Cardiovascular
Machine learning in precision diabetes care and cardiovascular risk prediction
Sep
Haug CJ, Drazen JM. Artificial intelligence and machine learning in clinical medicine, 2023. N Engl J Med. 2023;388(13):1201–8.
Google Scholar
Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56.
Google Scholar
Joseph JJ, Deedwania P, Acharya T, Aguilar D, Bhatt DL, Chyun DA, et al. Comprehensive management of cardiovascular risk factors for adults with type 2 diabetes: a scientific statement from the American Heart Association. Circulation. 2022;145(9):e722–59.
Google Scholar
Ong KL, Stafford LK, McLaughlin SA, Boyko EJ, Vollset SE, Smith AE, et al. Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021. Lancet. 2023. https://doi.org/10.1016/S0140-6736(23)01301-6.
Google Scholar
Ravaut M, Harish V, Sadeghi H, Leung KK, Volkovs M, Kornas K, et al. Development and validation of a machine learning model using administrative health data to predict onset of type 2 diabetes. JAMA Netw Open. 2021;4(5): e2111315.
Google Scholar
Seto H, Oyama A, Kitora S, Toki H, Yamamoto R, Kotoku J, et al. Gradient boosting decision tree becomes more reliable than logistic regression in predicting probability for diabetes with big data. Sci Rep. 2022;12(1):15889.
Google Scholar
Kulkarni AR, Patel AA, Pipal KV, Jaiswal SG, Jaisinghani MT, Thulkar V, et al. Machine-learning algorithm to non-invasively detect diabetes and pre-diabetes from electrocardiogram. BMJ Innov. 2023. https://doi.org/10.1136/bmjinnov-2021-000759.
Google Scholar
Hahn S-J, Kim S, Choi YS, Lee J, Kang J. Prediction of type 2 diabetes using genome-wide polygenic risk score and metabolic profiles: a machine learning analysis of population-based 10-year prospective cohort study. EBioMedicine. 2022;86: 104383.
Google Scholar
Carrasco-Zanini J, Pietzner M, Lindbohm JV, Wheeler E, Oerton E, Kerrison N, et al. Proteomic signatures for identification of impaired glucose tolerance. Nat Med. 2022;28(11):2293–300.
Google Scholar
Tallam H, Elton DC, Lee S, Wakim P, Pickhardt PJ, Summers RM. Fully automated abdominal CT biomarkers for type 2 diabetes using deep learning. Radiology. 2022;304(1):85–95.
Google Scholar
Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402–10.
Google Scholar
Oikonomou EK, Suchard MA, McGuire DK, Khera R. Phenomapping-derived tool to individualize the effect of canagliflozin on cardiovascular risk in type 2 diabetes. Diabetes Care. 2022;45(4):965–74.
Google Scholar
Manzini E, Vlacho B, Franch-Nadal J, Escudero J, Génova A, Reixach E, et al. Longitudinal deep learning clustering of type 2 diabetes mellitus trajectories using routinely collected health records. J Biomed Inform. 2022;135: 104218.
Google Scholar
Hanna J, Nargesi AA, Essien UR, Sangha V, Lin Z, Krumholz HM, et al. County-level variation in cardioprotective antihyperglycemic prescribing among medicare beneficiaries. Am J Prev Cardiol. 2022;11: 100370.
Google Scholar
Sangha V, Lipska K, Lin Z, Inzucchi SE, McGuire DK, Krumholz HM, et al. Patterns of prescribing sodium–glucose cotransporter-2 inhibitors for medicare beneficiaries in the United States. Circ Cardiovasc Qual Outcomes. 2021;14(12): e008381.
Google Scholar
Nargesi AA, Clark C, Aminorroaya A, Chen L, Liu M, Reddy A, et al. Persistence on novel cardioprotective antihyperglycemic therapies in the United States. Am J Cardiol. 2023;196:89–98.
Google Scholar
Nargesi AA, Jeyashanmugaraja GP, Desai N, Lipska K, Krumholz H, Khera R. Contemporary national patterns of eligibility and use of novel cardioprotective antihyperglycemic agents in type 2 diabetes mellitus. J Am Heart Assoc. 2021;10(13): e021084.
