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Open Access 01-12-2023 | Artificial Intelligence | Review

A cardiologist’s guide to machine learning in cardiovascular disease prognosis prediction

Authors: Karl-Patrik Kresoja, Matthias Unterhuber, Rolf Wachter, Holger Thiele, Philipp Lurz

Published in: Basic Research in Cardiology | Issue 1/2023

Abstract

A modern-day physician is faced with a vast abundance of clinical and scientific data, by far surpassing the capabilities of the human mind. Until the last decade, advances in data availability have not been accompanied by analytical approaches. The advent of machine learning (ML) algorithms might improve the interpretation of complex data and should help to translate the near endless amount of data into clinical decision-making. ML has become part of our everyday practice and might even further change modern-day medicine. It is important to acknowledge the role of ML in prognosis prediction of cardiovascular disease. The present review aims on preparing the modern physician and researcher for the challenges that ML might bring, explaining basic concepts but also caveats that might arise when using these methods. Further, a brief overview of current established classical and emerging concepts of ML disease prediction in the fields of omics, imaging and basic science is presented.

Ahmad F, McNally EM, Ackerman MJ, Baty LC, Day SM, Kullo IJ, Madueme PC, Maron MS, Martinez MW, Salberg L, Taylor MR, Wilcox JE (2019) Establishment of specialized clinical cardiovascular genetics programs: recognizing the need and meeting standards: a scientific statement from the American Heart Association. Circ Genom Precis Med 12:e000054. https://doi.org/10.1161/HCG.0000000000000054CrossRefPubMed

Akella A, Akella S (2021) Machine learning algorithms for predicting coronary artery disease: efforts toward an open source solution. Future Sci OA 7:FSO698. https://doi.org/10.2144/fsoa-2020-0206CrossRefPubMedPubMedCentral

Anderson M, Anderson SL (2019) How should AI be developed, validated, and implemented in patient care? AMA J Ethics 21:E125-130. https://doi.org/10.1001/amajethics.2019.125CrossRefPubMed

Antman EM, Loscalzo J (2016) Precision medicine in cardiology. Nat Rev Cardiol 13:591–602. https://doi.org/10.1038/nrcardio.2016.101CrossRefPubMed

Asch FM, Poilvert N, Abraham T, Jankowski M, Cleve J, Adams M, Romano N, Hong H, Mor-Avi V, Martin RP, Lang RM (2019) Automated echocardiographic quantification of left ventricular ejection fraction without volume measurements using a machine learning algorithm mimicking a human expert. Circ Cardiovasc Imaging 12:e009303. https://doi.org/10.1161/CIRCIMAGING.119.009303CrossRefPubMedPubMedCentral

Atienza F, Martinez-Alzamora N, Velasco JA de, Dreiseitl S, Ohno-Machado L (2000) Risk stratification in heart failure using artificial neural networks. In: Proc AMIA Symp, pp 32–36

Badillo S, Banfai B, Birzele F, Davydov II, Hutchinson L, Kam-Thong T, Siebourg-Polster J, Steiert B, Zhang JD (2020) An introduction to machine learning. Clin Pharmacol Ther 107:871–885. https://doi.org/10.1002/cpt.1796CrossRefPubMedPubMedCentral

Bayés-Genís A, Lanfear D (2019) Liquid biopsy and ehealth in heart failure: the future is now. J Am Coll Cardiol 73:2206–2208. https://doi.org/10.1016/j.jacc.2019.01.071CrossRefPubMed

Bazoukis G, Stavrakis S, Zhou J, Bollepalli SC, Tse G, Zhang Q, Singh JP, Armoundas AA (2020) Machine learning versus conventional clinical methods in guiding management of heart failure patients—a systematic review. Heart Fail Rev 26:23–34. https://doi.org/10.1007/s10741-020-10007-3CrossRefPubMedCentral

10.

Bojer CS, Meldgaard JP (2021) Kaggle forecasting competitions: an overlooked learning opportunity. Int J Forecast 37:587–603. https://doi.org/10.1016/j.ijforecast.2020.07.007CrossRef

11.

Breiman L (2001) Bagging predictors. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324CrossRef

12.

Burghardt TP, Ajtai K (2018) Neural/Bayes network predictor for inheritable cardiac disease pathogenicity and phenotype. J Mol Cell Cardiol 119:19–27. https://doi.org/10.1016/j.yjmcc.2018.04.006CrossRefPubMed

13.

