心血管风险模式通过人工智能增强的纵向健康数据集群
Maryam Mahdavi1, Anoshirvan Kazemnejad1, Abbas Asosheh2
1Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.
Journal of diabetes and metabolic disorders
|March 17, 2025
概括
无监督学习在伊朗成年人中确定了不同的心血管疾病 (CVD) 风险因素轨迹. 高风险轨迹显示心血管疾病发病率显著增加,改善了早期识别和预防策略.
科学领域:
- 心血管疾病的研究研究.
- 公共卫生 公共卫生
- 医学中的数据科学.
背景情况:
- 心血管疾病 (CVD) 是全球主要的死亡原因.
- 危险因素包括吸烟,高血压,饮食不良和不活动.
- 了解心血管疾病风险动态对于预防至关重要.
研究的目的:
- 分析伊朗成年人的纵向心血管疾病风险因素轨迹.
- 识别风险因素随时间变化的独特模式.
- 检查这些轨迹与心血管疾病发病率之间的关系.
主要方法:
- 利用无监督学习,特别是k-means集群,基于德黑兰脂质和葡萄糖研究 (TLGS) 的纵向数据.
- 分析了1872名年龄在40-79岁的成年人的数据,在基线时没有动脉样硬化心血管疾病.
- 工作时间SeriesScalerMeanVariance用于标准化和轮得分,以实现最佳的集群确定.
主要成果:
- 确定了四种不同的心血管疾病风险因素轨迹.
- 一个高风险集群 (集群1) 与一个低风险集群 (集群4) 相比,显示心血管疾病事件的风险明显更高.
- 高风险集群在前五年内表现出89%的心血管疾病发病率.
结论:
- 基于轨迹的聚类有效地识别了患心血管疾病高风险的个体.
- 对风险因素轨迹的纵向监测有助于早期检测.
- 可以根据已识别的风险轨迹制定有针对性的预防策略.
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