Google Scholar
Vollmer S, Mateen BA, Bohner G, Király FJ, Ghani R, Jonsson P, et al. Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness. BMJ. 2020;368: l6927.
Google Scholar
Srikumar M, Finlay R, Abuhamad G, Ashurst C, Campbell R, Campbell-Ratcliffe E, et al. Advancing ethics review practices in AI research. Nat Mach Intell. 2022;4(12):1061–4.
Google Scholar
Muehlematter UJ, Daniore P, Vokinger KN. Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015–20): a comparative analysis. Lancet Digit Health. 2021;3(3):e195-203.
Google Scholar
Gottlieb S, Silvis L. Regulators face novel challenges as artificial intelligence tools enter medical practice. JAMA Health Forum. 2023;4(6): e232300.
Google Scholar
Sarker IH. Machine learning: algorithms, real-world applications and research directions. SN Comput Sci. 2021;2(3):160.
Google Scholar
Panch T, Szolovits P, Atun R. Artificial intelligence, machine learning and health systems. J Glob Health. 2018;8(2): 020303.
Google Scholar
Austin PC, van Klaveren D, Vergouwe Y, Nieboer D, Lee DS, Steyerberg EW. Validation of prediction models: examining temporal and geographic stability of baseline risk and estimated covariate effects. Diagn Progn Res. 2017;1:12.
Google Scholar
Jing L, Tian Y. Self-supervised visual feature learning with deep neural networks: a survey. IEEE Trans Pattern Anal Mach Intell. 2021;43(11):4037–58.
Google Scholar
Huang S-C, Pareek A, Jensen M, Lungren MP, Yeung S, Chaudhari AS. Self-supervised learning for medical image classification: a systematic review and implementation guidelines. NPJ Digit Med. 2023;6(1):74.
Google Scholar
Holste G, Oikonomou EK, Mortazavi B, Wang Z, Khera R. Self-supervised learning of echocardiogram videos enables data-efficient clinical diagnosis. arXiv [cs.CV]. 2022. http://arxiv.org/abs/2207.11581.
Holste G, Oikonomou EK, Mortazavi BJ, Coppi A, Faridi KF, Miller EJ, et al. Severe aortic stenosis detection by deep learning applied to echocardiography. Eur Heart J. 2023; Available from: https://doi.org/10.1093/eurheartj/ehad456.
Hu X, Zeng D, Xu X, Shi Y. Semi-supervised contrastive learning for label-efficient medical image segmentation. arXiv [cs.CV]. 2021. http://arxiv.org/abs/2109.07407.
Mehari T, Strodthoff N. Self-supervised representation learning from 12-lead ECG data. Comput Biol Med. 2022;141: 105114.
Google Scholar
Goff DC Jr, Lloyd-Jones DM, Bennett G, Coady S, D’Agostino RB, Gibbons R, et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation. 2014;129(25 Suppl 2):S49-73.
Google Scholar
Hippisley-Cox J, Coupland C, Brindle P. Development and validation of QRISK3 risk prediction algorithms to estimate future risk of cardiovascular disease: prospective cohort study. BMJ. 2017;357: j2099.
Google Scholar
SCORE2 Working Group and ESC Cardiovascular Risk Collaboration. SCORE2 risk prediction algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe. Eur Heart J. 2021;42(25):2439–54.
Google Scholar
Ishwaran H, Kogalur UB, Blackstone EH, Lauer MS. Random survival forests. arXiv [stat.AP]. 2008. http://arxiv.org/abs/0811.1645.
Pickett KL, Suresh K, Campbell KR, Davis S, Juarez-Colunga E. Random survival forests for dynamic predictions of a time-to-event outcome using a longitudinal biomarker. BMC Med Res Methodol. 2021;21(1):216.
Google Scholar
Gandin I, Saccani S, Coser A, Scagnetto A, Cappelletto C, Candido R, et al. Deep-learning-based prognostic modeling for incident heart failure in patients with diabetes using electronic health records: a retrospective cohort study. PLoS ONE. 2023;18(2): e0281878.
Google Scholar
Steyerberg EW, Vickers AJ, Cook NR, Gerds T, Gonen M, Obuchowski N, et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology. 2010;21(1):128–38.
