ClinicalTrials.gov
public full text
Public Domain
AI-Driven Health Management to Prevent Ischemic Stroke in High-Risk Adults
This study will evaluate whether an artificial intelligence (AI)-driven dynamic health management strategy can help prevent ischemic stroke in adults at high risk of stroke. Participants will be identified through community-based screening in Beijing using the AI-ExpoStroke model together with established stroke risk factors. Communities will be randomly assigned to either an AI-driven health management group or a usual community-based health management group. Participants in the AI-driven group will receive continuous health management supported by a digital platform, mobile applications or WeChat-based tools, wearable-device data when available, personalized health guidance, and remote support from community health care providers. Participants in the usual-care group will receive routine community health services, including health examinations, health education, chronic disease follow-up, and medication guidance. Participants will be followed for 36 months. The main goal is to determine whether AI-driven health management reduces the occurrence of first-ever ischemic stroke. The study will also evaluate transient ischemic attacks, stroke-related disability, mortality, control of major vascular risk factors, adherence to health management, and health economic outcomes.
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Artificial Intelligence Support for Stroke#826186
- Artificial Intelligence and occupational health: global umbrella review of applications and limitations#317480
- Predict + Protect Study: Exploring the Effectiveness of a Predictive Health Education Intervention on the Adoption of Protective Behaviors Related to ILI#1197430
- Self-screening of Cardiovascular Risk#857789
- Continuous Glucose Monitoring (CGM) in Prediabetes With Community Based Recruitment#874177
Record · ID 1262355
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive