Published on 30.01.18 in Vol 20, No 1 (2018): January
Works citing "Prediction of Incident Hypertension Within the Next Year: Prospective Study Using Statewide Electronic Health Records and Machine Learning"
According to Crossref, the following articles are citing this article (DOI 10.2196/jmir.9268):
(note that this is only a small subset of citations)
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Chaikijurajai T, Laffin LJ, Tang WHW. Artificial Intelligence and Hypertension: Recent Advances and Future Outlook. American Journal of Hypertension 2020;33(11):967
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Junwei K, Yang H, Junjiang L, Zhijun Y. Dynamic prediction of cardiovascular disease using improved LSTM. International Journal of Crowd Science 2019;3(1):14
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Qaffas AA, Hoque R, Almazmomi N. The Internet of Things and Big Data Analytics for Chronic Disease Monitoring in Saudi Arabia. Telemedicine and e-Health 2021;27(1):74
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Yoo TK, Ryu IH, Choi H, Kim JK, Lee IS, Kim JS, Lee G, Rim TH. Explainable Machine Learning Approach as a Tool to Understand Factors Used to Select the Refractive Surgery Technique on the Expert Level. Translational Vision Science & Technology 2020;9(2):8
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Krittanawong C, Bomback AS, Baber U, Bangalore S, Messerli FH, Wilson Tang WH. Future Direction for Using Artificial Intelligence to Predict and Manage Hypertension. Current Hypertension Reports 2018;20(9)
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Vest JR, Ben-Assuli O. Prediction of emergency department revisits using area-level social determinants of health measures and health information exchange information. International Journal of Medical Informatics 2019;129:205
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Jin Q, Xue X, Peng W, Cai W, Zhang Y, Zhang L. TBLC-rAttention: A Deep Neural Network Model for Recognizing the Emotional Tendency of Chinese Medical Comment. IEEE Access 2020;8:96811
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Dworzynski P, Aasbrenn M, Rostgaard K, Melbye M, Gerds TA, Hjalgrim H, Pers TH. Nationwide prediction of type 2 diabetes comorbidities. Scientific Reports 2020;10(1)
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Wang H, Liang C, Li Y. Application of Basic Epidemiologic Principles and Electronic Health Records in a Deep Learning Prediction Model—Reply. JAMA Dermatology 2020;156(4):474
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Guo Y, Zheng G, Fu T, Hao S, Ye C, Zheng L, Liu M, Xia M, Jin B, Zhu C, Wang O, Wu Q, Culver DS, Alfreds ST, Stearns F, Kanov L, Bhatia A, Sylvester KG, Widen E, McElhinney DB, Ling XB. Assessing Statewide All-Cause Future One-Year Mortality: Prospective Study With Implications for Quality of Life, Resource Utilization, and Medical Futility. Journal of Medical Internet Research 2018;20(6):e10311
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Li Z, Liu X, Zhang Z, Huang L, Zhong Q, He R, Chen P, Li A, Liang J, Lei J. Epidemiology of Hypertension in a Typical State-Level Poverty-Stricken County in China and Evaluation of a Whole Population Health Prevention Project Intervention. International Journal of Hypertension 2019;2019:1
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Sheikhalishahi S, Miotto R, Dudley JT, Lavelli A, Rinaldi F, Osmani V. Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review. JMIR Medical Informatics 2019;7(2):e12239
