In intensive care units (ICUs), critically ill patients are monitored with electroencephalograms (EEGs) to prevent serious brain injury. The number of patients who can be monitored is constrained by the availability of trained physicians to read EEGs, and EEG interpretation can be subjective and prone to inter-observer variability. Automated deep learning systems for EEG could reduce human bias and accelerate the diagnostic process. However, black box deep learning models are untrustworthy, difficult to troubleshoot, and lack accountability in real-world applications, leading to a lack of trust and adoption by clinicians. To address these challenges, we propose a novel interpretable deep learning model that not only predicts the presence of harmful brainwave patterns but also provides high-quality case-based explanations of its decisions. Our model performs better than the corresponding black box model, despite being constrained to be interpretable. The learned 2D embedded space provides the first global overview of the structure of ictal-interictal-injury continuum brainwave patterns. The ability to understand how our model arrived at its decisions will not only help clinicians to diagnose and treat harmful brain activities more accurately but also increase their trust and adoption of machine learning models in clinical practice; this could be an integral component of the ICU neurologists' standard workflow.

翻译：在重症监护病房（ICU）中，危重患者通过脑电图（EEG）监测来预防严重脑损伤。可接受监测的患者数量受限于能解读EEG的资深医师的可及性，且EEG判读可能存在主观性和观察者间差异。基于EEG的自动化深度学习系统可减少人为偏差并加速诊断流程。然而，黑箱式深度学习模型在真实临床应用中存在不可信赖、难以调试和缺乏问责性等问题，导致临床医生对其缺乏信任且难以推广使用。为解决这些挑战，我们提出了一种新型可解释深度学习模型，该模型不仅能预测有害脑电波模式的存在，还能提供基于案例的高质量决策解释。尽管受限于可解释性约束，我们的模型性能仍优于对应的黑箱模型。学习得到的二维嵌入空间首次提供了癫痫发作-发作间期-脑损伤连续谱脑电波模式的全局结构概览。理解模型决策机理的能力不仅能帮助临床医生更准确地诊断和治疗有害脑电活动，还能增强他们对机器学习模型的信任度及在临床实践中的采纳率；这有望成为ICU神经科医师标准工作流程的核心组成部分。