Medical error is a leading cause of patient death in the United States. Among the different types of medical errors, harm to patients caused by doctors missing early signs of deterioration is especially challenging to address due to the heterogeneity of patients' physiological patterns. In this study, we implemented risk prediction models using the gradient boosted tree method to derive risk estimates for acute onset diseases in the near future. The prediction model uses physiological variables as input signals and the time of the administration of outcome-related interventions and discharge diagnoses as labels. We examine four categories of acute onset illness: acute heart failure (AHF), acute lung injury (ALI), acute kidney injury (AKI), and acute liver failure (ALF). To develop and test the model, we consider data from two sources: 23,578 admissions to the Intensive Care Unit (ICU) from the MIMIC-3 dataset (Beth-Israel Hospital) and 16,612 ICU admissions on hospitals affiliated with our institution (University of Washington Medical Center and Harborview Medical Center, the UW-CDR dataset). We systematically identify outcome-related interventions for each acute organ failure, then use them, along with discharge diagnoses, to label proxy events to train gradient boosted trees. The trained models achieve the highest F1 score with a value of 0.6018 when predicting the need for life-saving interventions for ALI within the next 24 h in the MIMIC-3 dataset while showing a median F1 score of 0.3850 from all acute organ failures in both datasets. The approach also achieves the highest F1 score of 0.6301 when classifying a patient's ALI status at the time of discharge from the MIMIC-3 dataset, with a median F1 score of 0.4307 in both datasets. This study shows the potential for using the time of outcome-related intervention administrations and discharge diagnoses as labels to train supervised machine learning models that predict the risk of acute onset illnesses.

中文翻译：

---

通过使用远程监督了解救生行动和结果来预测严重的临床事件。

医疗错误是美国患者死亡的主要原因。在不同类型的医疗错误中，由于患者生理模式的异质性，由医生缺少恶化的早期迹象引起的对患者的伤害尤其难以解决。在这项研究中，我们使用梯度增强树方法实施了风险预测模型，以在不久的将来得出急性发作疾病的风险估计。预测模型使用生理变量作为输入信号，并使用与结果相关的干预措施和出院诊断的时间作为标签。我们检查了四种急性发作疾病：急性心力衰竭（AHF），急性肺损伤（ALI），急性肾损伤（AKI）和急性肝衰竭（ALF）。为了开发和测试模型，我们考虑了以下两个来源的数据：23，MIMIC-3数据集（贝斯-以色列医院）有578例重症监护病房（ICU）入院，我们机构附属医院（华盛顿大学医学中心和Harborview医学中心的UW-CDR数据集）有16,612例ICU入院。我们针对每个急性器官衰竭系统地确定与结果相关的干预措施，然后将其与出院诊断一起用于标记代理事件以训练梯度增强的树木。当预测MIMIC-3数据集在未来24小时内需要进行ALI挽救生命的ALI干预时，训练有素的模型将获得最高的F1得分，值为0.6018，而这两个模型中所有急性器官衰竭的F1得分中位数均为0.3850数据集。在对患者进行分类时，该方法还可以达到0.6301的最高F1分数 从MIMIC-3数据集中放电时的ALI状态，两个数据集中的F1中位数均为0.4307。这项研究显示了使用与结果相关的干预管理和出院诊断时间作为标签来训练有监督的机器学习模型的潜力，该模型预测了急性发作疾病的风险。