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Golembiewski E, Allen KS, Blackmon AM, Hinrichs RJ, Vest JR. Combining Nonclinical Determinants of Health and Clinical Data for Research and Evaluation: Rapid Review. JMIR Public Health and Surveillance 2019;5(4):e12846
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Virani SS, Alonso A, Benjamin EJ, Bittencourt MS, Callaway CW, Carson AP, Chamberlain AM, Chang AR, Cheng S, Delling FN, Djousse L, Elkind MS, Ferguson JF, Fornage M, Khan SS, Kissela BM, Knutson KL, Kwan TW, Lackland DT, Lewis TT, Lichtman JH, Longenecker CT, Loop MS, Lutsey PL, Martin SS, Matsushita K, Moran AE, Mussolino ME, Perak AM, Rosamond WD, Roth GA, Sampson UK, Satou GM, Schroeder EB, Shah SH, Shay CM, Spartano NL, Stokes A, Tirschwell DL, VanWagner LB, Tsao CW. Heart Disease and Stroke Statistics—2020 Update: A Report From the American Heart Association. Circulation 2020;141(9)
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Wang X, Zhang Y, Hao S, Zheng L, Liao J, Ye C, Xia M, Wang O, Liu M, Weng CH, Duong SQ, Jin B, Alfreds ST, Stearns F, Kanov L, Sylvester KG, Widen E, McElhinney DB, Ling XB. Prediction of the 1-Year Risk of Incident Lung Cancer: Prospective Study Using Electronic Health Records from the State of Maine. Journal of Medical Internet Research 2019;21(5):e13260
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Shoenbill K, Song Y, Craven M, Johnson H, Smith M, Mendonca EA. Identifying patterns and predictors of lifestyle modification in electronic health record documentation using statistical and machine learning methods. Preventive Medicine 2020;136:106061
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Park J, Kim J, Ryu B, Heo E, Jung SY, Yoo S. Patient-Level Prediction of Cardio-Cerebrovascular Events in Hypertension Using Nationwide Claims Data. Journal of Medical Internet Research 2019;21(2):e11757
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Barth M, Emrich E, Güllich A. A Machine Learning Approach to “Revisit” Specialization and Sampling in Institutionalized Practice. SAGE Open 2019;9(2):215824401984055
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Wang H, Wang Y, Liang C, Li Y. Assessment of Deep Learning Using Nonimaging Information and Sequential Medical Records to Develop a Prediction Model for Nonmelanoma Skin Cancer. JAMA Dermatology 2019;155(11):1277
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Kanegae H, Suzuki K, Fukatani K, Ito T, Harada N, Kario K. Highly precise risk prediction model for new‐onset hypertension using artificial intelligence techniques. The Journal of Clinical Hypertension 2020;22(3):445
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Chang W, Liu Y, Xiao Y, Yuan X, Xu X, Zhang S, Zhou S. A Machine-Learning-Based Prediction Method for Hypertension Outcomes Based on Medical Data. Diagnostics 2019;9(4):178
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Makino M, Yoshimoto R, Ono M, Itoko T, Katsuki T, Koseki A, Kudo M, Haida K, Kuroda J, Yanagiya R, Saitoh E, Hoshinaga K, Yuzawa Y, Suzuki A. Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning. Scientific Reports 2019;9(1)
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Ye C, Li J, Hao S, Liu M, Jin H, Zheng L, Xia M, Jin B, Zhu C, Alfreds ST, Stearns F, Kanov L, Sylvester KG, Widen E, McElhinney D, Ling XB. Identification of elders at higher risk for fall with statewide electronic health records and a machine learning algorithm. International Journal of Medical Informatics 2020;137:104105
