Scalable High-Recall Constraint-Satisfaction-Based Information Retrieval for Clinical Trials Matching
2026-04-10 • Computation and Language
Computation and LanguageArtificial IntelligenceDatabasesMultiagent SystemsSymbolic Computation
AI summaryⓘ
The authors developed SatIR, a new method to help find suitable clinical trials for patients more accurately and quickly. Unlike older methods that rely on keyword matching, SatIR uses formal logic and medical knowledge to better understand and match patient information with trial requirements. They also use advanced language models to clarify unclear or missing patient details. Tested on thousands of trials and dozens of patients, SatIR found more relevant trials faster than previous methods. This approach is both scalable and easier to interpret.
Clinical TrialsConstraint SatisfactionSatisfiability Modulo Theories (SMT)Relational AlgebraLarge Language Models (LLMs)Medical OntologiesTrial EligibilityInformation RetrievalPatient Profile MatchingInterpretability
Authors
Cyrus Zhou, Yufei Jin, Yilin Xu, Yu-Chiang Wang, Chieh-Ju Chao, Monica S. Lam
Abstract
Clinical trials are central to evidence-based medicine, yet many struggle to meet enrollment targets, despite the availability of over half a million trials listed on ClinicalTrials.gov, which attracts approximately two million users monthly. Existing retrieval techniques, largely based on keyword and embedding-similarity matching between patient profiles and eligibility criteria, often struggle with low recall, low precision, and limited interpretability due to complex constraints. We propose SatIR, a scalable clinical trial retrieval method based on constraint satisfaction, enabling high-precision and interpretable matching of patients to relevant trials. Our approach uses formal methods -- Satisfiability Modulo Theories (SMT) and relational algebra -- to efficiently represent and match key constraints from clinical trials and patient records. Beyond leveraging established medical ontologies and conceptual models, we use Large Language Models (LLMs) to convert informal reasoning regarding ambiguity, implicit clinical assumptions, and incomplete patient records into explicit, precise, controllable, and interpretable formal constraints. Evaluated on 59 patients and 3,621 trials, SatIR outperforms TrialGPT on all three evaluated retrieval objectives. It retrieves 32%-72% more relevant-and-eligible trials per patient, improves recall over the union of useful trials by 22-38 points, and serves more patients with at least one useful trial. Retrieval is fast, requiring 2.95 seconds per patient over 3,621 trials. These results show that SatIR is scalable, effective, and interpretable.