Bzdok D, Altman N, Krzywinski M (2018) Statistics versus machine learning. Nat Methods 15:233–234. https://doi.org/10.1038/nmeth.4642CrossRefPubMedPubMedCentral

14.

de Caestecker M, Humphreys BD, Liu KD, Fissell WH, Cerda J, Nolin TD, Askenazi D, Mour G, Harrell FE, Pullen N, Okusa MD, Faubel S (2015) Bridging translation by improving preclinical study design in AKI. J Am Soc Nephrol 26:2905–2916. https://doi.org/10.1681/ASN.2015070832CrossRefPubMedPubMedCentral

15.

Ceglarek U, Schellong P, Rosolowski M, Scholz M, Willenberg A, Kratzsch J, Zeymer U, Fuernau G, de Waha-Thiele S, Büttner P, Jobs A, Freund A, Desch S, Feistritzer H-J, Isermann B, Thiery J, Pöss J, Thiele H (2021) The novel cystatin C, lactate, interleukin-6, and N-terminal pro-B-type natriuretic peptide (CLIP)-based mortality risk score in cardiogenic shock after acute myocardial infarction. Eur Heart J 42:2344–2352. https://doi.org/10.1093/eurheartj/ehab110CrossRefPubMed

16.

Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system, vol 11, pp 785–794. https://doi.org/10.1145/2939672.2939785

17.

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 9:14. https://doi.org/10.1167/tvst.9.2.14CrossRefPubMedPubMedCentral

18.

Churpek MM, Yuen TC, Winslow C, Meltzer DO, Kattan MW, Edelson DP (2016) Multicenter comparison of machine learning methods and conventional regression for predicting clinical deterioration on the wards. Crit Care Med 44:368–374. https://doi.org/10.1097/CCM.0000000000001571CrossRefPubMedPubMedCentral

19.

Clerx M, Heijman J, Collins P, Volders PGA (2018) Predicting changes to INa from missense mutations in human SCN5A. Sci Rep 8:12797. https://doi.org/10.1038/s41598-018-30577-5CrossRefPubMedPubMedCentral

20.

Commandeur F, Slomka PJ, Goeller M, Chen X, Cadet S, Razipour A, McElhinney P, Gransar H, Cantu S, Miller RJH, Rozanski A, Achenbach S, Tamarappoo BK, Berman DS, Dey D (2020) Machine learning to predict the long-term risk of myocardial infarction and cardiac death based on clinical risk, coronary calcium, and epicardial adipose tissue: a prospective study. Cardiovasc Res 116:2216–2225. https://doi.org/10.1093/cvr/cvz321CrossRefPubMed

21.

Senn S (2005) Dichotomania: an obsessive compulsive disorder that is badly affecting the quality of analysis of pharmaceutical trials. https://www.isi-web.org/isi.cbs.nl/iamamember/CD6-Sydney2005/ISI2005_Papers/398.pdf

22.

Ernst AF, Albers CJ (2017) Regression assumptions in clinical psychology research practice—a systematic review of common misconceptions. PeerJ. https://doi.org/10.7717/peerj.3323CrossRefPubMedPubMedCentral

23.

Frizzell JD, Liang L, Schulte PJ, Yancy CW, Heidenreich PA, Hernandez AF, Bhatt DL, Fonarow GC, Laskey WK (2017) Prediction of 30-day all-cause readmissions in patients hospitalized for heart failure: comparison of machine learning and other statistical approaches. JAMA Cardiol 2:204–209. https://doi.org/10.1001/jamacardio.2016.3956CrossRefPubMed

24.

Georgevici AI, Terblanche M (2019) Neural networks and deep learning: a brief introduction. Intensive Care Med 45:712–714. https://doi.org/10.1007/s00134-019-05537-wCrossRefPubMed

25.

Ho JE, Enserro D, Brouwers FP, Kizer JR, Shah SJ, Psaty BM, Bartz TM, Santhanakrishnan R, Lee DS, Chan C, Liu K, Blaha MJ, Hillege HL, van der Harst P, van Gilst WH, Kop WJ, Gansevoort RT, Vasan RS, Gardin JM, Levy D, Gottdiener JS, de Boer RA, Larson MG (2016) Predicting heart failure with preserved and reduced ejection fraction: the international collaboration on heart failure subtypes. Circ Heart Fail. https://doi.org/10.1161/CIRCHEARTFAILURE.115.003116CrossRefPubMedPubMedCentral

26.