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Chiavegatto Filho A, Batista AFDM, dos Santos HG. Data Leakage in Health Outcomes Prediction With Machine Learning. Comment on “Prediction of Incident Hypertension Within the Next Year: Prospective Study Using Statewide Electronic Health Records and Machine Learning”. Journal of Medical Internet Research 2021;23(2):e10969
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Virani SS, Alonso A, Aparicio HJ, Benjamin EJ, Bittencourt MS, Callaway CW, Carson AP, Chamberlain AM, Cheng S, Delling FN, Elkind MS, Evenson KR, Ferguson JF, Gupta DK, Khan SS, Kissela BM, Knutson KL, Lee CD, Lewis TT, Liu J, Loop MS, Lutsey PL, Ma J, Mackey J, Martin SS, Matchar DB, Mussolino ME, Navaneethan SD, Perak AM, Roth GA, Samad Z, Satou GM, Schroeder EB, Shah SH, Shay CM, Stokes A, VanWagner LB, Wang N, Tsao CW. Heart Disease and Stroke Statistics—2021 Update. Circulation 2021;143(8)
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Chen M, Tan X, Padman R. Social determinants of health in electronic health records and their impact on analysis and risk prediction: A systematic review. Journal of the American Medical Informatics Association 2020;27(11):1764
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Diao X, Huo Y, Yan Z, Wang H, Yuan J, Wang Y, Cai J, Zhao W. An Application of Machine Learning to Etiological Diagnosis of Secondary Hypertension: Retrospective Study Using Electronic Medical Records. JMIR Medical Informatics 2021;9(1):e19739
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Tsoi K, Yiu K, Lee H, Cheng H, Wang T, Tay J, Teo BW, Turana Y, Soenarta AA, Sogunuru GP, Siddique S, Chia Y, Shin J, Chen C, Wang J, Kario K. Applications of artificial intelligence for hypertension management. The Journal of Clinical Hypertension 2021;23(3):568
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CAI J, ZHA M, SONG Y, CHEN H. Predicting the Development of Surgery-Related Pressure Injury Using a Machine Learning Algorithm Model. Journal of Nursing Research 2021;29(1):e135
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Padmanabhan S, Tran TQB, Dominiczak AF. Artificial Intelligence in Hypertension. Circulation Research 2021;128(7):1100
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Wang L, Niu D, Wang X, Khan J, Shen Q, Xue Y. A Novel Machine Learning Strategy for the Prediction of Antihypertensive Peptides Derived from Food with High Efficiency. Foods 2021;10(3):550
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López Bernal S, Martínez Valverde J, Huertas Celdrán A, Martínez Pérez G. SENIOR: An Intelligent Web-Based Ecosystem to Predict High Blood Pressure Adverse Events Using Biomarkers and Environmental Data. Applied Sciences 2021;11(6):2506
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Chang W, Ji X, Xiao Y, Zhang Y, Chen B, Liu H, Zhou S. Prediction of Hypertension Outcomes Based on Gain Sequence Forward Tabu Search Feature Selection and XGBoost. Diagnostics 2021;11(5):792
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Kang EM, Ryu IH, Lee G, Kim JK, Lee IS, Jeon GH, Song H, Kamiya K, Yoo TK. Development of a Web-Based Ensemble Machine Learning Application to Select the Optimal Size of Posterior Chamber Phakic Intraocular Lens. Translational Vision Science & Technology 2021;10(6):5
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Jones IA, Van Oyen MP, Lavieri MS, Andrews CA, Stein JD. Predicting rapid progression phases in glaucoma using a soft voting ensemble classifier exploiting Kalman filtering. Health Care Management Science 2021;24(4):686
