由于外周动脉疾病而接受下肢截肢患者的新型亚型的预测模型:无监督机器学习研究
Yuanliang Ma1, Lin Zhang1, Que Li1
1The First Affiliated Hospital of Guangxi Medical University, PR China.
Heliyon
|August 19, 2024
概括
一个人工智能 (AI) 模型确定了不同的外周动脉疾病 (PAD) 亚型,改善了关键肢体缺血和截肢的风险分层. 这种人工智能驱动的方法提高了患者的管理,并高精度地预测疾病的进展.
科学领域:
- 心血管医学 心血管医学
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 周围动脉疾病 (PAD) 是一种常见的疾病,与关键肢体缺血和截肢有关.
- 关键下肢缺血需要截肢的结果在患者之间可能有很大差异.
研究的目的:
- 开发一种人工智能 (AI) 驱动的外周动脉疾病 (PAD) 亚型的预测模型.
- 提高PAD患者风险评估和疾病进展预测的精度和准确性.
主要方法:
- 一项回顾性研究分析了经过下肢截肢的PAD患者的临床数据.
- 无监督机器学习算法 (UMLA) 用于子组识别和风险分层.
- 为了预测,构建了一个二进制后勤回归模型,并通过后续数据验证.
主要成果:
- UMLA确定了两个不同的PAD子组 (集群1和2),其中集群1表现出较差的预后结果.
- 开发了一种包含八个预测因素 (性别,年龄,吸烟,糖尿病,冠状动脉心脏病,白蛋白,干预,截肢水平) 的名录.
- 纳米图准确地将患者分类到已识别的集群中,其AUC为0.861.
结论:
- 在PAD病例中,UMLA有效地确定了具有不同截肢风险的新型表型子组.
- 对PAD亚型的AI驱动的预测模型为风险分层和临床管理提供了高准确度和可靠性.
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