Objective: Clinical notes contain information not present elsewhere, including drug response and symptoms, all of which are highly important when predicting key outcomes in acute care patients. We propose the automatic annotation of phenotypes from clinical notes as a method to capture essential information, which is complementary to typically used vital signs and laboratory test results, to predict outcomes in the Intensive Care Unit (ICU). Methods: We develop a novel phenotype annotation model to annotate phenotypic features of patients which are then used as input features of predictive models to predict ICU patient outcomes. We demonstrate and validate our approach conducting experiments on three ICU prediction tasks including in-hospital mortality, physiological decompensation and length of stay for over 24,000 patients by using MIMIC-III dataset. Results: The predictive models incorporating phenotypic information achieve 0.845 (AUC-ROC) to predict in-hospital mortality, 0.839 (AUC-ROC) for physiological decompensation and 0.430 (Kappa) for length of stay, all of which consistently outperform the baseline models leveraging only vital signs and laboratory test results. Moreover, we conduct a thorough interpretability study, showing that phenotypes provide valuable insights at the patient and cohort levels. Conclusion: The proposed approach demonstrates phenotypic information complements traditionally used vital signs and laboratory test results, improving significantly forecast of outcomes in the ICU.
翻译:目标:临床说明包含在别处没有出现的信息,包括药物反应和症状,所有这些都在预测急性护理病人的主要结果时非常重要。我们提议对临床说明中的苯型进行自动注解,作为获取基本信息的一种方法,作为对通常使用的生命迹象和实验室测试结果的补充,以预测强化护理股(ICU)的结果。 方法:我们开发了一种新颖的苯型注解模式,以预测病人的性倾向特征,这些特征随后被用作预测性模型的输入特征,以预测ICU病人的结果。我们展示和验证了我们在三项ICU预测任务方面进行实验的方法,包括住院死亡率、生理衰减和24 000名病人的停留时间,这是使用MIMIC-III数据集来获取基本信息的一种方法。 结果:包含性信息的预测模型达到了0.845(AUSC-ROC),以预测住院死亡率,0.839(AUSC-ROC)用于生理补充,0.430(KAPPA)用于停留时间长度,所有这些实验都持续超过住院死亡率,通过MIC-III数据集对我们使用的基本结果进行彻底分析。