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Abrar S, Loo CK, Kubota N. A Multi-Agent Approach for Personalized Hypertension Risk Prediction. IEEE Access 2021;9:75090
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Mateo J, Rius-Peris J, Maraña-Pérez A, Valiente-Armero A, Torres A. Extreme gradient boosting machine learning method for predicting medical treatment in patients with acute bronchiolitis. Biocybernetics and Biomedical Engineering 2021;41(2):792
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Martinez-Ríos E, Montesinos L, Alfaro-Ponce M, Pecchia L. A review of machine learning in hypertension detection and blood pressure estimation based on clinical and physiological data. Biomedical Signal Processing and Control 2021;68:102813
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Vest J, Kasthurirathne S, Ge W, Gutta J, Ben-Assuli O, Halverson P. Choice of measurement approach for area-level social determinants of health and risk prediction model performance. Informatics for Health and Social Care 2021;:1
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Lin C, Li C, Liu C, Lin C, Wang M, Yang S, Li T. A risk scoring system to predict the risk of new‐onset hypertension among patients with type 2 diabetes. The Journal of Clinical Hypertension 2021;23(8):1570
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Abba M, Nduka C, Anjorin S, Mohamed S, Agogo E, Uthman O. One Hundred Years of Hypertension Research: Topic Modeling Study. JMIR Formative Research 2022;6(5):e31292
CrossRef -
Kharb S, Joshi A. Multi-omics and machine learning for the prevention and management of female reproductive health. Frontiers in Endocrinology 2023;14
CrossRef -
Gusev AV, Novitskiy RE, Ivshin AA, Alekseev AA. Machine learning based on laboratory data for disease prediction. FARMAKOEKONOMIKA. Modern Pharmacoeconomics and Pharmacoepidemiology 2021;14(4):581
CrossRef -
King JB, Pinheiro LC, Bryan Ringel J, Bress AP, Shimbo D, Muntner P, Reynolds K, Cushman M, Howard G, Manly JJ, Safford MM. Multiple Social Vulnerabilities to Health Disparities and Hypertension and Death in the REGARDS Study. Hypertension 2022;79(1):196
CrossRef -
. Künstliche Intelligenz (KI) in der Diabetologie – jetzt und in der Zukunft. Die Diabetologie 2023;19(1):35
CrossRef -
Duong SQ, Zheng L, Xia M, Jin B, Liu M, Li Z, Hao S, Alfreds ST, Sylvester KG, Widen E, Teuteberg JJ, McElhinney DB, Ling XB, Mordaunt DA. Identification of patients at risk of new onset heart failure: Utilizing a large statewide health information exchange to train and validate a risk prediction model. PLOS ONE 2021;16(12):e0260885
CrossRef -
Zhang Y, Chen S, Chen X, Zhang H, Huang X, Xue X, Guo Y, Ruan X, Liu X, Deng G, Luo S, Gao J. Association Between Vaginal Gardnerella and Tubal Pregnancy in Women With Symptomatic Early Pregnancies in China: A Nested Case-Control Study. Frontiers in Cellular and Infection Microbiology 2022;11
CrossRef -
Chen Y, Hao L, Zou VZ, Hollander Z, Ng RT, Isaac KV. Automated medical chart review for breast cancer outcomes research: a novel natural language processing extraction system. BMC Medical Research Methodology 2022;22(1)
CrossRef -
Kotsyfakis S, Iliaki-Giannakoudaki E, Anagnostopoulos A, Papadokostaki E, Giannakoudakis K, Goumenakis M, Kotsyfakis M. The application of machine learning to imaging in hematological oncology: A scoping review. Frontiers in Oncology 2022;12
CrossRef -
Islam SMS, Talukder A, Awal MA, Siddiqui MMU, Ahamad MM, Ahammed B, Rawal LB, Alizadehsani R, Abawajy J, Laranjo L, Chow CK, Maddison R. Machine Learning Approaches for Predicting Hypertension and Its Associated Factors Using Population-Level Data From Three South Asian Countries. Frontiers in Cardiovascular Medicine 2022;9
