This paper is in the following e-collection/theme issue:

Clinical Information and Decision Making (1426) Decision Support for Health Professionals (1203) Electronic Health Records (1067) Machine Learning (1491) Hypertension Prevention and Treatment (228)

Published on in Vol 20, No 1 (2018): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/9268, first published .
Prediction of Incident Hypertension Within the Next Year: Prospective Study Using Statewide Electronic Health Records and Machine Learning

Prediction of Incident Hypertension Within the Next Year: Prospective Study Using Statewide Electronic Health Records and Machine Learning

Prediction of Incident Hypertension Within the Next Year: Prospective Study Using Statewide Electronic Health Records and Machine Learning

Authors of this article:

Chengyin Ye1, 2 Author Orcid Image ;   Tianyun Fu3 Author Orcid Image ;   Shiying Hao4, 5 Author Orcid Image ;   Yan Zhang6 Author Orcid Image ;   Oliver Wang3 Author Orcid Image ;   Bo Jin3 Author Orcid Image ;   Minjie Xia3 Author Orcid Image ;   Modi Liu3 Author Orcid Image ;   Xin Zhou7 Author Orcid Image ;   Qian Wu8 Author Orcid Image ;   Yanting Guo2, 9 Author Orcid Image ;   Chunqing Zhu3 Author Orcid Image ;   Yu-Ming Li7 Author Orcid Image ;   Devore S Culver10 Author Orcid Image ;   Shaun T Alfreds10 Author Orcid Image ;   Frank Stearns3 Author Orcid Image ;   Karl G Sylvester2 Author Orcid Image ;   Eric Widen3 Author Orcid Image ;   Doff McElhinney4, 5 Author Orcid Image ;   Xuefeng Ling2, 5, 11 Author Orcid Image
Article Authors Cited by (151) Tweetations (13) Metrics

Journals

  1. Chaikijurajai T, Laffin L, Tang W. Artificial Intelligence and Hypertension: Recent Advances and Future Outlook. American Journal of Hypertension 2020;33(11):967 View
  2. 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 View
  3. Qaffas A, 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 View
  4. Yoo T, Ryu I, Choi H, Kim J, Lee I, Kim J, Lee G, Rim T. 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 View
  5. Krittanawong C, Bomback A, Baber U, Bangalore S, Messerli F, Wilson Tang W. Future Direction for Using Artificial Intelligence to Predict and Manage Hypertension. Current Hypertension Reports 2018;20(9) View
  6. Vest J, 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 View
  7. 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 View
  8. Dworzynski P, Aasbrenn M, Rostgaard K, Melbye M, Gerds T, Hjalgrim H, Pers T. Nationwide prediction of type 2 diabetes comorbidities. Scientific Reports 2020;10(1) View
  9. 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 View
  10. 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 D, Alfreds S, Stearns F, Kanov L, Bhatia A, Sylvester K, Widen E, McElhinney D, Ling X. 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 View
  11. 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 View
  12. Sheikhalishahi S, Miotto R, Dudley J, Lavelli A, Rinaldi F, Osmani V. Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review. JMIR Medical Informatics 2019;7(2):e12239 View
  13. Golembiewski E, Allen K, Blackmon A, Hinrichs R, Vest J. Combining Nonclinical Determinants of Health and Clinical Data for Research and Evaluation: Rapid Review. JMIR Public Health and Surveillance 2019;5(4):e12846 View
  14. Virani S, Alonso A, Benjamin E, Bittencourt M, Callaway C, Carson A, Chamberlain A, Chang A, Cheng S, Delling F, Djousse L, Elkind M, Ferguson J, Fornage M, Khan S, Kissela B, Knutson K, Kwan T, Lackland D, Lewis T, Lichtman J, Longenecker C, Loop M, Lutsey P, Martin S, Matsushita K, Moran A, Mussolino M, Perak A, Rosamond W, Roth G, Sampson U, Satou G, Schroeder E, Shah S, Shay C, Spartano N, Stokes A, Tirschwell D, VanWagner L, Tsao C. Heart Disease and Stroke Statistics—2020 Update: A Report From the American Heart Association. Circulation 2020;141(9) View
  15. Wang X, Zhang Y, Hao S, Zheng L, Liao J, Ye C, Xia M, Wang O, Liu M, Weng C, Duong S, Jin B, Alfreds S, Stearns F, Kanov L, Sylvester K, Widen E, McElhinney D, Ling X. 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 View
