AI 中文总结
该研究基于可解释证据神经网络模型，结合SHAP分析，识别1型糖尿病患者夜间低血糖的关键预测因子，为临床预防夜间低血糖提供决策支持。
AI 中文摘要
目的：夜间低血糖（NH）仍是1型糖尿病（T1D）患者面临的挑战，尤其是那些身体活跃或采用每日多次注射（MDI）治疗的患者。我们利用可解释证据神经网络模型预测夜间最低血糖，以识别NH风险因素并生成NH预防建议。方法：我们使用SHapley加性解释（SHAP）分析血糖和身体活动（PA）因素对预测NH概率的影响。数据来自20名采用MDI治疗的T1D成人（10名女性；平均年龄39岁；糖化血红蛋白（HbA1c）为7%），他们参与了一项交叉研究（NCT05967260）。结果：共分析了502个夜晚的情况。睡前血糖是NH最强的预测因子，其他相关因素包括近期高血糖或低血糖暴露、睡前前的血糖变异性以及PA的时间安排。一些关联在生理上看似违反直觉，可能反映了行为调整。基于已识别的风险因素及其SHAP值，我们确定了关键决策点并制定了预防NH的建议，例如食用睡前零食或与医疗保健提供者讨论胰岛素治疗的潜在调整方案。结论：识别NH的预测因子为临床决策支持中管理NH风险提供了见解。
英文摘要
Purpose: Nocturnal hypoglycemia (NH) remains a challenge for individuals with type 1 diabetes (T1D), particularly those who are physically active or on multiple daily injections (MDI). We leveraged an explainable evidential neural network model that forecasts minimum overnight glucose to identify NH risk factors and generate recommendations for NH prevention. Methods: We analyzed the impact of glucose and physical activity (PA) factors on predicted NH probability using SHapley Additive exPlanations (SHAP). Data were from 20 adults with T1D on MDI (10 females; mean age 39 years; HbA1c 7\%) who participated in a cross-over study (NCT05967260). Results: A total of 502 nights were analyzed. Bedtime glucose was the strongest predictor of NH. Other relevant factors included recent exposure to high or low glucose, glucose variability before bedtime, and timing of PA. Some associations appeared physiologically counterintuitive, possibly reflecting behavioral adjustments. Based on the identified risk factors and their SHAP values, we determined key decision points and developed recommendations that may help prevent NH, such as consuming a bedtime snack or discussing potential adjustments to insulin therapy with a healthcare provider. Conclusion: Identifying predictors of NH offers insights for managing NH risk in clinical decision support.
