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

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):215824401984055 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 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

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