CrossRef -
Kim G, Lim H, Kim Y, Kwon O, Choi J. Intra-person multi-task learning method for chronic-disease prediction. Scientific Reports 2023;13(1)
CrossRef -
. Wie profitieren Menschen mit Diabetes von Big Data und künstlicher Intelligenz?. Der Diabetologe 2021;17(8):799
CrossRef -
Chowdhury MZI, Leung AA, Walker RL, Sikdar KC, O’Beirne M, Quan H, Turin TC. A comparison of machine learning algorithms and traditional regression-based statistical modeling for predicting hypertension incidence in a Canadian population. Scientific Reports 2023;13(1)
CrossRef -
Zhu C, Xu Z, Gu Y, Zheng S, Sun X, Cao J, Song B, Jin J, Liu Y, Wen X, Cheng S, Li J, Wu X. Prediction of post-stroke urinary tract infection risk in immobile patients using machine learning: an observational cohort study. Journal of Hospital Infection 2022;122:96
CrossRef -
Garriga R, Mas J, Abraha S, Nolan J, Harrison O, Tadros G, Matic A. Machine learning model to predict mental health crises from electronic health records. Nature Medicine 2022;28(6):1240
CrossRef -
Ramón A, Torres AM, Milara J, Cascón J, Blasco P, Mateo J. eXtreme Gradient Boosting-based method to classify patients with COVID-19. Journal of Investigative Medicine 2022;70(7):1472
CrossRef -
Bear Don’t Walk OJ, Reyes Nieva H, Lee SS, Elhadad N. A scoping review of ethics considerations in clinical natural language processing. JAMIA Open 2022;5(2)
CrossRef -
Hossain E, Rana R, Higgins N, Soar J, Barua PD, Pisani AR, Turner K. Natural Language Processing in Electronic Health Records in relation to healthcare decision-making: A systematic review. Computers in Biology and Medicine 2023;155:106649
CrossRef -
Nguyen HT, Vasconcellos HD, Keck K, Reis JP, Lewis CE, Sidney S, Lloyd-Jones DM, Schreiner PJ, Guallar E, Wu CO, Lima JA, Ambale-Venkatesh B. Multivariate longitudinal data for survival analysis of cardiovascular event prediction in young adults: insights from a comparative explainable study. BMC Medical Research Methodology 2023;23(1)
CrossRef -
Ren L, Zhang H, Sekhari Seklouli A, Wang T, Bouras A. Stacking-based multi-objective ensemble framework for prediction of hypertension. Expert Systems with Applications 2023;215:119351
CrossRef -
Abdullah Alfayez A, Kunz H, Grace Lai A. Predicting the risk of cancer in adults using supervised machine learning: a scoping review. BMJ Open 2021;11(9):e047755
CrossRef -
Cai A, Zhu Y, Clarkson SA, Feng Y. The Use of Machine Learning for the Care of Hypertension and Heart Failure. JACC: Asia 2021;1(2):162
CrossRef -
Drake C, Lewinski AA, Rader A, Schexnayder J, Bosworth HB, Goldstein KM, Gierisch J, White-Clark C, McCant F, Zullig LL. Addressing Hypertension Outcomes Using Telehealth and Population Health Managers: Adaptations and Implementation Considerations. Current Hypertension Reports 2022;24(8):267
CrossRef -
Nelson CA, Bove R, Butte AJ, Baranzini SE. Embedding electronic health records onto a knowledge network recognizes prodromal features of multiple sclerosis and predicts diagnosis. Journal of the American Medical Informatics Association 2022;29(3):424
CrossRef -
Mateo-Sotos J, Torres AM, Santos JL, Quevedo O, Basar C. A Machine Learning-Based Method to Identify Bipolar Disorder Patients. Circuits, Systems, and Signal Processing 2022;41(4):2244
CrossRef -
Yoo TK, Ryu IH, Kim JK, Lee IS, Kim HK. A deep learning approach for detection of shallow anterior chamber depth based on the hidden features of fundus photographs. Computer Methods and Programs in Biomedicine 2022;219:106735
