可解释的人工智能用于LDL胆固醇预测和分类
Sevilay Sezer1, Ali Oter2, Betul Ersoz3
1Department of Medical Biochemistry, Ministry of Health, Ankara Bilkent City Hospital, Ankara, Turkey.
Clinical biochemistry
|July 8, 2024
概括
人工智能 (AI) 准确预测低密度脂蛋白胆固醇 (LDL-C) 水平,超过传统配方. 可解释性AI (XAI) 确保这些预测是可解释的,增强了对动脉样硬化心脏病风险的临床决策.
科学领域:
- 心血管医学 心血管医学
- 生物医学信息学 生物医学信息学
- 医疗保健中的人工智能
背景情况:
- 低密度脂蛋白胆固醇 (LDL-C) 的监测对于控制动脉样硬化心脏病风险至关重要.
- 准确的LDL-C测量或估计在临床实践中至关重要.
- 现有的LDL-C计算方法在精度上可能有局限性.
研究的目的:
- 评估人工智能 (AI) 和可解释AI (XAI) 用于预测LDL-C水平.
- 将人工智能驱动的LDL-C预测与传统计算值进行比较.
- 强调AI模型在LDL-C估计中的可解释性.
主要方法:
- 从医院实验室信息系统中对60,217名患者的脂质资料进行了回顾性分析.
- 应用人工智能模型,包括渐变增强 (GB),随机森林 (RF),支持向量机 (SVM) 和决策树 (DT).
- 利用XAI技术 (SHAP,LIME) 来解释AI模型的预测,并与直接的LDL-C测量和基于公式的计算进行比较.
主要成果:
- 人工智能模型,特别是RF和GB,与直接测量的LDL-C的相关性比基于公式的计算更强.
- 总胆固醇 (TC) 被确定为使用SHAP和LIME的LDL-C最重要的预测因素.
- 基于AI的LDL-C分类与基于配方的方法相比,与NCEP ATPIII指南的一致性更高.
结论:
- 人工智能为估计和分类LDL-C水平提供了可靠和可解释的方法.
- 人工智能驱动的预测提高了临床环境中LDL-C评估的准确性.
- 人工智能模型 (XAI) 的可解释性是其用于心血管风险管理的临床采用的关键.
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