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

Shaofu Lin1, Haokang Yan1, Shiwei Zhou1

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

Sensors (Basel, Switzerland)
|August 10, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces HRP-OG, an online hypertension monitoring model using reinforcement learning. It improves early hypertension risk prediction from electronic health records, especially with limited patient data.

Keywords:
electronic health recordsgenerative replayhypertension risk predictiononline learningreinforcement learning

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Area of Science:

  • Cardiovascular Medicine
  • Artificial Intelligence in Healthcare
  • Biomedical Informatics

Background:

  • Hypertension is a significant global health issue, escalating with aging populations and lifestyle changes.
  • Early hypertension detection and intervention are crucial for reducing disease prevalence and healthcare costs.
  • Existing early warning models struggle with limited, imbalanced patient data and non-stationary hypertension features.

Purpose of the Study:

  • To develop an effective online hypertension monitoring model for early risk prediction.
  • To address challenges posed by scarce, imbalanced multivisit records and non-stationary data in hypertension prediction.
  • To leverage electronic health records (EHRs) and real-time sensor data for dynamic patient monitoring.

Main Methods:

  • Proposed an online hypertension monitoring model (HRP-OG) using reinforcement learning and generative feature replay.
  • Transformed hypertension prediction into a sequential decision-making problem for multivisit record analysis.
  • Integrated real-time physiological data from medical devices and wearables into EHRs.
  • Utilized maximum likelihood estimation to evaluate generator fit and reduce feature space discrepancies.
  • Implemented online model updates using generative feature replay for adaptation to incremental data.

Main Results:

  • The HRP-OG model demonstrated improved accuracy in hypertension risk prediction compared to existing advanced methods.
  • Effectively handled few-shot multivisit records in non-stationary environments.
  • Showcased the benefit of integrating real-time sensor data for dynamic patient condition adaptation.

Conclusions:

  • The HRP-OG model offers a promising approach for accurate and timely hypertension risk prediction.
  • Online monitoring and adaptive learning are key to overcoming data limitations in chronic disease prediction.
  • This model facilitates timely interventions, potentially reducing the burden of hypertension-related diseases.