Li Xiangrui, Zhu Dongxiao, Levy Phillip
Department of Computer Science.
Integrative Bioscience Center; Wayne State University, Detroit, MI, USA.
AMIA Jt Summits Transl Sci Proc. 2020 May 30;2020:367-376. eCollection 2020.
The increasing availability of electronic health record data offers unprecedented opportunities for predictive modeling in healthcare informatics including outcomes such as mortality and disease diagnosis as well as risk factor identification. Recently, deep neural networks (DNNs) have been successfully applied in healthcare informatics and achieved state-of-art predictive performance. However, existing DNN models either rely on the pre-defined patient subgroups or take the "one-size-fits-all" approach and are built without considering patient stratification. Consequently, those models are not able to discover patient subgroups and the risk factors are thereafter identified for the entire patient population, failing to account for potential group differences. To address this challenge, we propose the use of deep mixture neural networks (DMNN), a unified DNN model for simultaneous patient stratification and predictive modeling. Experimental results on a clinic dataset show that our proposed DMNN can achieve good performance on predicting diagnosis of acute heart failure. With DMNN's ability to incorporate patient stratification, we are able to systematically identify group-specific risk factors for different patient subgroups which could potentially shed light on revealing factors that contribute to outcome differences.
电子健康记录数据的日益普及为医疗信息学中的预测建模提供了前所未有的机会，包括对死亡率和疾病诊断等结果以及风险因素识别进行预测建模。最近，深度神经网络（DNN）已成功应用于医疗信息学，并取得了领先的预测性能。然而，现有的DNN模型要么依赖于预定义的患者亚组，要么采用“一刀切”的方法，且在构建时未考虑患者分层。因此，这些模型无法发现患者亚组，只能为整个患者群体识别风险因素，无法考虑潜在的组间差异。为应对这一挑战，我们提出使用深度混合神经网络（DMNN），这是一种用于同时进行患者分层和预测建模的统一DNN模型。在一个临床数据集上的实验结果表明，我们提出的DMNN在预测急性心力衰竭诊断方面能够取得良好的性能。凭借DMNN纳入患者分层的能力，我们能够系统地为不同患者亚组识别特定组的风险因素，这可能有助于揭示导致结果差异的因素。
