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Updated: Jan 13, 2026

Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task

Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task

Published on: December 5, 2025

使用适应性深度学习 (EMBRACE) 进行可解释的多任务倦怠预测,用于住院医生:算法开发和验证研究.

Saima Alam¹, Mohammad Arif Ul Alam^2,3,4

¹Merrimack Health Methuen Hospital, Methuen, MA, United States.

|January 8, 2026

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这项研究介绍了EMBRACE,一种新的AI框架,使用可穿戴传感器来预测和解释住院医生的倦怠. 它为早期干预和改善医生福祉提供了可操作的见解.

科学领域:

医疗保健中的机器学习
可穿戴式传感器技术
医生倦怠研究研究医生倦怠研究

背景情况:

医疗人员面临高压力和倦怠,因为要求繁忙的时间表.
现有的机器学习模型缺乏临床解释.
可穿戴式传感器提供了客观预测倦怠的潜力.

研究的目的:

为了介绍EMBRACE,一个可解释的多任务倦怠预测框架.
用自适应深度学习来预测和解释住院医生未来的倦怠.
通过可解释AI (XAI) 技术增强临床信任.

主要方法:

开发了一个自适应式多任务深度学习框架 (EMBRACE).
利用可穿戴传感器数据来预测活动和倦怠程度.
集成的SHAP (沙普利添加式解释) 为模型的可解释性.
在三个数据集上验证,包括专有住院医生数据集.

主要成果:

在数据集中预测活动,倦怠程度和调查答案方面,EMBRACE 实现了高准确度.
SHAP分析确定了关键的倦怠预测因素,如心率变化和久坐行为.
91%的参与者认为特征重要性总结令人满意.

关键词:

临床解释性的临床解释性.未来的燃烧预测预测医疗保健信息学医疗保健信息学机器学习是机器学习.多任务学习学习可以穿戴的传感器.

相关实验视频

Last Updated: Jan 13, 2026

Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task

Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task

Published on: December 5, 2025

结论:

EMBRACE为早期倦怠检测提供了一种临床上可解释和可操作的解决方案.
该框架在各种数据集中显示出强度和通用性.
未来的工作包括扩展模型并评估对医生福祉的长期影响.