# TREEMENT: Interpretable Patient-Trial Matching via Personalized Dynamic Tree-Based Memory Network

arXiv: https://arxiv.org/abs/2307.09942 · HTML: https://mlsanity.com/paper/2307.09942/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 19 Jul 2023.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [IMRAM: Iterative Matching with Recurrent Attention Memory for Cross-Modal Image-Text Retrieval](https://mlsanity.com/paper/2003.03772/) (2020)
- [COMPOSE: Cross-Modal Pseudo-Siamese Network for Patient Trial Matching](https://mlsanity.com/paper/2006.08765/) (2020)
- [Learning Eligibility in Cancer Clinical Trials using Deep Neural Networks](https://mlsanity.com/paper/1803.08312/) (2018)
- [Tree Memory Networks for Modelling Long-term Temporal Dependencies](https://mlsanity.com/paper/1703.04706/) (2017)

## Built on by (most cited first, 2 of 2)

- [ACR: A Benchmark for Automatic Cohort Retrieval](https://mlsanity.com/paper/2406.14780/) (2024)
- [A Survey on LLM-Assisted Clinical Trial Recruitment](https://mlsanity.com/paper/2506.15301/) (2025)

## Abstract (arXiv)

Clinical trials are critical for drug development but often suffer from expensive and inefficient patient recruitment. In recent years, machine learning models have been proposed for speeding up patient recruitment via automatically matching patients with clinical trials based on longitudinal patient electronic health records (EHR) data and eligibility criteria of clinical trials. However, they either depend on trial-specific expert rules that cannot expand to other trials or perform matching at a very general level with a black-box model where the lack of interpretability makes the model results difficult to be adopted. To provide accurate and interpretable patient trial matching, we introduce a personalized dynamic tree-based memory network model named TREEMENT. It utilizes hierarchical clinical ontologies to expand the personalized patient representation learned from sequential EHR data, and then uses an attentional beam-search query learned from eligibility criteria embedding to offer a granular level of alignment for improved performance and interpretability. We evaluated TREEMENT against existing models on real-world datasets and demonstrated that TREEMENT outperforms the best baseline by 7% in terms of error reduction in criteria-level matching and achieves state-of-the-art results in its trial-level matching ability. Furthermore, we also show TREEMENT can offer good interpretability to make the model results easier for adoption.