  16. Shoenbill K, Song Y, Craven M, Johnson H, Smith M, Mendonca E. Identifying patterns and predictors of lifestyle modification in electronic health record documentation using statistical and machine learning methods. Preventive Medicine 2020;136:106061 View
  17. Park J, Kim J, Ryu B, Heo E, Jung S, Yoo S. Patient-Level Prediction of Cardio-Cerebrovascular Events in Hypertension Using Nationwide Claims Data. Journal of Medical Internet Research 2019;21(2):e11757 View
  18. Barth M, Emrich E, Güllich A. A Machine Learning Approach to “Revisit” Specialization and Sampling in Institutionalized Practice. Sage Open 2019;9(2) View
  19. 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 View
  20. 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 View
  21. 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 View
  22. 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) View
  23. Ye C, Li J, Hao S, Liu M, Jin H, Zheng L, Xia M, Jin B, Zhu C, Alfreds S, Stearns F, Kanov L, Sylvester K, Widen E, McElhinney D, Ling X. 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 View
  24. Chiavegatto Filho A, Batista A, dos Santos H. 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 View
  25. Virani S, Alonso A, Aparicio H, Benjamin E, Bittencourt M, Callaway C, Carson A, Chamberlain A, Cheng S, Delling F, Elkind M, Evenson K, Ferguson J, Gupta D, Khan S, Kissela B, Knutson K, Lee C, Lewis T, Liu J, Loop M, Lutsey P, Ma J, Mackey J, Martin S, Matchar D, Mussolino M, Navaneethan S, Perak A, Roth G, Samad Z, Satou G, Schroeder E, Shah S, Shay C, Stokes A, VanWagner L, Wang N, Tsao C. Heart Disease and Stroke Statistics—2021 Update. Circulation 2021;143(8) View
  26. 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 View
  27. 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 View
  28. Tsoi K, Yiu K, Lee H, Cheng H, Wang T, Tay J, Teo B, Turana Y, Soenarta A, Sogunuru G, 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 View
  29. 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 View
  30. Padmanabhan S, Tran T, Dominiczak A. Artificial Intelligence in Hypertension. Circulation Research 2021;128(7):1100 View
  31. 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 View
  32. 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 View
  33. 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 View
  34. Kang E, Ryu I, Lee G, Kim J, Lee I, Jeon G, Song H, Kamiya K, Yoo T. 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 View
  35. Jones I, Van Oyen M, Lavieri M, Andrews C, Stein J. Predicting rapid progression phases in glaucoma using a soft voting ensemble classifier exploiting Kalman filtering. Health Care Management Science 2021;24(4):686 View
  36. Abrar S, Loo C, Kubota N. A Multi-Agent Approach for Personalized Hypertension Risk Prediction. IEEE Access 2021;9:75090 View
  37. 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 View
  38. 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 View
  39. 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 View
  40. 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 View
  41. 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 View
  42. Kharb S, Joshi A. Multi-omics and machine learning for the prevention and management of female reproductive health. Frontiers in Endocrinology 2023;14 View
  43. Gusev A, Novitskiy R, Ivshin A, Alekseev A. Machine learning based on laboratory data for disease prediction. FARMAKOEKONOMIKA. Modern Pharmacoeconomics and Pharmacoepidemiology 2021;14(4):581 View
  44. King J, Pinheiro L, Bryan Ringel J, Bress A, Shimbo D, Muntner P, Reynolds K, Cushman M, Howard G, Manly J, Safford M. Multiple Social Vulnerabilities to Health Disparities and Hypertension and Death in the REGARDS Study. Hypertension 2022;79(1):196 View
  45. Kulzer B. Künstliche Intelligenz (KI) in der Diabetologie – jetzt und in der Zukunft. Die Diabetologie 2023;19(1):35 View
  46. Duong S, Zheng L, Xia M, Jin B, Liu M, Li Z, Hao S, Alfreds S, Sylvester K, Widen E, Teuteberg J, McElhinney D, Ling X, Mordaunt D. 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 View
