# COMPOSE: Cross-Modal Pseudo-Siamese Network for Patient Trial Matching

arXiv: https://arxiv.org/abs/2006.08765 · HTML: https://mlsanity.com/paper/2006.08765/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2020.

## Built on by (most cited first, 30 of 37)

- [M3Care: Learning with Missing Modalities in Multimodal Healthcare Data](https://mlsanity.com/paper/2210.17292/) (2022)
- [Matching Patients to Clinical Trials with Large Language Models](https://mlsanity.com/paper/2307.15051/) (2023)
- [HINT: Hierarchical Interaction Network for Trial Outcome Prediction Leveraging Web Data](https://mlsanity.com/paper/2102.04252/) (2021)
- [Zero-Shot Clinical Trial Patient Matching with LLMs](https://mlsanity.com/paper/2402.05125/) (2024)
- [Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies](https://mlsanity.com/paper/2107.09951/) (2021)
- [ClinicalAgent: Clinical Trial Multi-Agent System with Large Language Model-based Reasoning](https://mlsanity.com/paper/2404.14777/) (2024)
- [Large Language Models for Healthcare Data Augmentation: An Example on Patient-Trial Matching](https://mlsanity.com/paper/2303.16756/) (2023)
- [MediTab: Scaling Medical Tabular Data Predictors via Data Consolidation, Enrichment, and Refinement](https://mlsanity.com/paper/2305.12081/) (2023)
- [Scaling Clinical Trial Matching Using Large Language Models: A Case Study in Oncology](https://mlsanity.com/paper/2308.02180/) (2023)
- [Large Language Models in Drug Discovery and Development: From Disease Mechanisms to Clinical Trials](https://mlsanity.com/paper/2409.04481/) (2024)
- [Deep High-Resolution Representation Learning for Cross-Resolution Person Re-identification](https://mlsanity.com/paper/2105.11722/) (2021)
- [SPOT: Sequential Predictive Modeling of Clinical Trial Outcome with Meta-Learning](https://mlsanity.com/paper/2304.05352/) (2023)
- [A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises](https://mlsanity.com/paper/2306.04802/) (2023)
- [Panacea: A foundation model for clinical trial search, summarization, design, and recruitment](https://mlsanity.com/paper/2407.11007/) (2024)
- [Dynamic Uncertainty-aware Multimodal Fusion for Outdoor Health Monitoring](https://mlsanity.com/paper/2508.09085/) (2025)
- [AUTOCT: Automating Interpretable Clinical Trial Prediction with LLM Agents](https://mlsanity.com/paper/2506.04293/) (2025)
- [Multimodal Clinical Trial Outcome Prediction with Large Language Models](https://mlsanity.com/paper/2402.06512/) (2024)
- [DrugCLIP: Contrastive Drug-Disease Interaction For Drug Repurposing](https://mlsanity.com/paper/2407.02265/) (2024)
- [Artificial Intelligence for In Silico Clinical Trials: A Review](https://mlsanity.com/paper/2209.09023/) (2022)
- [FRAMM: Fair Ranking with Missing Modalities for Clinical Trial Site Selection](https://mlsanity.com/paper/2305.19407/) (2023)
- [Label Dependent Attention Model for Disease Risk Prediction Using Multimodal Electronic Health Records](https://mlsanity.com/paper/2201.06779/) (2022)
- [TrialBench: Multi-Modal Artificial Intelligence-Ready Clinical Trial Datasets](https://mlsanity.com/paper/2407.00631/) (2024)
- [Towards Fair Patient-Trial Matching via Patient-Criterion Level Fairness Constraint](https://mlsanity.com/paper/2303.13790/) (2023)
- [ADSNet: Cross-Domain LTV Prediction with an Adaptive Siamese Network in Advertising](https://mlsanity.com/paper/2406.10517/) (2024)
- [TrialEnroll: Predicting Clinical Trial Enrollment Success with Deep & Cross Network and Large Language Models](https://mlsanity.com/paper/2407.13115/) (2024)
- [Machine Learning Applications for Therapeutic Tasks with Genomics Data](https://mlsanity.com/paper/2105.01171/) (2021)
- [PyTrial: Machine Learning Software and Benchmark for Clinical Trial Applications](https://mlsanity.com/paper/2306.04018/) (2023)
- [TREEMENT: Interpretable Patient-Trial Matching via Personalized Dynamic Tree-Based Memory Network](https://mlsanity.com/paper/2307.09942/) (2023)
- [LLM-Match: An Open-Sourced Patient Matching Model Based on Large Language Models and Retrieval-Augmented Generation](https://mlsanity.com/paper/2503.13281/) (2025)
- [A Survey on LLM-Assisted Clinical Trial Recruitment](https://mlsanity.com/paper/2506.15301/) (2025)

## Abstract (arXiv)

Clinical trials play important roles in drug development but often suffer from expensive, inaccurate and insufficient patient recruitment. The availability of massive electronic health records (EHR) data and trial eligibility criteria (EC) bring a new opportunity to data driven patient recruitment. One key task named patient-trial matching is to find qualified patients for clinical trials given structured EHR and unstructured EC text (both inclusion and exclusion criteria). How to match complex EC text with longitudinal patient EHRs? How to embed many-to-many relationships between patients and trials? How to explicitly handle the difference between inclusion and exclusion criteria? In this paper, we proposed CrOss-Modal PseudO-SiamEse network (COMPOSE) to address these challenges for patient-trial matching. One path of the network encodes EC using convolutional highway network. The other path processes EHR with multi-granularity memory network that encodes structured patient records into multiple levels based on medical ontology. Using the EC embedding as query, COMPOSE performs attentional record alignment and thus enables dynamic patient-trial matching. COMPOSE also introduces a composite loss term to maximize the similarity between patient records and inclusion criteria while minimize the similarity to the exclusion criteria. Experiment results show COMPOSE can reach 98.0% AUC on patient-criteria matching and 83.7% accuracy on patient-trial matching, which leads 24.3% improvement over the best baseline on real-world patient-trial matching tasks.