# Doctor2Vec: Dynamic Doctor Representation Learning for Clinical Trial Recruitment

arXiv: https://arxiv.org/abs/1911.10395 · HTML: https://mlsanity.com/paper/1911.10395/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2019.

## Built on by (most cited first, 9 of 9)

- [HINT: Hierarchical Interaction Network for Trial Outcome Prediction Leveraging Web Data](https://mlsanity.com/paper/2102.04252/) (2021)
- [MeSIN: Multilevel Selective and Interactive Network for Medication Recommendation](https://mlsanity.com/paper/2104.11026/) (2021)
- [FRAMM: Fair Ranking with Missing Modalities for Clinical Trial Site Selection](https://mlsanity.com/paper/2305.19407/) (2023)
- [Clinical trial site matching with improved diversity using fair policy learning](https://mlsanity.com/paper/2204.06501/) (2022)
- [PyTrial: Machine Learning Software and Benchmark for Clinical Trial Applications](https://mlsanity.com/paper/2306.04018/) (2023)
- [Graph AI in Medicine](https://mlsanity.com/paper/2310.13767/) (2023)
- [Language Interaction Network for Clinical Trial Approval Estimation](https://mlsanity.com/paper/2405.06662/) (2024)
- [MEXA-CTP: Mode Experts Cross-Attention for Clinical Trial Outcome Prediction](https://mlsanity.com/paper/2501.06823/) (2025)
- [CLaDMoP: Learning Transferrable Models from Successful Clinical Trials via LLMs](https://mlsanity.com/paper/2505.18527/) (2025)

## Abstract (arXiv)

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.