HRP-OG: Online Learning with Generative Feature Replay for Hypertension Risk Prediction in a Nonstationary Environment

Author:

Lin Shaofu1ORCID,Yan Haokang1ORCID,Zhou Shiwei1,Qiao Ziqian1,Chen Jianhui12345

Affiliation:

1. Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China

2. Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing University of Technology, Beijing 100124, China

3. Beijing Key Laboratory of MRI and Brain Informatics, Beijing University of Technology, Beijing 100124, China

4. Engineering Research Center of Intelligent Perception and Autonomous Control, Beijing 100124, China

5. Engineering Research Center of Digital Community, Ministry of Education, Beijing 100124, China

Abstract

Hypertension is a major risk factor for many serious diseases. With the aging population and lifestyle changes, the incidence of hypertension continues to rise, imposing a significant medical cost burden on patients and severely affecting their quality of life. Early intervention can greatly reduce the prevalence of hypertension. Research on hypertension early warning models based on electronic health records (EHRs) is an important and effective method for achieving early hypertension warning. However, limited by the scarcity and imbalance of multivisit records, and the nonstationary characteristics of hypertension features, it is difficult to predict the probability of hypertension prevalence in a patient effectively. Therefore, this study proposes an online hypertension monitoring model (HRP-OG) based on reinforcement learning and generative feature replay. It transforms the hypertension prediction problem into a sequential decision problem, achieving risk prediction of hypertension for patients using multivisit records. Sensors embedded in medical devices and wearables continuously capture real-time physiological data such as blood pressure, heart rate, and activity levels, which are integrated into the EHR. The fit between the samples generated by the generator and the real visit data is evaluated using maximum likelihood estimation, which can reduce the adversarial discrepancy between the feature space of hypertension and incoming incremental data, and the model is updated online based on real-time data using generative feature replay. The incorporation of sensor data ensures that the model adapts dynamically to changes in the condition of patients, facilitating timely interventions. In this study, the publicly available MIMIC-III data are used for validation, and the experimental results demonstrate that compared to existing advanced methods, HRP-OG can effectively improve the accuracy of hypertension risk prediction for few-shot multivisit record in nonstationary environments.

Funder

Beijing Natural Science Foundation

National Key Research and Development Program of China

Publisher

MDPI AG

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