CrossRef -
Luo Y, Henry S, Wang Y, Shen F, Uzuner O, Rumshisky A. The 2019 n2c2/UMass Lowell shared task on clinical concept normalization. Journal of the American Medical Informatics Association 2020;27(10):1529
CrossRef -
Sarwar T, Seifollahi S, Chan J, Zhang X, Aksakalli V, Hudson I, Verspoor K, Cavedon L. The Secondary Use of Electronic Health Records for Data Mining: Data Characteristics and Challenges. ACM Computing Surveys 2023;55(2):1
CrossRef -
Datta S, Morassi Sasso A, Kiwit N, Bose S, Nadkarni G, Miotto R, Böttinger EP. Predicting hypertension onset from longitudinal electronic health records with deep learning. JAMIA Open 2022;5(4)
CrossRef -
Manlhiot C, van den Eynde J, Kutty S, Ross HJ. A Primer on the Present State and Future Prospects for Machine Learning and Artificial Intelligence Applications in Cardiology. Canadian Journal of Cardiology 2022;38(2):169
CrossRef -
Tsao CW, Aday AW, Almarzooq ZI, Alonso A, Beaton AZ, Bittencourt MS, Boehme AK, Buxton AE, Carson AP, Commodore-Mensah Y, Elkind MS, Evenson KR, Eze-Nliam C, Ferguson JF, Generoso G, Ho JE, Kalani R, Khan SS, Kissela BM, Knutson KL, Levine DA, Lewis TT, Liu J, Loop MS, Ma J, Mussolino ME, Navaneethan SD, Perak AM, Poudel R, Rezk-Hanna M, Roth GA, Schroeder EB, Shah SH, Thacker EL, VanWagner LB, Virani SS, Voecks JH, Wang N, Yaffe K, Martin SS. Heart Disease and Stroke Statistics—2022 Update: A Report From the American Heart Association. Circulation 2022;145(8)
CrossRef -
Asgari P, Bozorgi ZD. The Effectiveness of Healthy Lifestyle Training and Existential Therapy on Distress Tolerance, Health Concerns and Blood Pressure in Elderly People with Hypertension. Current Psychology 2023;42(16):13951
CrossRef -
Xue CC, Li C, Hu JF, Wei CC, Wang H, Ahemaitijiang K, Zhang Q, Chen DN, Zhang C, Li F, Zhang J, Jonas JB, Wang YX. Retinal vessel caliber and tortuosity and prediction of 5-year incidence of hypertension. Journal of Hypertension 2023;41(5):830
CrossRef -
Busnatu , Niculescu A, Bolocan A, Petrescu GED, Păduraru DN, Năstasă I, Lupușoru M, Geantă M, Andronic O, Grumezescu AM, Martins H. Clinical Applications of Artificial Intelligence—An Updated Overview. Journal of Clinical Medicine 2022;11(8):2265
CrossRef -
Van den Eynde J, Lachmann M, Laugwitz K, Manlhiot C, Kutty S. Successfully implemented artificial intelligence and machine learning applications in cardiology: State-of-the-art review. Trends in Cardiovascular Medicine 2023;33(5):265
CrossRef -
Kanyongo W, Ezugwu AE. Machine learning approaches to medication adherence amongst NCD patients: A systematic literature review. Informatics in Medicine Unlocked 2023;38:101210
CrossRef -
Su D, Li Q, Zhang T, Veliz P, Chen Y, He K, Mahajan P, Zhang X. Prediction of acute appendicitis among patients with undifferentiated abdominal pain at emergency department. BMC Medical Research Methodology 2022;22(1)
CrossRef -
Visco V, Izzo C, Mancusi C, Rispoli A, Tedeschi M, Virtuoso N, Giano A, Gioia R, Melfi A, Serio B, Rusciano MR, Di Pietro P, Bramanti A, Galasso G, D’Angelo G, Carrizzo A, Vecchione C, Ciccarelli M. Artificial Intelligence in Hypertension Management: An Ace up Your Sleeve. Journal of Cardiovascular Development and Disease 2023;10(2):74
CrossRef -
Yang J, Ju X, Liu F, Asan O, Church T, Smith J. Prediction for the Risk of Multiple Chronic Conditions Among Working Population in the United States With Machine Learning Models. IEEE Open Journal of Engineering in Medicine and Biology 2021;2:291
CrossRef -
