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使用机器学习和可解释的人工智能预测糖尿病脚截肢的风险

Chien Wei Oei^1,2, Yam Meng Chan³, Xiaojin Zhang¹

¹Management Information Department, Office of Clinical Epidemiology, Analytics and Knowledge, Tan Tock Seng Hospital, Singapore.

Journal of diabetes science and technology

|January 30, 2024

概括

机器学习模型可以准确地预测糖尿病足患者的下肢截肢 (LEAs). 可解释的人工智能为早期干预提供临床见解,减少截肢风险.

科学领域:

医疗信息学医疗信息学
计算医学是一种计算医学.
糖尿病研究糖尿病研究

背景情况:

糖尿病足 (DFU) 是下肢截肢的重要原因之一.
早期识别高风险患者对于预防LEA至关重要.
机器学习 (ML) 和可解释的AI为改善风险预测提供了潜力.

研究的目的:

开发一种可解释的ML模型,用于预测DFU患者的LEA风险.
在DFU案件中确定LEA的关键预测因素.
加强早期干预的临床决策.

主要方法:

对2559个DFU事件 (2012-2017年) 的回顾性审查.
开发使用51个特征的ML模型,用于主要,次要和任何LEA结果.
使用ROC曲线,平衡精度和F1得分对模型性能进行评估;为了可解释性,SHapley添加式解释 (SHAP).

主要成果:

机器学习模型实现了高预测性能 (主要LEA的ROC为0.820).
XGBoost 和梯度增强树是表现最好的算法.
SHAP分析确定了总白细胞计数,并发病率得分和伤口特征作为关键预测因素.

结论:

ML模型在预测DFU患者的LEA风险方面表现出强大的有效性.
可解释的人工智能通过突出关键风险因素来提高临床效用.
这种方法促进了有针对性的早期干预,以防止截肢.

关键词:

莎普利的添加式解释糖尿病糖尿病患者糖尿病患者糖尿病足糖尿病足下肢截肢是指截肢的情况.机器学习是机器学习.模型可解释性模型可解释性伤口的伤口是伤口.

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Diabetic Foot Ulcer

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Definition A diabetic foot ulcer (DFU) is a chronic, non-healing wound that develops in individuals with diabetes. It typically occurs on pressure-bearing areas such as the heel, metatarsal heads, or hallux, and carries a high risk of infection and amputation.Pathophysiology • The development of DFUs can be explained by four interconnected mechanisms: neuropathy, ischemia, infection, and impaired wound healing. • Neuropathy is the most common factor. Sensory neuropathy reduces pain perception,...