Jaganathan K, KyriazopoulouPanagiotopoulou S, McRae JF, Darbandi SF, Knowles D, Li YI, Kosmicki JA, Arbelaez J, Cui W, Schwartz GB, Chow ED, Kanterakis E, Gao H, Kia A, Batzoglou S, Sanders SJ, Farh KK-H (2019) Predicting splicing from primary sequence with deep learning. Cell 176:535-548.e24. https://doi.org/10.1016/j.cell.2018.12.015CrossRefPubMed

27.

Johnson KW, Torres Soto J, Glicksberg BS, Shameer K, Miotto R, Ali M, Ashley E, Dudley JT (2018) Artificial intelligence in cardiology. J Am Coll Cardiol 71:2668–2679. https://doi.org/10.1016/j.jacc.2018.03.521CrossRefPubMed

28.

Johnston BC, Alonso-Coello P, Friedrich JO, Mustafa RA, Tikkinen KAO, Neumann I, Vandvik PO, Akl EA, Da Costa BR, Adhikari NK, Dalmau GM, Kosunen E, Mustonen J, Crawford MW, Thabane L, Guyatt GH (2016) Do clinicians understand the size of treatment effects? A randomized survey across 8 countries. CMAJ 188:25–32. https://doi.org/10.1503/cmaj.150430CrossRefPubMed

29.

Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, Tunyasuvunakool K, Bates R, Žídek A, Potapenko A, Bridgland A, Meyer C, Kohl SAA, Ballard AJ, Cowie A, Romera-Paredes B, Nikolov S, Jain R, Adler J, Back T, Petersen S, Reiman D, Clancy E, Zielinski M, Steinegger M, Pacholska M, Berghammer T, Bodenstein S, Silver D, Vinyals O, Senior AW, Kavukcuoglu K, Kohli P, Hassabis D (2021) Highly accurate protein structure prediction with AlphaFold. Nature 596:583–589. https://doi.org/10.1038/s41586-021-03819-2CrossRefPubMedPubMedCentral

30.

Kahneman D (2012) Thinking, fast and slow. Penguin psychology. Penguin Books, London

31.

Karwath A, Bunting KV, Gill SK, Tica O, Pendleton S, Aziz F, Barsky AD, Chernbumroong S, Duan J, Mobley AR, Cardoso VR, Slater L, Williams JA, Bruce E-J, Wang X, Flather MD, Coats AJS, Gkoutos GV, Kotecha D (2021) Redefining β-blocker response in heart failure patients with sinus rhythm and atrial fibrillation: a machine learning cluster analysis. Lancet 398:1427–1435. https://doi.org/10.1016/S0140-6736(21)01638-XCrossRefPubMedPubMedCentral

32.

Katz DH, Deo RC, Aguilar FG, Selvaraj S, Martinez EE, Beussink-Nelson L, Kim K-YA, Peng J, Irvin MR, Tiwari H, Rao DC, Arnett DK, Shah SJ (2017) Phenomapping for the identification of hypertensive patients with the myocardial substrate for heart failure with preserved ejection fraction. J Cardiovasc Transl Res 10:275–284. https://doi.org/10.1007/s12265-017-9739-zCrossRefPubMed

33.

Kimenai DM, Henry RMA, van der Kallen CJH, Dagnelie PC, Schram MT, Stehouwer CDA, van Suijlen JDE, Niens M, Bekers O, Sep SJS, Schaper NC, van Dieijen-Visser MP, Meex SJR (2016) Direct comparison of clinical decision limits for cardiac troponin T and I. Heart 102:610–616. https://doi.org/10.1136/heartjnl-2015-308917CrossRefPubMed

34.

Kleppe A, Skrede O-J, de Raedt S, Liestøl K, Kerr DJ, Danielsen HE (2021) Designing deep learning studies in cancer diagnostics. Nat Rev Cancer 21:199–211. https://doi.org/10.1038/s41568-020-00327-9CrossRefPubMed

35.

Krittanawong C, Johnson KW, Choi E, Kaplin S, Venner E, Murugan M, Wang Z, Glicksberg BS, Amos CI, Schatz MC, Tang WHW (2022) Artificial intelligence and cardiovascular genetics. Life (Basel). https://doi.org/10.3390/life12020279CrossRefPubMed

36.