Gusev IV, Gavrilov DV, Novitsky RE, Kuznetsova TY, Boytsov SA. Improvement of cardiovascular risk assessment using machine learning methods. Russian Journal of Cardiology 2021;26(12):4618
CrossRef -
Suri JS, Bhagawati M, Paul S, Protogerou AD, Sfikakis PP, Kitas GD, Khanna NN, Ruzsa Z, Sharma AM, Saxena S, Faa G, Laird JR, Johri AM, Kalra MK, Paraskevas KI, Saba L. A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review. Diagnostics 2022;12(3):722
CrossRef -
Yoo TK, Ryu IH, Kim JK, Lee IS. Deep learning for predicting uncorrected refractive error using posterior segment optical coherence tomography images. Eye 2022;36(10):1959
CrossRef -
Chen N, Fan F, Geng J, Yang Y, Gao Y, Jin H, Chu Q, Yu D, Wang Z, Shi J. Evaluating the risk of hypertension in residents in primary care in Shanghai, China with machine learning algorithms. Frontiers in Public Health 2022;10
CrossRef -
Berg K, Doktorchik C, Quan H, Saini V. Automating data collection methods in electronic health record systems: a Social Determinant of Health (SDOH) viewpoint. Health Systems 2023;12(4):472
CrossRef -
Lee S, Kim H. Prospect of Artificial Intelligence Based on Electronic Medical Records. Journal of Lipid and Atherosclerosis 2021;10(3):282
CrossRef -
Silva GFS, Fagundes TP, Teixeira BC, Chiavegatto Filho ADP. Machine Learning for Hypertension Prediction: a Systematic Review. Current Hypertension Reports 2022;24(11):523
CrossRef -
Kumar K, Kumar P, Deb D, Unguresan M, Muresan V. Artificial Intelligence and Machine Learning Based Intervention in Medical Infrastructure: A Review and Future Trends. Healthcare 2023;11(2):207
CrossRef -
Yang J, Liu F, Wang B, Chen C, Church T, Dukes L, Smith JO. Blood Pressure States Transition Inference Based on Multi-State Markov Model. IEEE Journal of Biomedical and Health Informatics 2021;25(1):237
CrossRef -
Mu T, Xu R, Zhu Q, Chen L, Dong D, Xu J, Shen C. Diet-related knowledge, attitudes, and behaviors among young and middle-aged individuals with high-normal blood pressure: A cross-sectional study in China. Frontiers in Public Health 2022;10
CrossRef -
Chen S, Xue X, Zhang Y, Zhang H, Huang X, Chen X, Deng G, Luo S, Gao J, Theis KR, Cai Z. Vaginal
Atopobium
is Associated with Spontaneous Abortion in the First Trimester: a Prospective Cohort Study in China. Microbiology Spectrum 2022;10(2)
CrossRef -
Peng H, Duan S, Pan L, Wang M, Chen J, Wang Y, Yao S. Development and validation of machine learning models for nonalcoholic fatty liver disease. Hepatobiliary & Pancreatic Diseases International 2023;22(6):615
CrossRef -
Kao Y, Huang C, Fang Y, Liu J, Chang T. Machine Learning-Based Prediction of Atrial Fibrillation Risk Using Electronic Medical Records in Older Aged Patients. The American Journal of Cardiology 2023;198:56
CrossRef -
Zhang Y, Du S, Hu T, Xu S, Lu H, Xu C, Li J, Zhu X. Establishment of a model for predicting preterm birth based on the machine learning algorithm. BMC Pregnancy and Childbirth 2023;23(1)
CrossRef -
Kaveshnikov VS, Bragin DS, Vaizov VK, Kaveshnikov AV, Kuzmichkina MA, Trubacheva IA. POSSIBILITIES OF APPLYING MACHINE LEARNING TECHNOLOGIES IN THE SPHERE OF PRIMARY PREVENTION OF CARDIOVASCULAR DISEASES. Complex Issues of Cardiovascular Diseases 2023;12(3):109
CrossRef -
Manga S, Muthavarapu N, Redij R, Baraskar B, Kaur A, Gaddam S, Gopalakrishnan K, Shinde R, Rajagopal A, Samaddar P, Damani DN, Shivaram S, Dey S, Mitra D, Roy S, Kulkarni K, Arunachalam SP. Estimation of Physiologic Pressures: Invasive and Non-Invasive Techniques, AI Models, and Future Perspectives. Sensors 2023;23(12):5744
CrossRef -
