Massive electronic health records (EHRs) enable the success of learning
accurate patient representations to support various predictive health
applications. In contrast, doctor representation was not well studied despite
that doctors play pivotal roles in healthcare. How to construct the right
doctor representations? How to use doctor representation to solve important
health analytic problems? In this work, we study the problem on {\it clinical
trial recruitment}, which is about identifying the right doctors to help
conduct the trials based on the trial description and patient EHR data of those
doctors. We propose doctor2vec which simultaneously learns 1) doctor
representations from EHR data and 2) trial representations from the description
and categorical information about the trials. In particular, doctor2vec
utilizes a dynamic memory network where the doctor's experience with patients
are stored in the memory bank and the network will dynamically assign weights
based on the trial representation via an attention mechanism. Validated on
large real-world trials and EHR data including 2,609 trials, 25K doctors and
430K patients, doctor2vec demonstrated improved performance over the best
baseline by up to $8.7\%$ in PR-AUC. We also demonstrated that the doctor2vec
embedding can be transferred to benefit data insufficiency settings including
trial recruitment in less populated/newly explored country with $13.7\%$
improvement or for rare diseases with $8.1\%$ improvement in PR-AUC.

本文提出 doctor2vec，利用临床试验描述和分类信息以及医生从 EHR 数据中学习医生代表的动态记忆网络，为临床试验招募合适的医生提供了方法，实验证明其性能优于最佳基线，而 doctor2vec 嵌入式还可用于数据不足的情况。