Google Scholar
Van Calster B, McLernon DJ, van Smeden M, Wynants L, Steyerberg EW. Topic group ‘evaluating diagnostic tests and prediction models’ of the STRATOS initiative. Calibration: the Achilles heel of predictive analytics. BMC Med. 2019;17(1):230.
Google Scholar
Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Ann Intern Med. 2015;162(1):55–63.
Google Scholar
Fitzgerald M, Saville BR, Lewis RJ. Decision curve analysis. JAMA. 2015;313(4):409–10.
Google Scholar
Ghassemi M, Oakden-Rayner L, Beam AL. The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit Health. 2021;3(11):e745–50.
Google Scholar
Reddy S. Explainability and artificial intelligence in medicine. Lancet Digit Health. 2022;4(4):e214–5.
Google Scholar
Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell. 2019;1(5):206–15.
Google Scholar
ElSayed NA, Aleppo G, Aroda VR, Bannuru RR, Brown FM, Bruemmer D, et al. 2. Classification and diagnosis of diabetes: standards of care in diabetes-2023. Diabetes Care. 2023;46(Suppl 1):S19–40.
Google Scholar
US Preventive Services Task Force, Davidson KW, Barry MJ, Mangione CM, Cabana M, Caughey AB, et al. Screening for prediabetes and type 2 diabetes: US Preventive Services Task Force recommendation statement. JAMA. 2021;326(8):736–43.
Google Scholar
Bang H, Edwards AM, Bomback AS, Ballantyne CM, Brillon D, Callahan MA, et al. Development and validation of a patient self-assessment score for diabetes risk. Ann Intern Med. 2009;151(11):775–83.
Google Scholar
Chen L, Magliano DJ, Balkau B, Colagiuri S, Zimmet PZ, Tonkin AM, et al. AUSDRISK: an Australian type 2 diabetes risk assessment tool based on demographic, lifestyle and simple anthropometric measures. Med J Aust. 2010;192(4):197–202.
Google Scholar
Thomas C, Hyppönen E, Power C. Type 2 diabetes mellitus in midlife estimated from the Cambridge risk score and body mass index. Arch Intern Med. 2006;166(6):682–8.
Google Scholar
Stiglic G, Pajnkihar M. Evaluation of major online diabetes risk calculators and computerized predictive models. PLoS ONE. 2015;10(11): e0142827.
Google Scholar
Dinh A, Miertschin S, Young A, Mohanty SD. A data-driven approach to predicting diabetes and cardiovascular disease with machine learning. BMC Med Inform Decis Mak. 2019;19(1):1–15.
Google Scholar
Weisman A, Tu K, Young J, Kumar M, Austin PC, Jaakkimainen L, et al. Validation of a type 1 diabetes algorithm using electronic medical records and administrative healthcare data to study the population incidence and prevalence of type 1 diabetes in Ontario, Canada. BMJ Open Diabetes Res Care. 2020;8(1): e001224. https://doi.org/10.1136/bmjdrc-2020-001224.
Google Scholar
Carter TC, Rein D, Padberg I, Peter E, Rennefahrt U, David DE, et al. Validation of a metabolite panel for early diagnosis of type 2 diabetes. Metabolism. 2016;65(9):1399–408.
Google Scholar
Chen R, Mias GI, Li-Pook-Than J, Jiang L, Lam HYK, Chen R, et al. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell. 2012;148(6):1293–307.
Google Scholar
Zhou W, Sailani MR, Contrepois K, Zhou Y, Ahadi S, Leopold SR, et al. Longitudinal multi-omics of host-microbe dynamics in prediabetes. Nature. 2019;569(7758):663–71.
Google Scholar
Piening BD, Zhou W, Contrepois K, Röst H, Gu Urban GJ, Mishra T, et al. Integrative personal omics profiles during periods of weight gain and loss. Cell Syst. 2018;6(2):157-170.e8.
Google Scholar
Ahadi S, Zhou W, Schüssler-Fiorenza Rose SM, Sailani MR, Contrepois K, Avina M, et al. Personal aging markers and ageotypes revealed by deep longitudinal profiling. Nat Med. 2020;26(1):83–90.