McNeill E, Lindenfeld Z, Mostafa L, Zein D, Silver D, Pagán J, Weeks WB, Aerts A, Des Rosiers S, Boch J, Chang JE. Uses of Social Determinants of Health Data to Address Cardiovascular Disease and Health Equity: A Scoping Review. Journal of the American Heart Association 2023;12(21)
CrossRef -
Guo S, Ge J, Liu S, Zhou J, Li C, Chen H, Chen L, Shen Y, Zhou Q. Development of a convenient and effective hypertension risk prediction model and exploration of the relationship between Serum Ferritin and Hypertension Risk: a study based on NHANES 2017—March 2020. Frontiers in Cardiovascular Medicine 2023;10
CrossRef -
Stafie CS, Sufaru I, Ghiciuc CM, Stafie I, Sufaru E, Solomon SM, Hancianu M. Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review. Diagnostics 2023;13(12):1995
CrossRef -
El-Sherbini AH, Hassan Virk HU, Wang Z, Glicksberg BS, Krittanawong C. Machine-Learning-Based Prediction Modelling in Primary Care: State-of-the-Art Review. AI 2023;4(2):437
CrossRef -
Zhang T, Tan T, Wang X, Gao Y, Han L, Balkenende L, D’Angelo A, Bao L, Horlings HM, Teuwen J, Beets-Tan RG, Mann RM. RadioLOGIC, a healthcare model for processing electronic health records and decision-making in breast disease. Cell Reports Medicine 2023;4(8):101131
CrossRef -
Jeong J, Han H, Ro DH, Han H, Won S. Development of Prediction Model Using Machine-Learning Algorithms for Nonsteroidal Anti-inflammatory Drug-Induced Gastric Ulcer in Osteoarthritis Patients: Retrospective Cohort Study of a Nationwide South Korean Cohort. Clinics in Orthopedic Surgery 2023;15(4):678
CrossRef -
Borna S, Maniaci MJ, Haider CR, Maita KC, Torres-Guzman RA, Avila FR, Lunde JJ, Coffey JD, Demaerschalk BM, Forte AJ. Artificial Intelligence Models in Health Information Exchange: A Systematic Review of Clinical Implications. Healthcare 2023;11(18):2584
CrossRef -
Zhao Y, Han J, Hu X, Hu B, Zhu H, Wang Y, Zhu X. Hypertension risk prediction models for patients with diabetes based on machine learning approaches. Multimedia Tools and Applications 2023;
CrossRef -
Zhu Q, Mu T, Dong D, Chen L, Xu J, Shen C, Komlaga G. Renin-angiotensin system mechanism underlying the effect of auricular acupuncture on blood pressure in hypertensive patients with phlegm-dampness constitution: Study protocol for a randomized controlled trial. PLOS ONE 2024;19(2):e0294306
CrossRef -
Gudigar A, Kadri NA, Raghavendra U, Samanth J, Maithri M, Inamdar MA, Prabhu MA, Hegde A, Salvi M, Yeong CH, Barua PD, Molinari F, Acharya UR. Automatic identification of hypertension and assessment of its secondary effects using artificial intelligence: A systematic review (2013–2023). Computers in Biology and Medicine 2024;172:108207
CrossRef -
Schjerven FE, Lindseth F, Steinsland I, Behnoush AH. Prognostic risk models for incident hypertension: A PRISMA systematic review and meta-analysis. PLOS ONE 2024;19(3):e0294148
CrossRef -
Almansouri NE, Awe M, Rajavelu S, Jahnavi K, Shastry R, Hasan A, Hasan H, Lakkimsetti M, AlAbbasi RK, Gutiérrez BC, Haider A. Early Diagnosis of Cardiovascular Diseases in the Era of Artificial Intelligence: An In-Depth Review. Cureus 2024;
CrossRef -
Schjerven FE, Ingeström EML, Steinsland I, Lindseth F. Development of risk models of incident hypertension using machine learning on the HUNT study data. Scientific Reports 2024;14(1)
CrossRef -
Chen J, Yuan D, Dong R, Cai J, Ai Z, Zhou S. Artificial intelligence significantly facilitates development in the mental health of college students: a bibliometric analysis. Frontiers in Psychology 2024;15
CrossRef -
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