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

Clinical Information and Decision Making (1915) Decision Support for Health Professionals (1539) Electronic Health Records (1319) Machine Learning (2076) Hypertension Prevention and Treatment (296)

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 (188) Tweetations (13) Metrics

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  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
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  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 2025;51(5):860 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
  134. Varga D. Mitigating Data Leakage in a WiFi CSI Benchmark for Human Action Recognition. Sensors 2024;24(24):8201 View
  135. Huang C, Long X, van der Ven M, Kaptein M, Oei S, van den Heuvel E. Predicting preterm birth using electronic medical records from multiple prenatal visits. BMC Pregnancy and Childbirth 2024;24(1) View
  136. Yoshimura K, Hirota Y, Saito S, Nishikage S, Yamamoto A, Takayoshi T, Urai S, Ogawa W. Prediction of Insulin Requirements by Explainable Machine Learning for Individuals With Type 1 Diabetes. The Journal of Clinical Endocrinology & Metabolism 2025;110(9):e3093 View
  137. Dreyfuss M, Getz B, Lebwohl B, Ramni O, Underberger D, Ber T, Steinberg-Koch S, Jenudi Y, Gazit S, Patalon T, Chodick G, Shoenfeld Y, Ben-Tov A. A machine learning tool for early identification of celiac disease autoimmunity. Scientific Reports 2024;14(1) View
  138. Patel A, Kankva D, Prajapati P. Revolutionizing Hypertension Management: AI-Powered Precision Medicine Approaches. International Journal of Pharmaceutical Sciences and Nanotechnology(IJPSN) 2024;17(6):7714 View
  139. Tanaka M, Akiyama Y, Mori K, Hosaka I, Endo K, Ogawa T, Sato T, Suzuki T, Yano T, Ohnishi H, Hanawa N, Furuhashi M. Machine learning-based analyses of contributing factors for the development of hypertension: a comparative study. Clinical and Experimental Hypertension 2025;47(1) View
  140. Lee H, Park M, Won Y. AI Machine Learning–Based Diabetes Prediction in Older Adults in South Korea: Cross-Sectional Analysis. JMIR Formative Research 2025;9:e57874 View
  141. Clifford N, Tunis R, Ariyo A, Yu H, Rhee H, Radhakrishnan K. Trends and Gaps in Digital Precision Hypertension Management: Scoping Review. Journal of Medical Internet Research 2025;27:e59841 View
  142. Yu N, Shin D, Ryu I, Yoo T, Koh K. Retinal vein occlusion risk prediction without fundus examination using a no-code machine learning tool for tabular data: a nationwide cross-sectional study from South Korea. BMC Medical Informatics and Decision Making 2025;25(1) View
  143. Jayakumar S, verma V, Rout U. Advanced Predictive Modeling for Hypertension Risk Based on Health Indicators and Machine Learning Technique. Seminars in Medical Writing and Education 2023;2:128 View
  144. Deng M, Guo J, Li B, Yang J, Zhang X, Liang D, Wang Y, Huang X. Intelligent risk stratification of hypertension based on ambulatory blood pressure monitoring and machine learning algorithms. Physiological Measurement 2025;46(3):035001 View
  145. Cheng Y, Gu K, Ji W, Hu Z, Yang Y, Zhou Y. Two-Year Hypertension Incidence Risk Prediction in Populations in the Desert Regions of Northwest China: Prospective Cohort Study. Journal of Medical Internet Research 2025;27:e68442 View
  146. Armoundas A, Ahmad F, Attia Z, Doudesis D, Khera R, Kyriakoulis K, Stergiou G, Tang W. Controversy in Hypertension: Pro-Side of the Argument Using Artificial Intelligence for Hypertension Diagnosis and Management. Hypertension 2025;82(6):929 View
  147. Chuang Y, Sarkar A, Hsu Y, Mohammed N, Jiang X. Robust privacy amidst innovation with large language models through a critical assessment of the risks. Journal of the American Medical Informatics Association 2025;32(5):885 View
  148. Wang Y, Deng R, Geng X. Exploring the integration of medical and preventive chronic disease health management in the context of big data. Frontiers in Public Health 2025;13 View
  149. Indolfi C, Agostoni P, Barillà F, Barison A, Benenati S, Bilo G, Boriani G, Brunetti N, Calabrò P, Carugo S, Casella M, Ciccarelli M, Ciccone M, Ferrari G, Greco G, Esposito G, Locati E, Mariani A, Merlo M, Muscoli S, Nodari S, Olivotto I, Paolillo S, Polimeni A, Porcari A, Porto I, Spaccarotella C, Vizza C, Leone N, Sinagra G, Filardi P, Curcio A. Expert consensus document on artificial intelligence of the Italian Society of Cardiology. Journal of Cardiovascular Medicine 2025;26(5):200 View
  150. Karnik A, Yang E. Artificial Intelligence to Improve Blood Pressure Control: A State-of-the-Art Review. American Journal of Hypertension 2025;38(8):519 View
  151. CHEEMA B, HOURMOZDI J, KLINE A, AHMAD F, KHERA R. Artificial Intelligence in the Management of Heart Failure. Journal of Cardiac Failure 2025;31(10):1561 View
  152. Layton A. A Heart-to-Heart With ChatGPT: AI Applications in Hypertension. American Journal of Hypertension 2025;38(9):621 View
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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
  12. Nenova Z, Foster K. Reference Module in Social Sciences. View
  13. Gregory J. Enhancing Biomedical Education. View
  14. Orsolini L, Fiorani M, Longo G, Volpe U. Comorbidity between Mental and Physical Disorders. View
  15. Majumder A, Sanyal M, Mondal A, Banerjee P. Smart Systems and Wireless Communication. View

Conference Proceedings

  1. Nasir N, Oswald P, Barneih F, Alshaltone O, AlShabi M, Bonny T, Shammaa A. 2021 14th International Conference on Developments in eSystems Engineering (DeSE). Hypertension Classification Using Machine Learning Part II View
  2. Gozali A. 2023 10th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE). Hypertension Multi-Year Prediction and Risk Factors Analysis Using Decision Tree View
  3. Gupta A, Rathi M. Proceedings of the 2023 Fifteenth International Conference on Contemporary Computing. Disentangling the Hidden Patterns of Heart Disease: A Factor Analysis and Machine Learning Approach for Early Detection and Prevention View
  4. Li Y, Cao W, Chang W, Zhou S, Zhang R. Proceedings of the 4th International Conference on Advanced Information Science and System. An XGBoost risk prediction model of cardiovascular and cerebrovascular diseases with plateau healthcare dataset View
  5. You J, Li J, Li X, Li H, Tu J, Zhang Y, Gao J, Wu J, Ye J. 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). Risk-prediction model for incident hypertension in patients with obstructive sleep apnea based on SpO2 signals View
  6. Wang X, Liu M, McElhinney D, T Alfreds S, Han S, Zhou J, B Ling X. 2023 2nd International Conference on Machine Learning, Cloud Computing and Intelligent Mining (MLCCIM). A Prospective Investigation into the Predictive Modeling of Dementia Disease Risk Using Electronic Medical Records from Maine View
  7. Patil S, Madasu S, Rolla K, Gupta K, Yuvaraj N. 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). Examining the Potential of Machine Learning in Reducing Prescription Drug Costs View
  8. Bhoi A, Talmale G, Khobragade P. 2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA). Prediction Model for Preterm Birth Using Deep Learning View