VE-CAM-S: Visual EEG-Based Grading of Delirium Severity and Associations with Clinical Outcomes

This is a dataset and code to accompany a published prospective, observational cohort study, which used machine learning to develop the Visual EEG Confusion Assessment Method Severity (VE-CAM-S). VE-CAM-S is a physiological grading scale that quantifies the severity of delirium or coma secondary to acute encephalopathy. VE-CAM-S scores are well calibrated with the severity of delirium symptoms and coma and are associated with clinical outcomes, including in-hospital and 3-month mortality and functional disability at hospital discharge.

本数据集及代码旨在伴随一项已发表的预期性、观察性队列研究，该研究运用机器学习技术构建了视觉脑电图昏迷评估方法严重程度评分（VE-CAM-S）。VE-CAM-S是一种生理学分级量表，用于量化急性脑病继发昏迷或谵妄的严重程度。VE-CAM-S评分与昏迷及谵妄症状的严重程度高度校准，并与临床结果相关联，包括住院期间及出院后3个月内的死亡率以及出院时的功能障碍程度。