Hocini M, Haïssaguerre M, Jaïs P, et al. Patient-derived models link re-entrant driver localization in atrial fibrillation to fibrosis spatial pattern. Cardiovasc Res. 2016; 110(3):443–54.

    Google Scholar 

  29. Yang T, Yu L, Jin Q, Wu L, He B. Localization of origins of premature ventricular contraction by means of convolutional neural network from 12-lead ECG. IEEE Trans Biomed Eng. 2017; 65(7):1662–71.

    Google Scholar 

  30. Gyawali PK, Horacek BM, Sapp JL, Wang L. Sequential factorized autoencoder for localizing the origin of ventricular activation from 12-lead electrocardiograms. IEEE Trans Biomed Eng. 2019; 67(5):1505–16.

    Google Scholar 

  31. Shade JK, Ali RL, Basile D, Popescu D, Akhtar T, Marine JE, Spragg DD, Calkins H, Trayanova NA. Preprocedure application of machine learning and mechanistic simulations predicts likelihood of paroxysmal atrial fibrillation recurrence following pulmonary vein isolation. Circ: Arrhythmia Electrophysiol. 2020; 13(7):e008213.

    Google Scholar 

  32. Sörnmo L, Laguna P. Bioelectrical signal processing in cardiac and neurological applications. Burlington: Academic Press; 2005.

    Google Scholar 

  33. Gacek A, Pedrycz W. ECG signal processing, classification and interpretation: a comprehensive framework of computational intelligence. London Limited: Springer; 2012.

    Book  Google Scholar 

  34. Hong S, Zhou Y, Shang J, Xiao C, Sun J. Opportunities and challenges of deep learning methods for electrocardiogram data: a systematic review. Comput Biol Med. 2020; 122: 103801.

    Google Scholar 

  35. Bond R, Finlay D, Nugent C, Moore G. A review of ECG storage formats. Int J Med Inform. 2011; 80:681–97.

    Google Scholar 

  36. Trigo J, Alesanco A, Martínez I, García J. A review on digital ECG formats and the relationships between them. IEEE Trans Inf Technol Biomed. 2012; 16:432–44.

    Google Scholar 

  37. Badilini F, Young B, Brown B, Vaglio M. Archiving and exchange of digital ECGs: a review of existing data formats. J Electrocardiol. 2018; 51:S113-5.

    Google Scholar 

  38. Martínez J, Almeida R, Olmos S, Rocha A, Laguna P. A wavelet-based ECG delineator: evaluation on standard databases. IEEE Trans Biomed Eng. 2004; 51:570–81.

    Google Scholar 

  39. Lyon A, Mincholé A, Martínez J, Laguna P, Rodriguez B. Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances. J R Soc Interface. 2018; 15:20170821.

    Google Scholar 

  40. Somani S, Russak A, Richter F, Zhao S, Vaid A, Chaudhry F, De Freitas J, Naik N, Miotto R, Nadkarni G, Narula J, Argulian E, Glicksberg B. Deep learning and the electrocardiogram: review of the current state-of-the-art. EP Eur. 2021; euaa377.

    Google Scholar 

  41. Perez Alday E, Gu A, Shah AJ, Robichaux C, Ian Wong A, Liu C, Liu F, Bahrami Rad A, Elola A, Seyedi S, Li Q, Sharma A, Clifford G, Reyna M. Classification of 12-lead ECGs: the PhysioNet/Computing in Cardiology Challenge 2020. Physiol Meas. 2021; 41:124003.

    Google Scholar 

  42. Liu F, Liu C, Zhao L, Zhang X, Wu X, Xu X, Liu Y, Ma C, Wei S, He Z, Li J, Yin Kwee E. An open access database for evaluating the algorithms of electrocardiogram rhythm and morphology abnormality detection. J Med Imaging Health Inform. 2018; 8:1368–73.

    Google Scholar 

  43. Sodmann P, Vollmer M, Nath N, Kaderali L. A convolutional neural network for ECG annotation as the basis for classification of cardiac rhythms. Physiol Meas. 2018; 39: 104005.