Lahav O, Mastronarde N, van der Schaar M (2018) What is interpretable? Using machine learning to design interpretable decision-support systems. ML4H. https://arxiv.org/abs/1811.10799

37.

LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444. https://doi.org/10.1038/nature14539CrossRefPubMed

38.

Levy WC, Mozaffarian D, Linker DT, Sutradhar SC, Anker SD, Cropp AB, Anand I, Maggioni A, Burton P, Sullivan MD, Pitt B, Poole-Wilson PA, Mann DL, Packer M (2006) The Seattle Heart Failure Model: prediction of survival in heart failure. Circulation 113:1424–1433. https://doi.org/10.1161/CIRCULATIONAHA.105.584102CrossRefPubMed

39.

Li C, Sun D, Liu J, Li M, Zhang B, Liu Y, Wang Z, Wen S, Zhou J (2019) A prediction model of essential hypertension based on genetic and environmental risk factors in Northern Han Chinese. Int J Med Sci 16:793–799. https://doi.org/10.7150/ijms.33967CrossRefPubMedPubMedCentral

40.

Li L, Cheng W-Y, Glicksberg BS, Gottesman O, Tamler R, Chen R, Bottinger EP, Dudley JT (2015) Identification of type 2 diabetes subgroups through topological analysis of patient similarity. Sci Transl Med 7:311ra174. https://doi.org/10.1126/scitranslmed.aaa9364CrossRefPubMedPubMedCentral

41.

Lim N-K, Lee J-Y, Lee J-Y, Park H-Y, Cho M-C (2015) The role of genetic risk score in predicting the risk of hypertension in the Korean population: Korean Genome and Epidemiology Study. PLoS ONE 10:e0131603. https://doi.org/10.1371/journal.pone.0131603CrossRefPubMedPubMedCentral

42.

Liu L, Chen X, Petinrin OO, Zhang W, Rahaman S, Tang Z-R, Wong K-C (2021) Machine learning protocols in early cancer detection based on liquid biopsy: a survey. Life (Basel). https://doi.org/10.3390/life11070638CrossRefPubMedPubMedCentral

43.

Lundberg S, Lee S-I (2017) A unified approach to interpreting model predictions. https://arxiv.org/abs/1705.07874

44.

Luo Y, Ahmad FS, Shah SJ (2017) Tensor factorization for precision medicine in heart failure with preserved ejection fraction. J Cardiovasc Transl Res 10:305–312. https://doi.org/10.1007/s12265-016-9727-8CrossRefPubMedPubMedCentral

45.

Malki MA, Pearson ER (2020) Drug-drug-gene interactions and adverse drug reactions. Pharmacogenom J 20:355–366. https://doi.org/10.1038/s41397-019-0122-0CrossRef

46.

Marcu Ş-B, Tăbîrcă S, Tangney M (2022) An overview of Alphafold’s breakthrough. Front Artif Intell 5:875587. https://doi.org/10.3389/frai.2022.875587CrossRefPubMedPubMedCentral

47.

Márquez-Luna C, Loh P-R, Price AL (2017) Multiethnic polygenic risk scores improve risk prediction in diverse populations. Genet Epidemiol 41:811–823. https://doi.org/10.1002/gepi.22083CrossRefPubMedPubMedCentral

48.

Maulud D, Abdulazeez AM (2020) A review on linear regression comprehensive in machine learning. JASTT 1:140–147. https://doi.org/10.38094/jastt1457

49.

Menon S, Damian A, Hu S, Ravi N, Rudin C (2020) PULSE: self-supervised photo upsampling via latent space exploration of generative models. https://arxiv.org/abs/2003.03808

50.

Meskó B, Görög M (2020) A short guide for medical professionals in the era of artificial intelligence. NPJ Digit Med 3:126. https://doi.org/10.1038/s41746-020-00333-zCrossRefPubMedPubMedCentral

51.

Miotto R, Li L, Kidd BA, Dudley JT (2016) Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci Rep 6:26094. https://doi.org/10.1038/srep26094CrossRefPubMedPubMedCentral

52.

Moen E, Bannon D, Kudo T, Graf W, Covert M, van Valen D (2019) Deep learning for cellular image analysis. Nat Methods 16:1233–1246. https://doi.org/10.1038/s41592-019-0403-1CrossRefPubMedPubMedCentral

53.