  47. 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 View
  48. Chen Y, Hao L, Zou V, Hollander Z, Ng R, Isaac K. Automated medical chart review for breast cancer outcomes research: a novel natural language processing extraction system. BMC Medical Research Methodology 2022;22(1) View
  49. 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 View
  50. Islam S, Talukder A, Awal M, Siddiqui M, Ahamad M, Ahammed B, Rawal L, Alizadehsani R, Abawajy J, Laranjo L, Chow C, 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 View
  51. 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) View
  52. Kulzer B. Wie profitieren Menschen mit Diabetes von Big Data und künstlicher Intelligenz?. Der Diabetologe 2021;17(8):799 View
  53. Chowdhury M, Leung A, Walker R, Sikdar K, O’Beirne M, Quan H, Turin T. 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) View
  54. 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 View
  55. 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 View
  56. Ramón A, Torres A, 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 View
  57. Bear Don’t Walk O, Reyes Nieva H, Lee S, Elhadad N. A scoping review of ethics considerations in clinical natural language processing. JAMIA Open 2022;5(2) View
  58. Hossain E, Rana R, Higgins N, Soar J, Barua P, Pisani A, 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 View
  59. Nguyen H, Vasconcellos H, Keck K, Reis J, Lewis C, Sidney S, Lloyd-Jones D, Schreiner P, Guallar E, Wu C, Lima J, 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) View
  60. 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 View
  61. 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 View
  62. Cai A, Zhu Y, Clarkson S, Feng Y. The Use of Machine Learning for the Care of Hypertension and Heart Failure. JACC: Asia 2021;1(2):162 View
  63. Drake C, Lewinski A, Rader A, Schexnayder J, Bosworth H, Goldstein K, Gierisch J, White-Clark C, McCant F, Zullig L. Addressing Hypertension Outcomes Using Telehealth and Population Health Managers: Adaptations and Implementation Considerations. Current Hypertension Reports 2022;24(8):267 View
  64. Nelson C, Bove R, Butte A, Baranzini S. 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 View
  65. Mateo-Sotos J, Torres A, Santos J, Quevedo O, Basar C. A Machine Learning-Based Method to Identify Bipolar Disorder Patients. Circuits, Systems, and Signal Processing 2022;41(4):2244 View
  66. Yoo T, Ryu I, Kim J, Lee I, Kim H. 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 View
  67. 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 View
  68. 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 View
  69. Datta S, Morassi Sasso A, Kiwit N, Bose S, Nadkarni G, Miotto R, Böttinger E. Predicting hypertension onset from longitudinal electronic health records with deep learning. JAMIA Open 2022;5(4) View
  70. Manlhiot C, van den Eynde J, Kutty S, Ross H. 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 View
  71. Tsao C, Aday A, Almarzooq Z, Alonso A, Beaton A, Bittencourt M, Boehme A, Buxton A, Carson A, Commodore-Mensah Y, Elkind M, Evenson K, Eze-Nliam C, Ferguson J, Generoso G, Ho J, Kalani R, Khan S, Kissela B, Knutson K, Levine D, Lewis T, Liu J, Loop M, Ma J, Mussolino M, Navaneethan S, Perak A, Poudel R, Rezk-Hanna M, Roth G, Schroeder E, Shah S, Thacker E, VanWagner L, Virani S, Voecks J, Wang N, Yaffe K, Martin S. Heart Disease and Stroke Statistics—2022 Update: A Report From the American Heart Association. Circulation 2022;145(8) View
  72. Asgari P, Bozorgi Z. 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 View
  73. Xue C, Li C, Hu J, Wei C, Wang H, Ahemaitijiang K, Zhang Q, Chen D, Zhang C, Li F, Zhang J, Jonas J, Wang Y. Retinal vessel caliber and tortuosity and prediction of 5-year incidence of hypertension. Journal of Hypertension 2023 View
  74. Busnatu Ș, Niculescu A, Bolocan A, Petrescu G, Păduraru D, Năstasă I, Lupușoru M, Geantă M, Andronic O, Grumezescu A, Martins H. Clinical Applications of Artificial Intelligence—An Updated Overview. Journal of Clinical Medicine 2022;11(8):2265 View