Google Scholar
Schüssler-Fiorenza Rose SM, Contrepois K, Moneghetti KJ, Zhou W, Mishra T, Mataraso S, et al. A longitudinal big data approach for precision health. Nat Med. 2019;25(5):792–804.
Google Scholar
Cefalu WT, Andersen DK, Arreaza-Rubín G, Pin CL, Sato S, Verchere CB, et al. Heterogeneity of diabetes: β-cells, phenotypes, and precision medicine: proceedings of an international symposium of the Canadian Institutes of Health Research’s Institute of Nutrition, Metabolism and Diabetes and the U.S. National Institutes of Health’s National Institute of Diabetes and Digestive and Kidney Diseases. Diabetes. 2021. https://doi.org/10.2337/db21-0777.
Google Scholar
Ahlqvist E, Storm P, Käräjämäki A, Martinell M, Dorkhan M, Carlsson A, et al. Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables. Lancet Diabetes Endocrinol. 2018;6(5):361–9.
Google Scholar
Martinez-De la Torre A, Perez-Cruz F, Weiler S, Burden AM. Comorbidity clusters associated with newly treated type 2 diabetes mellitus: a Bayesian nonparametric analysis. Sci Rep. 2022;12(1):20653.
Google Scholar
Richesson RL, Rusincovitch SA, Wixted D, Batch BC, Feinglos MN, Miranda ML, et al. A comparison of phenotype definitions for diabetes mellitus. J Am Med Inform Assoc. 2013;20(e2):e319–26.
Google Scholar
Sarraju A, Zammit A, Ngo S, Witting C, Hernandez-Boussard T, Rodriguez F. Identifying reasons for statin nonuse in patients with diabetes using deep learning of electronic health records. J Am Heart Assoc. 2023;12(7): e028120.
Google Scholar
Bora A, Balasubramanian S, Babenko B, Virmani S, Venugopalan S, Mitani A, et al. Predicting the risk of developing diabetic retinopathy using deep learning. Lancet Digit Health. 2021;3(1):e10–9.
Google Scholar
Dai L, Wu L, Li H, Cai C, Wu Q, Kong H, et al. A deep learning system for detecting diabetic retinopathy across the disease spectrum. Nat Commun. 2021;12(1):3242.
Google Scholar
Ruamviboonsuk P, Tiwari R, Sayres R, Nganthavee V, Hemarat K, Kongprayoon A, et al. Real-time diabetic retinopathy screening by deep learning in a multisite national screening programme: a prospective interventional cohort study. Lancet Digit Health. 2022;4(4):e235–44.
Google Scholar
Nunez do Rio JM, Nderitu P, Raman R, Rajalakshmi R, Kim R, Rani PK, et al. Using deep learning to detect diabetic retinopathy on handheld non-mydriatic retinal images acquired by field workers in community settings. Sci Rep. 2023;13(1):1392.
Google Scholar
Young LH, Wackers FJT, Chyun DA, Davey JA, Barrett EJ, Taillefer R, et al. Cardiac outcomes after screening for asymptomatic coronary artery disease in patients with type 2 diabetes: the DIAD study: a randomized controlled trial. JAMA. 2009;301(15):1547–55.
Google Scholar
Malik S, Zhao Y, Budoff M, Nasir K, Blumenthal RS, Bertoni AG, et al. Coronary artery calcium score for long-term risk classification in individuals with type 2 diabetes and metabolic syndrome from the multi-ethnic study of atherosclerosis. JAMA Cardiol. 2017;2(12):1332–40.
Google Scholar
Blanke P, Naoum C, Ahmadi A, Cheruvu C, Soon J, Arepalli C, et al. Long-term prognostic utility of coronary CT angiography in stable patients with diabetes mellitus. JACC Cardiovasc Imaging. 2016;9(11):1280–8.
Google Scholar
Fan R, Zhang N, Yang L, Ke J, Zhao D, Cui Q. AI-based prediction for the risk of coronary heart disease among patients with type 2 diabetes mellitus. Sci Rep. 2020;10(1):14457.
Google Scholar
Hossain ME, Uddin S, Khan A. Network analytics and machine learning for predictive risk modelling of cardiovascular disease in patients with type 2 diabetes. Expert Syst Appl. 2021;164: 113918.
