SatIR: New Constraint-Satisfaction Method for Clinical Trial Retrieval
A recent study introduces SatIR, a scalable approach for retrieving clinical trials by treating eligibility criteria as formal constraints. Detailed in arXiv:2604.08849, this method tackles the issue of aligning patients with trials, a vital task since many studies fail to reach their enrollment goals, despite over half a million trials available on ClinicalTrials.gov, which attracts nearly two million users each month. Current methods often struggle with low recall and precision. By utilizing medical ontologies and formal constraint satisfaction, SatIR enhances accuracy. Announced as a replace-cross type on arXiv, the paper, submitted in April 2026, aims to improve the efficiency of clinical trial matching and suggests applicability to other fields, such as job matching.
Key facts
- SatIR is a new method for clinical trial retrieval based on formal constraint satisfaction.
- It addresses the problem of matching patients to clinical trials, which often have strict eligibility criteria.
- ClinicalTrials.gov lists over half a million trials and attracts about two million users monthly.
- Existing retrieval techniques use keyword and embedding-similarity matching, treating eligibility as soft signals.
- SatIR aims for high precision, high recall, and interpretability.
- It leverages established medical ontologies.
- The paper is available on arXiv with identifier 2604.08849.
- The announcement type is 'replace-cross', indicating a revision.
Entities
Institutions
- arXiv
- ClinicalTrials.gov