[1] |
Gulati M, Levy PD, Mukherjee D, et al. 2021 AHA/ACC/ASE/CHEST/SAEM/SCCT/SCMR Guideline for the Evaluation and Diagnosis of Chest Pain: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation, 2021; 144, e368−454. |
[2] |
Six AJ, Backus BE, Kelder JC. Chest pain in the emergency room: value of the HEART score. Neth Heart J, 2008; 16, 191−6. doi: 10.1007/BF03086144 |
[3] |
Pollack CV Jr. , Sites FD, Shofer FS, et al. Application of the TIMI risk score for unstable angina and non-ST elevation acute coronary syndrome to an unselected emergency department chest pain population. Acad Emerg Med, 2006; 13, 13−8. doi: 10.1197/j.aem.2005.06.031 |
[4] |
Aarts GWA, Camaro C, van Geuns RJ, et al. Acute rule-out of non-ST-segment elevation acute coronary syndrome in the (pre)hospital setting by HEART score assessment and a single point-of-care troponin: rationale and design of the ARTICA randomised trial. BMJ Open, 2020; 10, e034403. doi: 10.1136/bmjopen-2019-034403 |
[5] |
Gibbs J, McCord J. Chest Pain Evaluation in the Emergency Department: Risk Scores and High-Sensitivity Cardiac Troponin. Curr Cardiol Rep, 2020; 22, 49. doi: 10.1007/s11886-020-01294-1 |
[6] |
Ma CP, Wang X, Wang QS, et al. A modified HEART risk score in chest pain patients with suspected non-ST-segment elevation acute coronary syndrome. J Geriatr Cardiol, 2016; 13, 64−9. |
[7] |
Atwood J. Management of Acute Coronary Syndrome. Emerg Med Clin North Am, 2022; 40, 693−706. doi: 10.1016/j.emc.2022.06.008 |
[8] |
Schwalbe N, Wahl B. Artificial intelligence and the future of global health. Lancet, 2020; 395, 1579−86. doi: 10.1016/S0140-6736(20)30226-9 |
[9] |
Ross EG, Shah NH, Dalman RL, et al. The use of machine learning for the identification of peripheral artery disease and future mortality risk. J Vasc Surg, 2016; 64, 1515-22. e3. doi: 10.1016/j.jvs.2016.04.026 |
[10] |
Ng ACT, Delgado V, Bax JJ. Individualized Patient Risk Stratification Using Machine Learning and Topological Data Analysis. JACC Cardiovasc Imaging, 2020; 13, 1133−4. doi: 10.1016/j.jcmg.2020.02.003 |
[11] |
Assaf D, Gutman Y, Neuman Y, et al. Utilization of machine-learning models to accurately predict the risk for critical COVID-19. Intern Emerg Med, 2020; 15, 1435−43. doi: 10.1007/s11739-020-02475-0 |
[12] |
Stark GF, Hart GR, Nartowt BJ, et al. Predicting breast cancer risk using personal health data and machine learning models. PloS one, 2019; 14, e0226765. doi: 10.1371/journal.pone.0226765 |
[13] |
Alizadehsani R, Abdar M, Roshanzamir M, et al. Machine learning-based coronary artery disease diagnosis: A comprehensive review. Comput Biol Med, 2019; 111, 103346. doi: 10.1016/j.compbiomed.2019.103346 |
[14] |
Noh YK, Park JY, Choi BG, et al. A Machine Learning-Based Approach for the Prediction of Acute Coronary Syndrome Requiring Revascularization. J Med Syst, 2019; 43, 253. doi: 10.1007/s10916-019-1359-5 |
[15] |
Motwani M, Dey D, Berman DS, et al. 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, 2017; 38, 500−7. |
[16] |
Sherazi SWA, Jeong YJ, Jae MH, Bae JW, Lee JY. A machine learning-based 1-year mortality prediction model after hospital discharge for clinical patients with acute coronary syndrome. Health Informatics J, 2020; 26, 1289−304. doi: 10.1177/1460458219871780 |
[17] |
Juan-Salvadores P, Veiga C, Díaz VAJ, et al. Using Machine Learning Techniques to Predict MACE in Very Young Acute Coronary Syndrome Patients. Diagnostics (Basel), 2022; 12, 422. doi: 10.3390/diagnostics12020422 |
[18] |
González-Juanatey C, Anguita-Sánchez M, Barrios V, et al. Major Adverse Cardiovascular Events in Coronary Type 2 Diabetic Patients: Identification of Associated Factors Using Electronic Health Records and Natural Language Processing. J Clin Med, 2022; 11, 6004. doi: 10.3390/jcm11206004 |
[19] |
Peng XR, Zhu T, Wang T, et al. Machine learning prediction of postoperative major adverse cardiovascular events in geriatric patients: a prospective cohort study. BMC Anesthesiol, 2022; 22, 284. doi: 10.1186/s12871-022-01827-x |
[20] |
Connor CW. Artificial Intelligence and Machine Learning in Anesthesiology. Anesthesiology, 2019;131, 1346−59. doi: 10.1097/ALN.0000000000002694 |
[21] |
Backus BE, Six AJ, Kelder JC, et al. A prospective validation of the HEART score for chest pain patients at the emergency department. Int J Cardiol, 2013; 168, 2153−8. doi: 10.1016/j.ijcard.2013.01.255 |
[22] |
Al'Aref SJ, Anchouche K, Singh G, et al. Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. Eur Heart J, 2019; 40, 1975−86. doi: 10.1093/eurheartj/ehy404 |
[23] |
D'Ascenzo F, De Filippo O, Gallone G, et al. Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets. Lanc et, 2021; 397, 199−207. doi: 10.1016/S0140-6736(20)32519-8 |
[24] |
Wu X, Yuan X, Wang W, et al. Value of a Machine Learning Approach for Predicting Clinical Outcomes in Young Patients With Hypertension. Hypertension, 2020; 75, 1271−8. doi: 10.1161/HYPERTENSIONAHA.119.13404 |