Mohammad MA, Olesen KKW, Koul S, Gale CP, Rylance R, Jernberg T, Baron T, Spaak J, James S, Lindahl B, Maeng M, Erlinge D (2022) Development and validation of an artificial neural network algorithm to predict mortality and admission to hospital for heart failure after myocardial infarction: a nationwide population-based study. Lancet Digital Health 4:e37–e45. https://doi.org/10.1016/S2589-7500(21)00228-4CrossRefPubMed

54.

Molenaar MA, Selder JL, Nicolas J, Claessen BE, Mehran R, Bescós JO, Schuuring MJ, Bouma BJ, Verouden NJ, Chamuleau SAJ (2022) Current state and future perspectives of artificial intelligence for automated coronary angiography imaging analysis in patients with ischemic heart disease. Curr Cardiol Rep 24:365–376. https://doi.org/10.1007/s11886-022-01655-yCrossRefPubMedPubMedCentral

55.

Motwani M, Dey D, Berman DS, Germano G, Achenbach S, Al-Mallah MH, Andreini D, Budoff MJ, Cademartiri F, Callister TQ, Chang H-J, Chinnaiyan K, Chow BJW, Cury RC, Delago A, Gomez M, Gransar H, Hadamitzky M, Hausleiter J, Hindoyan N, Feuchtner G, Kaufmann PA, Kim Y-J, Leipsic J, Lin FY, Maffei E, Marques H, Pontone G, Raff G, Rubinshtein R, Shaw LJ, Stehli J, Villines TC, Dunning A, Min JK, Slomka PJ (2017) Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis. Eur Heart J 38:500–507. https://doi.org/10.1093/eurheartj/ehw188CrossRefPubMed

56.

Musoro JZ, Zwinderman AH, Puhan MA, ter Riet G, Geskus RB (2014) Validation of prediction models based on lasso regression with multiply imputed data. BMC Med Res Methodol. https://doi.org/10.1186/1471-2288-14-116CrossRefPubMedPubMedCentral

57.

Naess H, Logallo N, Waje-Andreassen U, Thomassen L, Kvistad CE (2019) U-shaped relationship between hemoglobin level and severity of ischemic stroke. Acta Neurol Scand 140:56–61. https://doi.org/10.1111/ane.13100CrossRefPubMed

58.

Niiranen TJ, Havulinna AS, Langén VL, Salomaa V, Jula AM (2016) Prediction of blood pressure and blood pressure change with a genetic risk score. J Clin Hypertens (Greenwich) 18:181–186. https://doi.org/10.1111/jch.12702CrossRefPubMed

59.

Noseworthy PA, Attia ZI, Behnken EM, Giblon RE, Bews KA, Liu S, Gosse TA, Linn ZD, Deng Y, Yin J, Gersh BJ, Graff-Radford J, Rabinstein AA, Siontis KC, Friedman PA, Yao X (2022) Artificial intelligence-guided screening for atrial fibrillation using electrocardiogram during sinus rhythm: a prospective non-randomised interventional trial. Lancet 400:1206–1212. https://doi.org/10.1016/S0140-6736(22)01637-3CrossRefPubMed

60.

Oguz C, Sen SK, Davis AR, Fu Y-P, O’Donnell CJ, Gibbons GH (2017) Genotype-driven identification of a molecular network predictive of advanced coronary calcium in ClinSeq^® and Framingham Heart Study cohorts. BMC Syst Biol 11:99. https://doi.org/10.1186/s12918-017-0474-5CrossRefPubMedPubMedCentral

61.

Ortiz J, Ghefter CG, Silva CE, Sabbatini RM (1995) One-year mortality prognosis in heart failure: a neural network approach based on echocardiographic data. J Am Coll Cardiol 26:1586–1593. https://doi.org/10.1016/0735-1097(95)00385-1CrossRefPubMed

62.

Pocock SJ, Ariti CA, McMurray JJV, Maggioni A, Køber L, Squire IB, Swedberg K, Dobson J, Poppe KK, Whalley GA, Doughty RN (2013) Predicting survival in heart failure: a risk score based on 39 372 patients from 30 studies. Eur Heart J 34:1404–1413. https://doi.org/10.1093/eurheartj/ehs337CrossRefPubMed

63.

