论文标题
暂时泊松平方根图形模型
Temporal Poisson Square Root Graphical Models
论文作者
论文摘要
我们提出了颞泊泊托根根图形模型（TPSQRS），这是专为建模纵向事件数据而设计的泊松平方根图形模型（PSQR）的概括。通过估计所有可能成对的事件类型的时间关系，TPSQR可以就任何给定事件类型的出现是否会激发或抑制任何其他类型的出现提供整体观点。通过估计共享相同模板参数化的相互关联的PSQR的集合来学习TPSQR。这些PSQR以伪样方式共同估计，在该方式中，泊松假单胞菌用于近似于原始的计算中更强化的伪可能类似的问题。从理论上讲，我们证明在轻度假设下，泊松伪样近似值对于恢复了基础PSQR而言是稀疏的。从经验上讲，我们从Marshfield Clinic电子健康记录（EHRS）中学习TPSQR，其中数以百万计的药物处方和疾病诊断事件，用于不良药物反应（ADR）检测。实验结果表明，学到的TPSQR可以有效，有效地从EHR中恢复ADR信号。
We propose temporal Poisson square root graphical models (TPSQRs), a generalization of Poisson square root graphical models (PSQRs) specifically designed for modeling longitudinal event data. By estimating the temporal relationships for all possible pairs of event types, TPSQRs can offer a holistic perspective about whether the occurrences of any given event type could excite or inhibit any other type. A TPSQR is learned by estimating a collection of interrelated PSQRs that share the same template parameterization. These PSQRs are estimated jointly in a pseudo-likelihood fashion, where Poisson pseudo-likelihood is used to approximate the original more computationally-intensive pseudo-likelihood problem stemming from PSQRs. Theoretically, we demonstrate that under mild assumptions, the Poisson pseudo-likelihood approximation is sparsistent for recovering the underlying PSQR. Empirically, we learn TPSQRs from Marshfield Clinic electronic health records (EHRs) with millions of drug prescription and condition diagnosis events, for adverse drug reaction (ADR) detection. Experimental results demonstrate that the learned TPSQRs can recover ADR signals from the EHR effectively and efficiently.