    Google Scholar 

  44. Camps J, Rodríguez B MA. Deep learning based QRS multilead delineator in electrocardiogram signals. Proc Comput Cardiol Conf (CinC). 2018; 45:1–4.

    Google Scholar 

  45. Jimenez-Perez G, Alcaine A, Camara O. U-Net architecture for the automatic detection and delineation of the electrocardiogram. Proc Comput Cardiol (CinC). 2019; 46:1–4.

    Google Scholar 

  46. Jimenez-Perez G, Alcaine A, Camara O. Delineation of the electrocardiogram with a mixed-quality-annotations dataset using convolutional neural networks. Sci Rep. 2021; 11:863.

    Google Scholar 

  47. Moskalenko V, Zolotykh N, Osipov G. Deep learning for ECG segmentation. In: Advances in neural computation, machine learning, and cognitive research III. Springer International Publishing; 2020. p. 246–54.

    Google Scholar 

  48. Tison G, Zhang J, Delling F, Deo R. Automated and interpretable patient ECG profiles for disease detection, tracking, and discovery. Circ: Cardiovasc Qual Outcomes. 2019; 12:e005289.

    Google Scholar 

  49. Abrishami H, Han C, Zhou X, Campbell M, Czosek R. Supervised ECG interval segmentation using LSTM neural network. In: Proceedings international conference on bioinformatics and computational biology (BIOCOMP), 2018.

    Google Scholar 

  50. Puthusserypady S, Peimankar A. DENS-ECG: a deep learning approach for ECG signal delineation. Expert Syst Appl. 2021; 165:113911.

    Google Scholar 

  51. Mincholé A, Camps J, Lyon A, Rodríguez B. Machine learning in the electrocardiogram. J Electrocardiol. 2019; 57:S61–4.

    Google Scholar 

  52. Ebrahimi Z, Loni M, Daneshtalab M, Gharehbaghi A. A review on deep learning methods for ECG arrhythmia classification. Expert Syst Appl: X. 2020; 7:100033.

    Google Scholar 

  53. Parvaneh S, Rubin J, Babaeizadeh S, Xu-Wilson M. Cardiac arrhythmia detection using deep learning: A review. Journal of Electrocardiology. 2019;57:S70–4.

    Article  Google Scholar 

  54. Strodthoff N, Wagner P, Schaeffter T, Samek W. Deep learning for ECG analysis: benchmarks and insights from PTB-XL. IEEE J Biomed Health Inform. 2021; 25:1519–28.

    Google Scholar 

  55. Jain R, Tandri H, Daly A, Tichnell C, James C, Abraham T, Judge D, Calkins H, Dalal D. Reader- and instrument-dependent variability in the electrocardiographic assessment of arrhythmogenic right ventricular dysplasia/cardiomyopathy. J Cardiovasc Electrophysiol. 2011; 22:561–8.

    Google Scholar 

  56. Tomlinson D, Bashir Y, Betts T, Rajappan K. Accuracy of manual QRS duration assessment: its importance in patient selection for cardiac resynchronization and implantable cardioverter defibrillator therapy. Europace. 2009; 11:638–42.

    Google Scholar 

  57. Richter R, Vineet V, Roth S, Koltun V. Playing for data: ground truth from computer games. Proc Eur Conf Comput Vis (ECCV), LNCS. 2016; 9906:102–18.

    Google Scholar 

  58. Heimann T, Mountney P, John M, Ionasec R. Real-time ultrasound transducer localization in fluoroscopy images by transfer learning from synthetic training data. Med Image Anal. 2014; 18:1320–8.

    Google Scholar 

  59. Doste R, Lozano M, Gomez J, Alcaine A, Mont L, Berruezo A, Camara O, Sebastian R. Predicting the origin of outflow tract ventricular arrhythmias using machine learning techniques trained with patient-specific electrophysiological simulations. Proc Comput Cardiol (CinC). 2019; 46:1–4.

    Google Scholar 

  60. Jimenez-Perez G, Acosta J, Alcaine A, Camara O. Generalizing electrocardiogram delineation: training convolutional neural networks with synthetic data augmentation. 2021; Available online https://arxiv.org/abs/2111.12996.

