mlsanityData to 7 Oct 2026

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

arXiv 2006.08765 ↗First posted 15 Jun 2020cs.LG cs.AIcited by 37

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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.

Key facts. Cited by 37 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Jun 2020.

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Citing papers per year: 2021: 4; 2022: 3; 2023: 10; 2024: 10; 2025: 10.

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  1. 2022M3Care: Learning with Missing Modalities in Multimodal Healthcare Datacited by 71
  2. 2023Matching Patients to Clinical Trials with Large Language Modelscited by 68
  3. 2021HINT: Hierarchical Interaction Network for Trial Outcome Prediction Leveraging Web Datacited by 49
  4. 2024Zero-Shot Clinical Trial Patient Matching with LLMscited by 35
  5. 2021Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologiescited by 34
  6. 2024ClinicalAgent: Clinical Trial Multi-Agent System with Large Language Model-based Reasoningcited by 31
  7. 2023Large Language Models for Healthcare Data Augmentation: An Example on Patient-Trial Matchingcited by 30
  8. 2023MediTab: Scaling Medical Tabular Data Predictors via Data Consolidation, Enrichment, and Refinementcited by 24
  9. 2023Scaling Clinical Trial Matching Using Large Language Models: A Case Study in Oncologycited by 24
  10. 2024Large Language Models in Drug Discovery and Development: From Disease Mechanisms to Clinical Trialscited by 24
  11. 2021Deep High-Resolution Representation Learning for Cross-Resolution Person Re-identificationcited by 14
  12. 2023SPOT: Sequential Predictive Modeling of Clinical Trial Outcome with Meta-Learningcited by 13
25 more
  1. 2023A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promisescited by 13
  2. 2024Panacea: A foundation model for clinical trial search, summarization, design, and recruitmentcited by 10
  3. 2025Dynamic Uncertainty-aware Multimodal Fusion for Outdoor Health Monitoringcited by 10
  4. 2025AUTOCT: Automating Interpretable Clinical Trial Prediction with LLM Agentscited by 9
  5. 2024Multimodal Clinical Trial Outcome Prediction with Large Language Modelscited by 7
  6. 2024DrugCLIP: Contrastive Drug-Disease Interaction For Drug Repurposingcited by 7
  7. 2022Artificial Intelligence for In Silico Clinical Trials: A Reviewcited by 6
  8. 2023FRAMM: Fair Ranking with Missing Modalities for Clinical Trial Site Selectioncited by 6
  9. 2022Label Dependent Attention Model for Disease Risk Prediction Using Multimodal Electronic Health Recordscited by 5
  10. 2024TrialBench: Multi-Modal Artificial Intelligence-Ready Clinical Trial Datasetscited by 5
  11. 2023Towards Fair Patient-Trial Matching via Patient-Criterion Level Fairness Constraintcited by 4
  12. 2024ADSNet: Cross-Domain LTV Prediction with an Adaptive Siamese Network in Advertisingcited by 4
  13. 2024TrialEnroll: Predicting Clinical Trial Enrollment Success with Deep & Cross Network and Large Language Modelscited by 4
  14. 2021Machine Learning Applications for Therapeutic Tasks with Genomics Datacited by 3
  15. 2023PyTrial: Machine Learning Software and Benchmark for Clinical Trial Applicationscited by 3
  16. 2023TREEMENT: Interpretable Patient-Trial Matching via Personalized Dynamic Tree-Based Memory Networkcited by 2
  17. 2025LLM-Match: An Open-Sourced Patient Matching Model Based on Large Language Models and Retrieval-Augmented Generationcited by 2
  18. 2025A Survey on LLM-Assisted Clinical Trial Recruitmentcited by 2
  19. 2024Language Interaction Network for Clinical Trial Approval Estimationcited by 1
  20. 2025MEXA-CTP: Mode Experts Cross-Attention for Clinical Trial Outcome Predictioncited by 1
  21. 2025Recommending Clinical Trials for Online Patient Cases using Artificial Intelligencecited by 1
  22. 2025PyTDC: A multimodal machine learning training, evaluation, and inference platform for biomedical foundation modelscited by 1
  23. 2025TrialMatchAI: An End-to-End AI-powered Clinical Trial Recommendation System to Streamline Patient-to-Trial Matchingcited by 1
  24. 2025A Gift from the Integration of Discriminative and Diffusion-based Generative Learning: Boundary Refinement Remote Sensing Semantic Segmentationcited by 1
  25. 2025CLaDMoP: Learning Transferrable Models from Successful Clinical Trials via LLMscited by 0