  75. 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 View
  76. Kanyongo W, Ezugwu A. Machine learning approaches to medication adherence amongst NCD patients: A systematic literature review. Informatics in Medicine Unlocked 2023;38:101210 View
  77. 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) View
  78. Visco V, Izzo C, Mancusi C, Rispoli A, Tedeschi M, Virtuoso N, Giano A, Gioia R, Melfi A, Serio B, Rusciano M, 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 View
  79. 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 View
  80. Gusev I, Gavrilov D, Novitsky R, Kuznetsova T, Boytsov S. Improvement of cardiovascular risk assessment using machine learning methods. Russian Journal of Cardiology 2021;26(12):4618 View
  81. Suri J, Bhagawati M, Paul S, Protogerou A, Sfikakis P, Kitas G, Khanna N, Ruzsa Z, Sharma A, Saxena S, Faa G, Laird J, Johri A, Kalra M, Paraskevas K, 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 View
  82. Yoo T, Ryu I, Kim J, Lee I. Deep learning for predicting uncorrected refractive error using posterior segment optical coherence tomography images. Eye 2022;36(10):1959 View
  83. 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 View
  84. 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 View
  85. Lee S, Kim H. Prospect of Artificial Intelligence Based on Electronic Medical Records. Journal of Lipid and Atherosclerosis 2021;10(3):282 View
  86. Silva G, Fagundes T, Teixeira B, Chiavegatto Filho A. Machine Learning for Hypertension Prediction: a Systematic Review. Current Hypertension Reports 2022;24(11):523 View
  87. 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 View
  88. Yang J, Liu F, Wang B, Chen C, Church T, Dukes L, Smith J. Blood Pressure States Transition Inference Based on Multi-State Markov Model. IEEE Journal of Biomedical and Health Informatics 2021;25(1):237 View
  89. 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 View
  90. Chen S, Xue X, Zhang Y, Zhang H, Huang X, Chen X, Deng G, Luo S, Gao J, Theis K, Cai Z. Vaginal Atopobium is Associated with Spontaneous Abortion in the First Trimester: a Prospective Cohort Study in China. Microbiology Spectrum 2022;10(2) View
  91. 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 View
  92. 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 View
  93. 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) View
  94. Kaveshnikov V, Bragin D, Vaizov V, Kaveshnikov A, Kuzmichkina M, Trubacheva I. POSSIBILITIES OF APPLYING MACHINE LEARNING TECHNOLOGIES IN THE SPHERE OF PRIMARY PREVENTION OF CARDIOVASCULAR DISEASES. Complex Issues of Cardiovascular Diseases 2023;12(3):109 View
  95. Manga S, Muthavarapu N, Redij R, Baraskar B, Kaur A, Gaddam S, Gopalakrishnan K, Shinde R, Rajagopal A, Samaddar P, Damani D, Shivaram S, Dey S, Mitra D, Roy S, Kulkarni K, Arunachalam S. Estimation of Physiologic Pressures: Invasive and Non-Invasive Techniques, AI Models, and Future Perspectives. Sensors 2023;23(12):5744 View
  96. McNeill E, Lindenfeld Z, Mostafa L, Zein D, Silver D, Pagán J, Weeks W, Aerts A, Des Rosiers S, Boch J, Chang J. 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) View
  97. 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 View
  98. Stafie C, Sufaru I, Ghiciuc C, Stafie I, Sufaru E, Solomon S, Hancianu M. Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review. Diagnostics 2023;13(12):1995 View
  99. El-Sherbini A, Hassan Virk H, Wang Z, Glicksberg B, Krittanawong C. Machine-Learning-Based Prediction Modelling in Primary Care: State-of-the-Art Review. AI 2023;4(2):437 View
  100. Zhang T, Tan T, Wang X, Gao Y, Han L, Balkenende L, D’Angelo A, Bao L, Horlings H, Teuwen J, Beets-Tan R, Mann R. RadioLOGIC, a healthcare model for processing electronic health records and decision-making in breast disease. Cell Reports Medicine 2023;4(8):101131 View
  101. Jeong J, Han H, Ro D, 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 View