Google Scholar
Segar MW, Vaduganathan M, Patel KV, McGuire DK, Butler J, Fonarow GC, et al. Machine learning to predict the risk of incident heart failure hospitalization among patients with diabetes: the WATCH-DM risk score. Diabetes Care. 2019;42(12):2298–306.
Google Scholar
Kee OT, Harun H, Mustafa N, Abdul Murad NA, Chin SF, Jaafar R, et al. Cardiovascular complications in a diabetes prediction model using machine learning: a systematic review. Cardiovasc Diabetol. 2023;22(1):13.
Google Scholar
Stevens LM, Mortazavi BJ, Deo RC, Curtis L, Kao DP. Recommendations for reporting machine learning analyses in clinical research. Circ Cardiovasc Qual Outcomes. 2020;13(10): e006556.
Google Scholar
Luo W, Phung D, Tran T, Gupta S, Rana S, Karmakar C, et al. Guidelines for developing and reporting machine learning predictive models in biomedical research: a multidisciplinary view. J Med Internet Res. 2016;18(12): e323.
Google Scholar
Parikh RB, Teeple S, Navathe AS. Addressing bias in artificial intelligence in health care. JAMA. 2019;322(24):2377–8.
Google Scholar
Sng GGR, Tung JYM, Lim DYZ, Bee YM. Potential and pitfalls of ChatGPT and natural-language artificial intelligence models for diabetes education. Diabetes Care. 2023;46(5):e103–5.
Google Scholar
Lee Y-B, Kim G, Jun JE, Park H, Lee WJ, Hwang Y-C, et al. An integrated digital health care platform for diabetes management with AI-based dietary management: 48-week results from a randomized controlled trial. Diabetes Care. 2023;46(5):959–66.
Google Scholar
Attia ZI, Kapa S, Lopez-Jimenez F, McKie PM, Ladewig DJ, Satam G, et al. Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nat Med. 2019;25(1):70–4.
Google Scholar
Attia ZI, Noseworthy PA, Lopez-Jimenez F, Asirvatham SJ, Deshmukh AJ, Gersh BJ, et al. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet. 2019;394(10201):861–7.
Google Scholar
Cohen-Shelly M, Attia ZI, Friedman PA, Ito S, Essayagh BA, Ko W-Y, et al. Electrocardiogram screening for aortic valve stenosis using artificial intelligence. Eur Heart J. 2021;42(30):2885–96.
Google Scholar
Sangha V, Mortazavi BJ, Haimovich AD, Ribeiro AH, Brandt CA, Jacoby DL, et al. Automated multilabel diagnosis on electrocardiographic images and signals. Nat Commun. 2022;13(1):1583.
Google Scholar
Sangha V, Nargesi AA, Dhingra LS, Khunte A, Mortazavi BJ, Ribeiro AH, et al. Detection of left ventricular systolic dysfunction from electrocardiographic images. Circulation. 2023. https://doi.org/10.1161/CIRCULATIONAHA.122.062646.
Google Scholar
Khunte A, Sangha V, Oikonomou EK, Dhingra LS, Aminorroaya A, Mortazavi BJ, et al. Detection of left ventricular systolic dysfunction from single-lead electrocardiography adapted for portable and wearable devices. NPJ Digit Med. 2023;6(1):124.
Google Scholar
Porumb M, Stranges S, Pescapè A, Pecchia L. Precision medicine and artificial intelligence: a Pilot study on deep learning for hypoglycemic events detection based on ECG. Sci Rep. 2020;10(1):170.
Google Scholar
Lehmann V, Föll S, Maritsch M, van Weenen E, Kraus M, Lagger S, et al. Noninvasive hypoglycemia detection in people with diabetes using smartwatch data. Diabetes Care. 2023;46(5):993–7.
Google Scholar
Andellini M, Haleem S, Angelini M, Ritrovato M, Schiaffini R, Iadanza E, et al. Artificial intelligence for non-invasive glycaemic-events detection via ECG in a paediatric population: study protocol. Health Technol. 2023;13(1):145–54.
