Bayesian hierarchical vector autoregressive models for patient-level predictive modeling
Bayesian hierarchical vector autoregressive models for patient-level predictive modeling
复制标题
用于患者水平预测建模的贝叶斯分层向量自回归模型
作者：
Feihan Lu;Yao Zheng;H. Cleveland;C. Burton;D. Madigan
从纵向健康史预测健康结果对医疗保健至关重要。诸如患者日记数据库的观察性医疗保健数据库为患者级预测建模提供了丰富的资源。在本文中，我们提出了一个贝叶斯分层向量自回归（VAR）模型预测医疗和心理状况，使用多变量时间序列数据。与现有的患者特异性预测VAR模型相比，由于分层模型规范的合并效应，我们的模型在预测未来观察结果的点估计值和区间估计值方面表现出更高的准确性。此外，通过采用弹性网络先验，我们的模型在人群水平和患者水平上以及患者间异质性上对感兴趣的变量之间的关联提供了更好的解释性。我们将该模型应用于两个例子：1）预测大学生的物质使用渴望，负面情绪和烟草使用，2）预测功能性躯体症状和心理不适。
Predicting health outcomes from longitudinal health histories is of central importance to healthcare. Observational healthcare databases such as patient diary databases provide a rich resource for patient-level predictive modeling. In this paper, we propose a Bayesian hierarchical vector autoregressive (VAR) model to predict medical and psychological conditions using multivariate time series data. Compared to the existing patient-specific predictive VAR models, our model demonstrated higher accuracy in predicting future observations in terms of both point and interval estimates due to the pooling effect of the hierarchical model specification. In addition, by adopting an elastic-net prior, our model offers greater interpretability about the associations between variables of interest on both the population level and the patient level, as well as between-patient heterogeneity. We apply the model to two examples: 1) predicting substance use craving, negative affect and tobacco use among college students, and 2) predicting functional somatic symptoms and psychological discomforts.
影响因子：
120.7
作者：
Kansagara, Devan;Englander, Honora;Salanitro, Amanda;Kagen, David;Theobald, Cecelia;Freeman, Michele;Kripalani, Sunil
通讯作者：
Kripalani, Sunil