Published on in Vol 21 , No 2 (2019) :February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/11757, first published .
Patient-Level Prediction of Cardio-Cerebrovascular Events in Hypertension Using Nationwide Claims Data

Patient-Level Prediction of Cardio-Cerebrovascular Events in Hypertension Using Nationwide Claims Data

Patient-Level Prediction of Cardio-Cerebrovascular Events in Hypertension Using Nationwide Claims Data

Journals

  1. 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
  2. Wu X, Yuan X, Wang W, Liu K, Qin Y, Sun X, Ma W, Zou Y, Zhang H, Zhou X, Wu H, Jiang X, Cai J, Chang W, Zhou S, Song L. Value of a Machine Learning Approach for Predicting Clinical Outcomes in Young Patients With Hypertension. Hypertension 2020;75(5):1271 View
  3. López-Martínez F, Núñez-Valdez E, Crespo R, García-Díaz V. An artificial neural network approach for predicting hypertension using NHANES data. Scientific Reports 2020;10(1) View
  4. 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
  5. Surodina S, Lam C, Grbich S, Milne-Ives M, van Velthoven M, Meinert E. Machine Learning for Risk Group Identification and User Data Collection in a Herpes Simplex Virus Patient Registry: Algorithm Development and Validation Study. JMIRx Med 2021;2(2):e25560 View