Google Scholar
Cisuelo O, Stokes K, Oronti IB, Haleem MS, Barber TM, Weickert MO, et al. Development of an artificial intelligence system to identify hypoglycaemia via ECG in adults with type 1 diabetes: protocol for data collection under controlled and free-living conditions. BMJ Open. 2023;13(4): e067899.
Google Scholar
Shahid S, Hussain S, Khan WA. Predicting continuous blood glucose level using deep learning. In: Proceedings of the 14th IEEE/ACM international conference on utility and cloud computing companion. New York: Association for Computing Machinery; 2022. p. 1–5. (UCC ’21).
Zhu T, Uduku C, Li K, Herrero P, Oliver N, Georgiou P. Enhancing self-management in type 1 diabetes with wearables and deep learning. NPJ Digit Med. 2022;5(1):78.
Google Scholar
Dhingra LS, Aminorroaya A, Oikonomou EK, Nargesi AA, Wilson FP, Krumholz HM, et al. Use of wearable devices in individuals with or at risk for cardiovascular disease in the US, 2019 to 2020. JAMA Netw Open. 2023;6(6): e2316634.
Google Scholar
Aminorroaya A, Dhingra LS, Nargesi AA, Oikonomou EK, Krumholz HM, Khera R. Use of smart devices to track cardiovascular health goals in the United States. JACC Adv. 2023;2(7): 100544.
Google Scholar
Bothwell LE, Podolsky SH. The emergence of the randomized, controlled trial. N Engl J Med. 2016;375(6):501–4.
Google Scholar
Künzel SR, Sekhon JS, Bickel PJ, Yu B. Metalearners for estimating heterogeneous treatment effects using machine learning. Proc Natl Acad Sci USA. 2019;116(10):4156–65.
Google Scholar
Rekkas A, Paulus JK, Raman G, Wong JB, Steyerberg EW, Rijnbeek PR, et al. Predictive approaches to heterogeneous treatment effects: a scoping review. BMC Med Res Methodol. 2020;20(1):264.
Google Scholar
Dennis JM, Henley WE, Weedon MN, Lonergan M, Rodgers LR, Jones AG, et al. Sex and BMI alter the benefits and risks of sulfonylureas and thiazolidinediones in type 2 diabetes: a framework for evaluating stratification using routine clinical and individual trial data. Diabetes Care. 2018;41(9):1844–53.
Google Scholar
Dennis JM, Shields BM, Hill AV, Knight BA, McDonald TJ, Rodgers LR, et al. Precision medicine in type 2 diabetes: clinical markers of insulin resistance are associated with altered short- and long-term glycemic response to DPP-4 inhibitor therapy. Diabetes Care. 2018;41(4):705–12.
Google Scholar
Zou X, Huang Q, Luo Y, Ren Q, Han X, Zhou X, et al. The efficacy of canagliflozin in diabetes subgroups stratified by data-driven clustering or a supervised machine learning method: a post hoc analysis of canagliflozin clinical trial data. Diabetologia. 2022;65(9):1424–35.
Google Scholar
Edward JA, Josey K, Bahn G, Caplan L, Reusch JEB, Reaven P, et al. Heterogeneous treatment effects of intensive glycemic control on major adverse cardiovascular events in the ACCORD and VADT trials: a machine-learning analysis. Cardiovasc Diabetol. 2022;21(1):58.
Google Scholar
Oikonomou EK, Van Dijk D, Parise H, Suchard MA, de Lemos J, Antoniades C, et al. A phenomapping-derived tool to personalize the selection of anatomical vs. functional testing in evaluating chest pain (ASSIST). Eur Heart J. 2021;42(26):2536–48.
Google Scholar
Sharma A, Coles A, Sekaran NK, Pagidipati NJ, Lu MT, Mark DB, et al. Stress testing versus CT angiography in patients with diabetes and suspected coronary artery disease. J Am Coll Cardiol. 2019;73(8):893–902.
Google Scholar
Oikonomou EK, Spatz ES, Suchard MA, Khera R. Individualising intensive systolic blood pressure reduction in hypertension using computational trial phenomaps and machine learning: a post-hoc analysis of randomised clinical trials. Lancet Digit Health. 2022;4(11):e796-805.
