天真贝叶斯是一种可解释和预测的机器学习算法,可以预测骨质疏松性关节骨折在医院中的死亡率,与其他机器学习算法相比
1Department of Geriatric Medicine, The Canberra Hospital, ACT Health, Canberra, Australia.
PLOS digital health
|January 2, 2025
概括
预测老年人骨质疏松性关节骨折后的住院死亡率是一项挑战. 纯粹的贝叶斯模型提供了使用基本患者数据的简单,可解释和竞争性的方法.
科学领域:
- 老年学是一门学科.
- 老年医学 老年医学
- 医疗信息学 医疗信息学
背景情况:
- 老年人的骨质疏松性关节骨折 (HFs) 代表了严重的医疗负担,特别是在发达国家.
- 准确的预后估计入院后至关重要,但由于当前预测工具的局限性而具有挑战性.
- 现有的模型通常需要广泛的患者数据,这些数据在入院过程的早期无法获得.
研究的目的:
- 开发和评估机器学习 (ML) 模型,用于预测患有轻微创伤关节骨折的老年患者 (≥65岁) 的住院死亡率.
- 为了评估ML模型的性能,仅使用易于获得的社会人口统计和并发症数据.
- 为了提高临床应用的ML预测的可解释性.
主要方法:
- 开发了七种ML预测模型,包括随机森林,原始贝叶斯 (NB),极端梯度增强和后勤回归.
- 超参数调整使用分数因数设计和网格搜索.
- 模型评估涉及5倍交叉验证和接收器操作特征曲线 (AUROC) 下的区域;可解释性使用SHAP值进行评估.
主要成果:
- 性能最好的模型在0.682-0.696.6之间实现了AUROC.
- 确定的主要预测因素包括慢性病,心血管并发症和骨代谢标志物.
- 天真贝叶斯表现出具有竞争力的预测性能,具有增强的可解释性.
结论:
- 天真贝叶斯提出了一个有前途的算法,用于预测由于其简单性和可解释性的关节骨折后的住院死亡率.
- 开发的ML模型为使用有限的输入数据进行早期预后估计提供了可行的替代方案.
- 在老年骨折护理中对可解释的AI进行进一步研究是有必要的.
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