---
title: "AI-driven risk prediction and early precision management for heart failure"
id: "pubmed-42907229"
canonical_url: "https://medichelpline.com/clinical-feed/pubmed-42907229"
content_type: "clinical_feed_article"
specialty: "Cardiology"
source_name: "PubMed / NCBI"
source_url: "https://pubmed.ncbi.nlm.nih.gov/42907229/"
doi: "10.3760/cma.j.cn112148-20260721-00460"
published_at: "2026-10-24T00:00:00.000Z"
evidence_level: "Journal Article"
license: "CC-BY-NC-4.0 / Informational Use"
---
# AI-driven risk prediction and early precision management for heart failure
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/pubmed-42907229
- **Specialty:** [Cardiology](https://medichelpline.com/clinical-feed/cardiology.md)
- **Primary Source:** PubMed / NCBI
- **Source URL:** [Original Journal Publication](https://pubmed.ncbi.nlm.nih.gov/42907229/)
- **DOI:** [10.3760/cma.j.cn112148-20260721-00460](https://doi.org/10.3760%2Fcma.j.cn112148-20260721-00460)
- **Published At:** 2026-10-24T00:00:00.000Z
- **Evidence Rating:** Journal Article
## Executive GIST (TL;DR)
- The review focused on applications of **Artificial Intelligence** for risk stratification and early intervention in **heart failure**, summarizing recent research progress. - Investigators reported that heart failure has complex causes and often an insidious onset, which makes early diagnosis difficult and contributes to delayed intervention. - The authors noted long-term outpatient management of chronic heart failure suffers from missed early warnings of symptom deterioration and difficulties in dynamically optimizing treatment plans. - The review highlighted wide individual variability in treatment response among heart failure patients, which complicates standardized care pathways and motivates precision approaches. - Data indicated that rapid progress in **AI** techniques offers new ideas for early risk prediction, continuous monitoring, and individualized management strategies in heart failure care. - The article surveyed algorithmic and clinical applications; however, the PubMed record did not report specific study methodologies, model architectures, or quantitative performance metrics in detail. - The authors discussed the potential of integrating AI-driven risk tools into **precision medicine** frameworks, and they explored challenges and future research questions for clinical translation. - The cohort of authors declared no conflicts of interest; publication details include PMID 42907229 and DOI 10.3760/cma.j.cn112148-20260721-00460. - The review was published in Zhonghua Xin Xue Guan Bing Za Zhi (2026 Oct 24;54(10):1218-1222) and is listed under MeSH headings including Artificial Intelligence, Heart Failure/diagnosis, Precision Medicine, and Risk Assessment.
## Clinical Analysis & Structured Key Points
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Affiliations Expand ### Affiliation * 1 Department of Cardiology, Beijing Hospital, National Center for Gerontology, National Clinical Research Center for Gerontology, Key Laboratory of Geriatrics of National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100730, China. * PMID: **42907229** * DOI: [ 10.3760/cma.j.cn112148-20260721-00460 ](https://doi.org/10.3760/cma.j.cn112148-20260721-00460) Item in Clipboard Review # [AI-driven heart failure risk prediction and early precision management strategies] [Article in Chinese] Y Y Li et al. Zhonghua Xin Xue Guan Bing Za Zhi. 2026. Show details Display options Display options Format Abstract PubMed PMID Zhonghua Xin Xue Guan Bing Za Zhi Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Zhonghua+Xin+Xue+Guan+Bing+Za+Zhi%22%5Bjour%5D&sort=date&sort_order=desc) * [ Search in NLM Catalog ](https://www.ncbi.nlm.nih.gov/nlmcatalog?term=%22Zhonghua+Xin+Xue+Guan+Bing+Za+Zhi%22%5BTitle+Abbreviation%5D) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42907229/) . 2026 Oct 24;54(10):1218-1222. doi: 10.3760/cma.j.cn112148-20260721-00460. ### Authors [Y Y Li](https://pubmed.ncbi.nlm.nih.gov/?term=Li+YY&cauthor_id=42907229)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42907229/#short-view-affiliation-1 "Department of Cardiology, Beijing Hospital, National Center for Gerontology, National Clinical Research Center for Gerontology, Key Laboratory of Geriatrics of National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100730, China."), [H Wang](https://pubmed.ncbi.nlm.nih.gov/?term=Wang+H&cauthor_id=42907229)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42907229/#short-view-affiliation-1 "Department of Cardiology, Beijing Hospital, National Center for Gerontology, National Clinical Research Center for Gerontology, Key Laboratory of Geriatrics of National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100730, China."), [J F Yang](https://pubmed.ncbi.nlm.nih.gov/?term=Yang+JF&cauthor_id=42907229)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42907229/#short-view-affiliation-1 "Department of Cardiology, Beijing Hospital, National Center for Gerontology, National Clinical Research Center for Gerontology, Key Laboratory of Geriatrics of National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100730, China.") ### Affiliation * 1 Department of Cardiology, Beijing Hospital, National Center for Gerontology, National Clinical Research Center for Gerontology, Key Laboratory of Geriatrics of National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100730, China. * PMID: **42907229** * DOI: [ 10.3760/cma.j.cn112148-20260721-00460 ](https://doi.org/10.3760/cma.j.cn112148-20260721-00460) Item in Clipboard Full text links Cite Display options Display options Format Abstract PubMed PMID ## Abstract 心力衰竭已成为全球重大公共卫生挑战。其病因复杂、起病隐匿，早期诊断难度大。慢性心力衰竭长期随访管理中存在症状恶化缺乏早期预警、治疗方案难以动态优化、治疗反应个体差异大等问题，均为临床亟待解决的难点。随着人工智能技术快速发展，为心力衰竭风险分层、早期干预提供了新思路。该文综述人工智能在心力衰竭风险预测领域的研究进展，探讨其在心力衰竭精准防治中的应用前景。. 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