Google Scholar
Pallmann P, Bedding AW, Choodari-Oskooei B, Dimairo M, Flight L, Hampson LV, et al. Adaptive designs in clinical trials: why use them, and how to run and report them. BMC Med. 2018;16(1):29.
Google Scholar
Center for Drug Evaluation, Research. Adaptive design clinical trials for drugs and biologics guidance for industry. U.S. Food and Drug Administration. FDA. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/adaptive-design-clinical-trials-drugs-and-biologics-guidance-industry. Accessed 1 June 2022.
Oikonomou EK, Thangaraj PM, Bhatt DL, Ross JS, Young LH, Krumholz HM, et al. An explainable machine learning-based phenomapping strategy for adaptive predictive enrichment in randomized controlled trials. medRxiv. 2023. https://doi.org/10.1101/2023.06.18.23291542v1.abstract.
Google Scholar
Kernan WN, Viscoli CM, Furie KL, Young LH, Inzucchi SE, Gorman M, et al. Pioglitazone after ischemic stroke or transient ischemic attack. N Engl J Med. 2016;374(14):1321–31.
Google Scholar
SPRINT Research Group, Wright JT Jr, Williamson JD, Whelton PK, Snyder JK, Sink KM, et al. A randomized trial of intensive versus standard blood-pressure control. N Engl J Med. 2015;373(22):2103–16.
Google Scholar
Fogel DB. Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: s review. Contemp Clin Trials Commun. 2018;11:156–64.
Google Scholar
Bentley C, Cressman S, van der Hoek K, Arts K, Dancey J, Peacock S. Conducting clinical trials-costs, impacts, and the value of clinical trials networks: s scoping review. Clin Trials. 2019;16(2):183–93.
Google Scholar
Moore TJ, Zhang H, Anderson G, Alexander GC. Estimated costs of pivotal trials for novel therapeutic agents approved by the US Food and Drug Administration, 2015–2016. JAMA Intern Med. 2018;178(11):1451–7.
Google Scholar
Moore TJ, Heyward J, Anderson G, Alexander GC. Variation in the estimated costs of pivotal clinical benefit trials supporting the US approval of new therapeutic agents, 2015–2017: a cross-sectional study. BMJ Open. 2020;10(6): e038863.
Google Scholar
Khera R, Dhingra LS, Aminorroaya A, Li K, Zhou JJ, Arshad F, et al. Multinational patterns of second-line anti-hyperglycemic drug initiation across cardiovascular risk groups: a federated pharmacoepidemiologic evaluation in LEGEND-T2DM. medRxiv. 2022. https://doi.org/10.1101/2022.12.27.22283968v1.abstract.
Google Scholar
Khera R, Schuemie MJ, Lu Y, Ostropolets A, Chen R, Hripcsak G, et al. Large-scale evidence generation and evaluation across a network of databases for type 2 diabetes mellitus (LEGEND-T2DM): a protocol for a series of multinational, real-world comparative cardiovascular effectiveness and safety studies. BMJ Open. 2022;12(6): e057977.
Google Scholar
Djolonga J, Yung J, Tschannen M, Romijnders R, Beyer L, Kolesnikov A, et al. On robustness and transferability of convolutional neural networks. In: 2021 IEEE/CVF conference on computer vision and pattern recognition (CVPR). 2021. p. 16453–63.
Khera R, Haimovich J, Hurley NC, McNamara R, Spertus JA, Desai N, et al. Use of machine learning models to predict death after acute myocardial infarction. JAMA Cardiol. 2021;6(6):633–41.
Google Scholar
Volovici V, Syn NL, Ercole A, Zhao JJ, Liu N. Steps to avoid overuse and misuse of machine learning in clinical research. Nat Med. 2022;28(10):1996–9.
Google Scholar
Guo LL, Pfohl SR, Fries J, Posada J, Fleming SL, Aftandilian C, et al. Systematic review of approaches to preserve machine learning performance in the presence of temporal dataset shift in clinical medicine. Appl Clin Inform. 2021;12(4):808–15.
Google Scholar
Wong A, Otles E, Donnelly JP, Krumm A, McCullough J, DeTroyer-Cooley O, et al. External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. JAMA Intern Med. 2021;181(8):1065–70.
Google Scholar
Lundberg SM, Erion G, Chen H, DeGrave A, Prutkin JM, Nair B, et al. From local explanations to global understanding with explainable AI for trees. Nat Mach Intell. 2020;2(1):56–67.
Google Scholar
Linardatos P, Papastefanopoulos V, Kotsiantis S. Explainable AI: a review of machine learning interpretability methods. Entropy. 2020;23(1):18. https://doi.org/10.3390/e23010018.
Google Scholar
Diprose WK, Buist N, Hua N, Thurier Q, Shand G, Robinson R. Physician understanding, explainability, and trust in a hypothetical machine learning risk calculator. J Am Med Inform Assoc. 2020;27(4):592–600.
Google Scholar
Collins GS, Mallett S, Omar O, Yu L-M. Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting. BMC Med. 2011;9:103.
Google Scholar
Collins GS, de Groot JA, Dutton S, Omar O, Shanyinde M, Tajar A, et al. External validation of multivariable prediction models: a systematic review of methodological conduct and reporting. BMC Med Res Methodol. 2014;14:40.
Google Scholar
Chalmers I, Glasziou P. Avoidable waste in the production and reporting of research evidence. Lancet. 2009;374(9683):86–9.
Google Scholar
Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E, et al. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ. 2020;369: m1328.
Google Scholar
Christodoulou E, Ma J, Collins GS, Steyerberg EW, Verbakel JY, Van Calster B. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J Clin Epidemiol. 2019;110:12–22.
Google Scholar
Collins GS, Dhiman P, Andaur Navarro CL, Ma J, Hooft L, Reitsma JB, et al. Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open. 2021;11(7): e048008.
Google Scholar
Rajkomar A, Hardt M, Howell MD, Corrado G, Chin MH. Ensuring fairness in machine learning to advance health equity. Ann Intern Med. 2018;169(12):866–72.
Google Scholar
Mehrabi N, Morstatter F, Saxena N, Lerman K, Galstyan A. A survey on bias and fairness in machine learning. arXiv [cs.LG]. 2019. http://arxiv.org/abs/1908.09635.
Duffy G, Clarke SL, Christensen M, He B, Yuan N, Cheng S, et al. Confounders mediate AI prediction of demographics in medical imaging. NPJ Digit Med. 2022;5(1):188.
Google Scholar
Gichoya JW, Banerjee I, Bhimireddy AR, Burns JL, Celi LA, Chen L-C, et al. AI recognition of patient race in medical imaging: a modelling study. Lancet Digit Health. 2022;4(6):e406–14.
Google Scholar
Center for Devices, Radiological Health. Clinical decision support software—guidance. U.S. Food and Drug Administration. FDA; 2022. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/clinical-decision-support-software. Accessed 21 July 2023.
Johnston JL, Dhruva SS, Ross JS, Rathi VK. Clinical evidence supporting US Food and Drug Administration clearance of novel therapeutic devices via the de novo pathway between 2011 and 2019. JAMA Intern Med. 2020;180(12):1701–3.
Google Scholar
Kadakia KT, Dhruva SS, Caraballo C, Ross JS, Krumholz HM. Use of recalled devices in new device authorizations under the US Food and Drug Administration’s 510(k) pathway and risk of subsequent recalls. JAMA. 2023;329(2):136–43.
Google Scholar
Amann J, Blasimme A, Vayena E, Frey D, Madai VI, Precise4Q Consortium. Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC Med Inform Decis Mak. 2020;20(1):310.
Google Scholar
Yao X, Rushlow DR, Inselman JW, McCoy RG, Thacher TD, Behnken EM, et al. Artificial intelligence-enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial. Nat Med. 2021;27(5):815–9.
Google Scholar
He B, Kwan AC, Cho JH, Yuan N, Pollick C, Shiota T, et al. Blinded, randomized trial of sonographer versus AI cardiac function assessment. Nature. 2023;616(7957):520–4.
Google Scholar
Neal B, Perkovic V, Mahaffey KW, de Zeeuw D, Fulcher G, Erondu N, et al. Canagliflozin and cardiovascular and renal events in type 2 diabetes. N Engl J Med. 2017;377(7):644–57.
Google Scholar