Quer G, Arnaout R, Henne M, Arnaout R (2021) Machine learning and the future of cardiovascular care: JACC state-of-the-art review. J Am Coll Cardiol 77:300–313. https://doi.org/10.1016/j.jacc.2020.11.030CrossRefPubMedPubMedCentral

64.

Reel PS, Reel S, Pearson E, Trucco E, Jefferson E (2021) Using machine learning approaches for multi-omics data analysis: a review. Biotechnol Adv 49:107739. https://doi.org/10.1016/j.biotechadv.2021.107739CrossRefPubMed

65.

Rowe M (2019) An introduction to machine learning for clinicians. Acad Med 94:1433–1436. https://doi.org/10.1097/ACM.0000000000002792CrossRefPubMed

66.

Russell SJ, Norvig P (2021) Artificial intelligence: a modern approach, Fourth Edition Pearson series in artificial intelligence. Pearson, Hoboken

67.

Samuel AL (1959) Some studies in machine learning using the game of checkers. IBM J Res Dev 3:210–229. https://doi.org/10.1147/rd.33.0210CrossRef

68.

Selkoe DJ (2003) Folding proteins in fatal ways. Nature 426:900–904. https://doi.org/10.1038/nature02264CrossRefPubMed

69.

Shah SJ, Katz DH, Deo RC (2014) Phenotypic spectrum of heart failure with preserved ejection fraction. Heart Fail Clin 10:407–418. https://doi.org/10.1016/j.hfc.2014.04.008CrossRefPubMedPubMedCentral

70.

Shah SJ, Katz DH, Selvaraj S, Burke MA, Yancy CW, Gheorghiade M, Bonow RO, Huang C-C, Deo RC (2015) Phenomapping for novel classification of heart failure with preserved ejection fraction. Circulation 131:269–279. https://doi.org/10.1161/CIRCULATIONAHA.114.010637CrossRefPubMed

71.

Shah SJ, Kitzman DW, Borlaug BA, van Heerebeek L, Zile MR, Kass DA, Paulus WJ (2016) Phenotype-specific treatment of heart failure with preserved ejection fraction: a multiorgan roadmap. Circulation 134:73–90. https://doi.org/10.1161/CIRCULATIONAHA.116.021884CrossRefPubMedPubMedCentral

72.

Shameer K, Johnson KW, Glicksberg BS, Dudley JT, Sengupta PP (2018) Machine learning in cardiovascular medicine: are we there yet? Heart 104:1156–1164. https://doi.org/10.1136/heartjnl-2017-311198CrossRefPubMed

73.

Skolnick J, Gao M, Zhou H, Singh S (2021) AlphaFold 2: why it works and its implications for understanding the relationships of protein sequence, structure, and function. J Chem Inf Model 61:4827–4831. https://doi.org/10.1021/acs.jcim.1c01114CrossRefPubMedPubMedCentral

74.

Sounderajah V, Ashrafian H, Golub RM, Shetty S, de Fauw J, Hooft L, Moons K, Collins G, Moher D, Bossuyt PM, Darzi A, Karthikesalingam A, Denniston AK, Mateen BA, Ting D, Treanor D, King D, Greaves F, Godwin J, Pearson-Stuttard J, Harling L, McInnes M, Rifai N, Tomasev N, Normahani P, Whiting P, Aggarwal R, Vollmer S, Markar SR, Panch T, Liu X (2021) Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol. BMJ Open 11:e047709. https://doi.org/10.1136/bmjopen-2020-047709CrossRefPubMedPubMedCentral

75.

Sousa T, Correia J, Pereira V, Rocha M (2021) Generative deep learning for targeted compound design. J Chem Inf Model 61:5343–5361. https://doi.org/10.1021/acs.jcim.0c01496CrossRef

76.

Topol EJ (2019) High-performance medicine: the convergence of human and artificial intelligence. Nat Med 25:44–56. https://doi.org/10.1038/s41591-018-0300-7CrossRefPubMed

77.

Unterhuber M, Rommel K-P, Kresoja K-P, Lurz J, Kornej J, Hindricks G, Scholz M, Thiele H, Lurz P (2021) Deep learning detects heart failure with preserved ejection fraction using a baseline electrocardiogram. Eur Heart J Digit Health 2:699–703. https://doi.org/10.1093/ehjdh/ztab081CrossRefPubMedPubMedCentral

78.

