Traditional methods for assessing illness severity and predicting in-hospital mortality among critically ill patients require manual, time-consuming, and error-prone calculations that are further hindered by the use of static variable thresholds derived from aggregate patient populations. These coarse frameworks do not capture time-sensitive individual physiological patterns and are not suitable for instantaneous assessment of patients' acuity trajectories, a critical task for the ICU where conditions often change rapidly. Furthermore, they are ill-suited to capitalize on the emerging availability of streaming electronic health record data. We propose a novel acuity score framework (DeepSOFA) that leverages temporal patient measurements in conjunction with deep learning models to make accurate assessments of a patient's illness severity at any point during their ICU stay. We compare DeepSOFA with SOFA baseline models using the same predictors and find that at any point during an ICU admission, DeepSOFA yields more accurate predictions of in-hospital mortality.

提出了一种新的Acuity评分架构：DeepSOFA，使用可解释的深度学习模型和时间测量来评估ICU逗留期间的疾病严重程度和预测住院病死率，相比于基线模型SOFA，DeepSOFA具有更高的准确性，有助于及早发现ICU患者的生命危险，并在治疗策略和资源利用方面提供决策参考。

DeepSOFA：使用临床可解释的深度学习对危重病患者进行连续生理状态评分