■全球人口老龄化是一个重大挑战,老年人的身体和认知能力下降,对慢性疾病和不良健康结局的脆弱性增加。这项研究旨在开发一种可解释的深度学习(DL)模型,以预测住院72小时内老年患者的不良事件。
■该研究使用了台湾一家主要医疗中心的回顾性数据(2017-2020年)。其中包括非创伤老年患者,他们去了急诊科并被送往普通病房。数据预处理包括收集预后因素,如生命体征,实验室结果,病史,和临床管理。开发了一种深度前馈神经网络,并使用准确性评估性能,灵敏度,特异性,阳性预测值(PPV),和接受者工作特征曲线下面积(AUC)。模型解释利用了Shapley加法解释(SHAP)技术。
■分析包括127,268名患者,2.6%的人即将经历重症监护病房转移,呼吸衰竭,或在住院期间死亡。DL模型在验证集和测试集中实现了0.86和0.84的AUC,分别,优于序贯器官衰竭评估(SOFA)评分。敏感性和特异性值范围为0.79至0.81。SHAP技术提供了对特征重要性和交互的见解。
■开发的DL模型在预测老年患者住院72小时内的严重不良事件方面具有很高的准确性。它的性能优于SOFA分数,并为模型的决策过程提供了有价值的见解。
UNASSIGNED: The global aging population presents a significant challenge, with older adults experiencing declining physical and cognitive abilities and increased vulnerability to chronic diseases and adverse health outcomes. This study aims to develop an interpretable deep learning (DL) model to predict adverse events in geriatric patients within 72 hours of hospitalization.
UNASSIGNED: The study used retrospective data (2017-2020) from a major medical center in Taiwan. It included non-trauma geriatric patients who visited the emergency department and were admitted to the general ward. Data preprocessing involved collecting prognostic factors like vital signs, lab results, medical history, and clinical management. A deep feedforward neural network was developed, and performance was evaluated using accuracy, sensitivity, specificity, positive predictive value (PPV), and area under the receiver operating characteristic curve (AUC). Model interpretation utilized the Shapley Additive Explanation (SHAP) technique.
UNASSIGNED: The analysis included 127,268 patients, with 2.6% experiencing imminent intensive care unit transfer, respiratory failure, or death during hospitalization. The DL model achieved AUCs of 0.86 and 0.84 in the validation and test sets, respectively, outperforming the Sequential Organ Failure Assessment (SOFA) score. Sensitivity and specificity values ranged from 0.79 to 0.81. The SHAP technique provided insights into feature importance and interactions.
UNASSIGNED: The developed DL model demonstrated high accuracy in predicting serious adverse events in geriatric patients within 72 hours of hospitalization. It outperformed the SOFA score and provided valuable insights into the model\'s decision-making process.