  102. Borna S, Maniaci M, Haider C, Maita K, Torres-Guzman R, Avila F, Lunde J, Coffey J, Demaerschalk B, Forte A. Artificial Intelligence Models in Health Information Exchange: A Systematic Review of Clinical Implications. Healthcare 2023;11(18):2584 View
  103. 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;83(20):59085 View
  104. 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 View
  105. Gudigar A, Kadri N, Raghavendra U, Samanth J, Maithri M, Inamdar M, Prabhu M, Hegde A, Salvi M, Yeong C, Barua P, Molinari F, Acharya U. 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 View
  106. Schjerven F, Lindseth F, Steinsland I, Behnoush A. Prognostic risk models for incident hypertension: A PRISMA systematic review and meta-analysis. PLOS ONE 2024;19(3):e0294148 View
  107. Almansouri N, Awe M, Rajavelu S, Jahnavi K, Shastry R, Hasan A, Hasan H, Lakkimsetti M, AlAbbasi R, Gutiérrez B, Haider A. Early Diagnosis of Cardiovascular Diseases in the Era of Artificial Intelligence: An In-Depth Review. Cureus 2024 View
  108. Schjerven F, Ingeström E, Steinsland I, Lindseth F. Development of risk models of incident hypertension using machine learning on the HUNT study data. Scientific Reports 2024;14(1) View
  109. 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 View
  110. Yang W, Yi D, Zhou X, Leng Y. Translational analysis of data science and causal learning in real-world clinical evaluation of traditional Chinese medicine. Science of Traditional Chinese Medicine 2024;2(1):57 View
  111. Park H, Jung S, Han M, Jang Y, Moon Y, Kim T, Shin S, Hwang H. Lowering Barriers to Health Risk Assessments in Promoting Personalized Health Management. Journal of Personalized Medicine 2024;14(3):316 View
  112. Wei C, Wang J, Yu P, Li A, Xiong Z, Yuan Z, Yu L, Luo J. Comparison of different machine learning classification models for predicting deep vein thrombosis in lower extremity fractures. Scientific Reports 2024;14(1) View
  113. Layton A. AI, Machine Learning, and ChatGPT in Hypertension. Hypertension 2024;81(4):709 View
  114. Zheng H, Sherazi S, Lee J. A cost-sensitive deep neural network-based prediction model for the mortality in acute myocardial infarction patients with hypertension on imbalanced data. Frontiers in Cardiovascular Medicine 2024;11 View
  115. Armoundas A, Narayan S, Arnett D, Spector-Bagdady K, Bennett D, Celi L, Friedman P, Gollob M, Hall J, Kwitek A, Lett E, Menon B, Sheehan K, Al-Zaiti S. Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association. Circulation 2024;149(14) View
  116. Cho J, Park J. Application of artificial intelligence in hypertension. Clinical Hypertension 2024;30(1) View
  117. Kapoor S, Cantrell E, Peng K, Pham T, Bail C, Gundersen O, Hofman J, Hullman J, Lones M, Malik M, Nanayakkara P, Poldrack R, Raji I, Roberts M, Salganik M, Serra-Garcia M, Stewart B, Vandewiele G, Narayanan A. REFORMS: Consensus-based Recommendations for Machine-learning-based Science. Science Advances 2024;10(18) View
  118. Kulvinder Singh , Dhawan S, Mehla D. Performance Evaluation of Machine Learning Models for Multiple Chronic Disease Diagnosis Using Symptom Data. Automatic Control and Computer Sciences 2024;58(2):195 View
  119. Kaur S, Gulati H, Baldi A. Digitalization of hypertension management: a paradigm shift. Naunyn-Schmiedeberg's Archives of Pharmacology 2024;397(11):8477 View
  120. Seedat N, Imrie F, Schaar M. Navigating Data-Centric Artificial Intelligence With DC-Check: Advances, Challenges, and Opportunities. IEEE Transactions on Artificial Intelligence 2024;5(6):2589 View
  121. Norrman A, Hasselström J, Ljunggren G, Wachtler C, Eriksson J, Kahan T, Wändell P, Gudjonsdottir H, Lindblom S, Ruge T, Rosenblad A, Brynedal B, Carlsson A. Predicting new cases of hypertension in Swedish primary care with a machine learning tool. Preventive Medicine Reports 2024;44:102806 View
  122. Juyal A, Bisht S, Singh M. Smart solutions in hypertension diagnosis and management: a deep dive into artificial intelligence and modern wearables for blood pressure monitoring. Blood Pressure Monitoring 2024;29(5):260 View
