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
6. 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
7. Abramov M, Tsukanova E, Tulupyev A, Korepanova A, Aleksanin S. Identification of Deterioration caused by AHF, MADS or CE by RR and QT Data Classification. Informatics and Automation 2022;21(2):311 View
8. Morid M, Sheng O, Dunbar J. Time Series Prediction Using Deep Learning Methods in Healthcare. ACM Transactions on Management Information Systems 2023;14(1):1 View
9. 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
10. 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
11. Yang P, Qiu H, Wang L, Zhou L. Early prediction of high-cost inpatients with ischemic heart disease using network analytics and machine learning. Expert Systems with Applications 2022;210:118541 View
12. Sato J, Mitsutake N, Kitsuregawa M, Ishikawa T, Goda K. Predicting demand for long-term care using Japanese healthcare insurance claims data. Environmental Health and Preventive Medicine 2022;27(0):42 View
13. Fukunishi H, Kobayashi Y. Care-needs level prediction for elderly long-term care using insurance claims data. Informatics in Medicine Unlocked 2023;41:101321 View
14. Arends G, Loos N, van Kooij Y, Tabeau K, de Ridder W, Selles R, Veltkamp J, Wouters R. What are the perspectives of patients with hand and wrist conditions, chronic pain, and patients recovering from stroke on the use of patient and outcome information in everyday care? A Mixed-Methods study. Quality of Life Research 2024;33(9):2573 View
15. Swinckels L, Bennis F, Ziesemer K, Scheerman J, Bijwaard H, de Keijzer A, Bruers J. The Use of Deep Learning and Machine Learning on Longitudinal Electronic Health Records for the Early Detection and Prevention of Diseases: Scoping Review. Journal of Medical Internet Research 2024;26:e48320 View
16. Swinckels L, Keijzer d, Loos B, Applegate R, Kookal K, Kalenderian E, Bijwaard H, Bruers J. A Personalized Periodontitis Risk Based on Nonimage Electronic Dental Records by Machine Learning. Journal of Dentistry 2024:105469 View