Medical Data Science, Department of Computer Science, ETH Zurich, Zurich, Switzerland.
Swiss Institute of Bioinformatics, Lausanne, Switzerland.
J Am Med Inform Assoc. 2021 Mar 18;28(4):868-873. doi: 10.1093/jamia/ocaa299.
Unplanned hospital readmissions are a burden to patients and increase healthcare costs. A wide variety of machine learning (ML) models have been suggested to predict unplanned hospital readmissions. These ML models were often specifically trained on patient populations with certain diseases. However, it is unclear whether these specialized ML models-trained on patient subpopulations with certain diseases or defined by other clinical characteristics-are more accurate than a general ML model trained on an unrestricted hospital cohort. In this study based on an electronic health record cohort of consecutive inpatient cases of a single tertiary care center, we demonstrate that accurate prediction of hospital readmissions may be obtained by general, disease-independent, ML models. This general approach may substantially decrease the cost of development and deployment of respective ML models in daily clinical routine, as all predictions are obtained by the use of a single model.
非计划性住院再入院给患者带来负担，并增加医疗保健成本。已经提出了各种各样的机器学习 (ML) 模型来预测非计划性住院再入院。这些 ML 模型通常是针对特定疾病的患者人群进行专门训练的。然而，尚不清楚这些专门针对特定疾病或其他临床特征的患者亚群进行训练的 ML 模型是否比针对不受限制的住院患者队列进行训练的通用 ML 模型更准确。在这项基于单个三级护理中心连续住院病例的电子健康记录队列的研究中，我们证明了通过通用的、与疾病无关的 ML 模型可以实现对医院再入院的准确预测。这种通用方法可以大大降低在日常临床常规中开发和部署各自的 ML 模型的成本，因为所有预测都是通过使用单个模型获得的。