  123. 钟 玮. Risk Prediction of Hematoma Expansion in Hemorrhagic Stroke Patients Based on XGBoost Algorithm. Modeling and Simulation 2024;13(04):4271 View
  124. Jahangir Z, Muddassir Qureshi S, Abdul Rehman Y, Ur Rehman Shah S, Ahmed Qureshi H, Ahmad A. Revolutionizing AI-driven Hypertension Care: A Review of Current Trends and Future Directions. Journal of Science & Technology 2024;5(4):99 View
  125. Wang T, Tan J, Wang T, Xiang S, Zhang Y, Jian C, Jian J, Zhao W. A Real-World Study on the Short-Term Efficacy of Amlodipine in Treating Hypertension Among Inpatients. Pragmatic and Observational Research 2024;Volume 15:121 View
  126. Chen S, Yu J, Chamouni S, Wang Y, Li Y. Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions. BMC Medicine 2024;22(1) View
  127. Guerreiro J, Garriga R, Lozano Bagén T, Sharma B, Karnik N, Matić A. Transatlantic transferability and replicability of machine-learning algorithms to predict mental health crises. npj Digital Medicine 2024;7(1) View
  128. Kario K, Williams B, Tomitani N, McManus R, Schutte A, Avolio A, Shimbo D, Wang J, Khan N, Picone D, Tan I, Charlton P, Satoh M, Mmopi K, Lopez-Lopez J, Bothe T, Bianchini E, Bhandari B, Lopez-Rivera J, Charchar F, Tomaszewski M, Stergiou G. Innovations in blood pressure measurement and reporting technology: International Society of Hypertension position paper endorsed by the World Hypertension League, European Society of Hypertension, Asian Pacific Society of Hypertension, and Latin American Society of Hypertension. Journal of Hypertension 2024;42(11):1874 View
  129. Li C, Mowery D, Ma X, Yang R, Vurgun U, Hwang S, Donnelly H, Bandhey H, Senathirajah Y, Visweswaran S, Sadhu E, Akhtar Z, Getzen E, Freda P, Long Q, Becich M. Realizing the potential of social determinants data in EHR systems: A scoping review of approaches for screening, linkage, extraction, analysis, and interventions. Journal of Clinical and Translational Science 2024;8(1) View
  130. Nguyen H, Anderson W, Chou S, McWilliams A, Zhao J, Pajewski N, Taylor Y. Predictive Models for Sustained, Uncontrolled Hypertension and Hypertensive Crisis Based on Electronic Health Record Data: Algorithm Development and Validation. JMIR Medical Informatics 2024;12:e58732 View
  131. Singh C, Sodhi K. Targeting bioinformatics tools to study the dissemination and spread of antibiotic resistant genes in the environment and clinical settings. Critical Reviews in Microbiology 2024:1 View
  132. Hussain S, Ahmad S, Wasid M. Artificial intelligence-driven intelligent learning models for identification and prediction of cardioneurological disorders: A comprehensive study. Computers in Biology and Medicine 2025;184:109342 View
  133. Adeleke O, Adebayo S, Aworinde H, Adeleke O, Adeniyi A, Aroba O. Machine learning evaluation of a hypertension screening program in a university workforce over five years. Scientific Reports 2024;14(1) View

Books/Policy Documents

  1. Cho P, Singh K, Dunn J. Artificial Intelligence in Medicine. View
  2. Srivani M, Mala T, Murugappan A. Handbook of Research on Emerging Trends and Applications of Machine Learning. View
  3. Chaturvedi A, Srivastava S, Rai A, Cheema A, Chelimela D, Aravindakshan R. Evolving Technologies for Computing, Communication and Smart World. View
  4. Koshimizu H, Okuno Y. Artificial Intelligence in Medicine. View
  5. Koshimizu H, Okuno Y. Artificial Intelligence in Medicine. View
  6. S. Allen K, Gilliam N, Kharrazi H, McPheeters M, Dixon B. Health Information Exchange. View
  7. El Sherbini A, Glicksberg B, Krittanawong C. Artificial Intelligence in Clinical Practice. View
  8. Kumar R, Adatia A, Wander G, Sahani A. Proceedings of International Conference on Frontiers in Computing and Systems. View
  9. Deorankar P, Vaidya V, Munot N, Jain K, Patil A. Biosystems, Biomedical & Drug Delivery Systems. View
  10. Ongwere T, Rutuja N, Nguyen T. Intelligent Computing. View
  11. Lee S, Leung F, Wong W, Chang C, Liu T, Tse G. Internet of Things and Machine Learning for Type I and Type II Diabetes. View