  61. Unterhuber M, Rommel K, Kresoja K, Lurz J, Kornej J, Hindricks G, Scholz M, Thiele H, Lurz P. Deep learning detects heart failure with preserved ejection fraction using a baseline electrocardiogram. Eur Heart J-Digit Health. 2021; ztab081.

    Google Scholar 

  62. Maier-Hein L, Eisenmann M, Reinke A, Onogur S, Stankovic M, Scholz P, Arbel T, Bogunovic H, Bradley A, Carass A, Feldmann C, Frangi A, Full P, van Ginneken B, Hanbury A, Honauer K, Kozubek M, Landman B, März K, Maier O, Maier-Hein K, Menze B, Müller H, Neher P, Niessen W, Rajpoot N, Sharp G, Sirinukunwattana K, Speidel S, Stock C, Stoyanov D, Taha A, van der Sommen F, Wang C, Weber M, Zheng G, Jannin P, Kopp-Schneider A. Why rankings of biomedical image analysis competitions should be interpreted with care. Nat Commun. 2018; 9:5217.

    Google Scholar 

  63. Kao DP, Trinkley KE, Lin C-T. Heart failure management innovation enabled by electronic health records. JACC: Heart Fail. 2020; 8(3):223–33.

    Google Scholar 

  64. Shickel B, Tighe PJ, Bihorac A, Rashidi P. Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE J Biomed Health Inform. 2018; 22:1589–604.

    Google Scholar 

  65. Johnson A, Pollard T, Shen L, Lehman L, Feng M, Ghassemi M, Moody B, Szolovits P, Celi L, Mark R. MIMIC-III, a freely accessible critical care database. Sci Data. 2016; 3:160035.

    Google Scholar 

  66. Huang S-C, Pareek A, Seyyedi S, Banerjee I, Lungren MP. Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines. NPJ Digit Med. 2020; 3(136).

    Google Scholar 

  67. Harerimana G, Kim JW, Yoo H, Jang B. Deep learning for electronic health records analytics. IEEE Access. 2019; 7:101 245–59.

    Google Scholar 

  68. Choi E, Schuetz A, Stewart WF, Sun J. Using recurrent neural network models for early detection of heart failure onset. J Am Med Inform Assoc. 2016; 24(2):361–70.

    Google Scholar 

  69. Lipton ZC, Kale DC, Elkan C, Wetzel R. Learning to diagnose with LSTM recurrent neural networks, Proc. ICLR 2016.

    Google Scholar 

  70. Latif J, Xiao C, Tu S, Rehman SU, Imran A, Bilal A. Implementation and use of disease diagnosis systems for electronic medical records based on machine learning: a complete review. IEEE Access. 2020; 8:150 489–513.

    Google Scholar 

  71. Choi E, Bahadori MT, Schuetz A, Stewart WF, Sun J. Doctor AI: predicting clinical events via recurrent neural networks. In: Doshi-Velez F, Fackler J, Kale D, Wallace B, Wiens J, editors.Proceedings of the 1st machine learning for healthcare conference, series proceedings of machine learning research, vol. 56. Northeastern University, Boston, MA, USA: PMLR, 18–19 Aug 2016. p. 301–18.

    Google Scholar 

  72. Miotto R, Li L, Kidd BA, Dudley JT. Deep Patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci Rep. 2016; 6:26094.

    Google Scholar 

  73. Yuan Q, Cai T, Hong C, Du M, Johnson BE, Lanuti M, Cai T, Christiani DC. Performance of a machine learning algorithm using electronic health record data to identify and estimate survival in a longitudinal cohort of patients with lung cancer. JAMA Netw Open. 2021; 4(7):e2 114 723–e2 114 723.

    Google Scholar 

  74. Wesolowski S, Lemmon G, Hernandez EJ, Henrie A, Miller TA, Weyhrauch D, Puchalski MD, Bray BE, Shah RU, Deshmukh VG, Delaney R, Yost HJ, Eilbeck K, Tristani-Firouzi M, Yandell M. An explainable artificial intelligence approach for predicting cardiovascular outcomes using electronic health records. PLOS Digit Health. 2022; 1(1):1–17.

