标题： 使用潜在类别分析识别ARDS亚表型以提高机器学习预测性能 显示英文标题

标题： Using Latent Class Analysis to Identify ARDS Sub-phenotypes for Enhanced Machine Learning Predictive Performance

摘要： 在本工作中，我们利用机器学习对急性呼吸窘迫综合征（ARDS）高风险患者进行早期识别，这对于成功预防这种毁灭性综合征的策略至关重要。 早期识别ARDS的困难源于其复杂且异质的特性。 在本研究中，我们将ARDS患者的异质性知识整合到预测模型构建中。 使用MIMIC-III数据，我们首先应用潜在类别分析（LCA）来识别ARDS人群中的同质子组，然后在分组数据上构建预测模型。 结果表明，对于所识别的三种亚表型中的两种，可以显著提高预测性能。 实验表明，识别亚表型有助于构建ARDS的预测模型。 显示英文摘要

摘要： In this work, we utilize Machine Learning for early recognition of patients at high risk of acute respiratory distress syndrome (ARDS), which is critical for successful prevention strategies for this devastating syndrome. The difficulty in early ARDS recognition stems from its complex and heterogenous nature. In this study, we integrate knowledge of the heterogeneity of ARDS patients into predictive model building. Using MIMIC-III data, we first apply latent class analysis (LCA) to identify homogeneous sub-groups in the ARDS population, and then build predictive models on the partitioned data. The results indicate that significantly improved performances of prediction can be obtained for two of the three identified sub-phenotypes of ARDS. Experiments suggests that identifying sub-phenotypes is beneficial for building predictive model for ARDS.