Unterhuber M, Kresoja K-P, Rommel K-P, Besler C, Baragetti A, Klöting N, Ceglarek U, Blüher M, Scholz M, Catapano AL, Thiele H, Lurz P (2021) Proteomics-enabled deep learning machine algorithms can enhance prediction of mortality. J Am Coll Cardiol 78:1621–1631. https://doi.org/10.1016/j.jacc.2021.08.018CrossRefPubMed

79.

Unterhuber M, Kresoja K-P, Lurz P, Thiele H (2022) Artificial intelligence in proteomics: new frontiers from risk prediction to treatment? Eur Heart J. https://doi.org/10.1093/eurheartj/ehac391CrossRefPubMed

80.

Upadhyay AK, Sowdhamini R (2016) Genome-wide prediction and analysis of 3D-domain swapped proteins in the human genome from sequence information. PLoS ONE. https://doi.org/10.1371/journal.pone.0159627CrossRefPubMedPubMedCentral

81.

Wang X, Luan Y, Yue F (2022) EagleC: a deep-learning framework for detecting a full range of structural variations from bulk and single-cell contact maps. Sci Adv 8:eabn9215. https://doi.org/10.1126/sciadv.abn9215CrossRefPubMedPubMedCentral

82.

Williams MN, Grajales CAG, Kurkiewicz D (2013) Assumptions of multiple regression: correcting two misconceptions. University of Massachusetts Amherst

83.

Xiong G, Kola D, Heo R, Elmore K, Cho I, Min JK (2015) Myocardial perfusion analysis in cardiac computed tomography angiographic images at rest. Med Image Anal 24:77–89. https://doi.org/10.1016/j.media.2015.05.010CrossRefPubMedPubMedCentral

84.

Yadav SS, Jadhav SM (2019) Deep convolutional neural network based medical image classification for disease diagnosis. J Big Data. https://doi.org/10.1186/s40537-019-0276-2CrossRef

85.

Zhang J, Gajjala S, Agrawal P, Tison GH, Hallock LA, Beussink-Nelson L, Lassen MH, Fan E, Aras MA, Jordan C, Fleischmann KE, Melisko M, Qasim A, Shah SJ, Bajcsy R, Deo RC (2018) Fully automated echocardiogram interpretation in clinical practice. Circulation 138:1623–1635. https://doi.org/10.1161/CIRCULATIONAHA.118.034338CrossRefPubMedPubMedCentral

86.

Zou J, Huss M, Abid A, Mohammadi P, Torkamani A, Telenti A (2019) A primer on deep learning in genomics. Nat Genet 51:12–18. https://doi.org/10.1038/s41588-018-0295-5CrossRefPubMed

Title: A cardiologist’s guide to machine learning in cardiovascular disease prognosis prediction
Authors: Karl-Patrik Kresoja
Matthias Unterhuber
Rolf Wachter
Holger Thiele
Philipp Lurz
Publication date: 01-12-2023
Publisher: Springer Berlin Heidelberg
Keywords: Artificial Intelligence
Heart Failure
Published in: Basic Research in Cardiology / Issue 1/2023
Print ISSN: 0300-8428
Electronic ISSN: 1435-1803
DOI: https://doi.org/10.1007/s00395-023-00982-7

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Abstract

ACC 2024 Congress Coverage

Year in Review: Heart failure and cardiomyopathies

Semaglutide benefits people with type 2 diabetes and obesity-related heart failure

Incentivised to exercise

New intervention for STEMI-related cardiogenic shock

» View more highlights on the ACC 2024 congress hub page

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

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Other articles of this Issue 1/2023

The heterocellular heart: identities, interactions, and implications for cardiology

The RISK pathway leading to mitochondria and cardioprotection: how everything started

Membrane remodelling triggers maturation of excitation–contraction coupling in 3D-shaped human-induced pluripotent stem cell-derived cardiomyocytes

Endothelial ILK induces cardioprotection by preventing coronary microvascular dysfunction and endothelial-to-mesenchymal transition

Toll-like receptor-3 contributes to the development of aortic valve stenosis

Novel GSDMD inhibitor GI-Y1 protects heart against pyroptosis and ischemia/reperfusion injury by blocking pyroptotic pore formation

ACC 2024 Congress Coverage

Year in Review: Heart failure and cardiomyopathies

Semaglutide benefits people with type 2 diabetes and obesity-related heart failure

Incentivised to exercise

New intervention for STEMI-related cardiogenic shock

» View more highlights on the ACC 2024 congress hub page