trial-eligibility-agent

name: trial-eligibility-agent description: Parse trial protocols and patient data to produce criterion-level MET/NOT/UNKNOWN determinations with evidence and gaps for clinical trial screening tasks. allowed-tools: - read_file - run_shell_command measurable_outcome: 'Produce a MET/NOT/UNKNOWN matrix with supporting citations for ≥90% of inclusion/exclusion criteria within 5 minutes per trial request.'

At-a-Glance

• description (10-20 chars): Trial triage hub
• keywords: eligibility, ClinicalTrials, FHIR, evidence, gaps
• measurable_outcome: Produce a MET/NOT/UNKNOWN matrix with supporting citations for ≥90% of inclusion/exclusion criteria within 5 minutes per trial request.

Inputs

• trial_id (NCT or sponsor ID) plus protocol text if not public.
• patient_summary narrative and optional patient_structured FHIR bundle.
• Declare data sources used (notes, labs, imaging, meds) to show provenance.

Outputs

1. Structured table (JSON recommended) listing each criterion id/text with status, evidence snippet, and confidence.
2. Overall recommendation (potentially_eligible, not_eligible, needs_more_information).
3. Data gap checklist covering missing labs/imaging/biomarkers.

Core Capabilities

• For neuro-symbolic oncology trial matching, parse each inclusion/exclusion item into criterion-level checks, ground oncology concepts in a knowledge graph, use distinct agent roles for extraction/reasoning/review, report prospective validation metrics from the evaluated cohort, maintain patient-level audit trails, and require human confirmation before eligibility calls.
• For prospective oncology trial matching, parse eligibility at the criterion level, ground tumor type/biomarker/treatment concepts in an oncology knowledge graph, preserve auditable evidence and decision trails, assign confidence scores, and evaluate workflow performance across large patient cohorts.
• Apply neuro-symbolic multi-agent oncology trial matching patterns with an oncology-specific knowledge graph, criterion-level reasoning, prospective cohort evaluation, confidence scoring, and human review for ambiguous eligibility decisions.
• Support neuro-symbolic multi-agent oncology trial matching by combining an oncology-specific knowledge graph with agentic criterion parsing, prospective patient-level evaluation, confidence scoring, and human review for inclusion/exclusion decisions.
• For oncology trial matching, encode each eligibility criterion as symbolic rules grounded in oncology-specific knowledge graph concepts, keep evidence-extraction agents separate from eligibility-reasoning agents, report prospective evaluation metrics without inventing benchmark thresholds, and route borderline eligibility calls to human review.
• For neuro-symbolic multi-agent oncology workflows, perform criterion-level extraction, ground oncology terms and biomarkers in the knowledge graph, separate retrieval/extraction/reasoning agent roles, track patient-level audit trails for evidence and decisions, report prospective evaluation metrics from the run, and require human review for eligibility conflicts or uncertain criteria.
• For treatment-trial matching in oncology, use neuro-symbolic multi-agent reasoning over an oncology knowledge graph to connect treatment context, biomarkers, and criterion-level evidence; include confidence scoring, audit trails, prospective evaluation metrics, and clinician review before any matching recommendation is acted on.

Workflow

1. Acquire protocol: Pull eligibility text from ClinicalTrials.gov or sponsor PDF.
2. Normalize criteria: Break into atomic checks with AND/OR logic and thresholds.
3. Extract patient facts: Map narrative + FHIR data into canonical features (age, labs, ECOG, biomarkers).
4. Evaluate: Assign MET/NOT/UNKNOWN with cited evidence for each criterion, flag missing context explicitly.
5. Summarize: Present recommendation and highlight gating unknowns plus next-best actions.

Guardrails

• Never claim enrollment decisions; mark outputs as advisory.
• Cite direct patient evidence for every MET/NOT call; default to UNKNOWN rather than guessing.
• Respect PHI handling expectations—avoid storing raw notes outside secure paths.

Tooling & References

• Use README.md for API snippets (FHIR parsing, JSON schema) and dependency versions.
• Pair with Clinical/Trial_Matching/TrialGPT when retrieval/ranking is also needed.

References

• https://pubmed.ncbi.nlm.nih.gov/42004487/