    Google Scholar 

  75. Lasko TA, Denny JC, Levy MA. Computational phenotype discovery using unsupervised feature learning over noisy, sparse, and irregular clinical data. PLOS ONE. 2013; 8(6):1–13.

    Google Scholar 

  76. Beaulieu-Jones BK, Greene CS. Semi-supervised learning of the electronic health record for phenotype stratification. J Biomed Inform. 2016; 64:168–78.

    Google Scholar 

  77. Tran T, Nguyen TD, Phung D, Venkatesh S. Learning vector representation of medical objects via EMR-driven nonnegative restricted Boltzmann machines (eNRBM). J Biomed Inform. 2015; 54:96–105.

    Google Scholar 

  78. Choi E, Bahadori MT, Searles E, Coffey C, Thompson M, Bost J, Tejedor-Sojo J, Sun J. Multi-layer representation learning for medical concepts. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, series KDD ’16. New York, NY, USA: Association for Computing Machinery. 2016; p. 1495–504.

    Google Scholar 

  79. Choi E, Bahadori MT, Searles E, Coffey C, Thompson M, Bost J, Tejedor-Sojo J, Sun J. Medical concept representation learning from electronic health records and its application on heart failure prediction, arXiv. 2016.

  80. Landi I, Glicksberg BS, Lee H-C, Cherng S, Landi G, Danieletto M, Dudley JT, Furlanello C, Miotto R. Deep representation learning of electronic health records to unlock patient stratification at scale. NPJ Digit Med. 2020; 3(96).

    Google Scholar 

  81. Si Y, Du J, Li Z, Jiang X, Miller T, Wang F, Jim Zheng W, Roberts K. Deep representation learning of patient data from electronic health records (EHR): a systematic review. J Biomed Inform. 2020; 103671.

    Google Scholar 

  82. Bellamy D, Celi L, Beam AL. Evaluating progress on machine learning for longitudinal electronic healthcare data, arXiv. 2020.

  83. Messina P, Pino P, Parra D, Soto A, Besa C, Uribe S, Andía M, Tejos C, Prieto C, Capurro D. A survey on deep learning and explainability for automatic image-based medical report generation, arXiv. 2020.

  84. Rieke N, Hancox J, Li W, Milletarì F, Roth HR, Albarqouni S, Bakas S, Galtier MN, Landman BA, Maier-Hein K, Ourselin S, Sheller M, Summers RM, Trask A, Xu D, Baust M, Cardoso MJ. The future of digital health with federated learning. NPJ Digit Med. 2020; 3:119.

    Google Scholar 

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Acknowledgements

ND was supported by the French ANR (LABEX PRIMES of Univ. Lyon [ANR-11-LABX-0063] within the program “Investissements d’Avenir” [ANR-11-IDEX-0007], and the JCJC project “MIC-MAC” [ANR-19-CE45-0005]).

RS was supported by Generalitat Valenciana Grant AICO/2021/318 (Consolidables 2021) and Grant PID2020-114291RB-I00 funded by MCIN/ 10.13039/501100011033 and by “ERDF A way of making Europe”.

AK was supported by the EPSRC (EP/P001009/1), the Wellcome/EPSRC Centre for Medical Engineering at the School of Biomedical Engineering and Imaging Sciences, King’s College London (WT 203148/Z/16/Z) and the UKRI London Medical Imaging & Artificial Intelligence Centre for Value Based Healthcare.

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Authors and Affiliations

  1. Univ Lyon, Université Claude Bernard Lyon 1, INSA-Lyon, CNRS, Inserm, CREATIS UMR 5220, U1294, Lyon, France

    Nicolas Duchateau

  2. Institut Universitaire de France (IUF), Paris, France

    Nicolas Duchateau

  3. BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain

    Oscar Camara

  4. Department of Computer Science, Universitat de Valencia, Valencia, Spain

    Rafael Sebastian

  5. School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom

    Andrew King

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    Nicolas Duchateau

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    Andrew P. King

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Duchateau, N., Camara, O., Sebastian, R., King, A. (2023). Analysis of Non-imaging Data. In: Duchateau, N., King, A.P. (eds) AI and Big Data in Cardiology. Springer, Cham. https://doi.org/10.1007/978-3-031-05071-8_10

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