Anti-Inflammatory Clinical Trial Matching Triage with Pydantic AI
Screening patients for cardiovascular clinical trials targeting inflammatory pathways demands robust criteria validation. Build a type-safe Pydantic AI triage system managed with OpenHands to validate patient EHR records against complex inclusion rules for IL-6 targeted biological therapy.
What you are building
Construct a type-safe clinical trial eligibility workflow using Pydantic AI and OpenHands to evaluate cardiovascular patient cohorts for anti-inflammatory trials.
How work is evaluated
Evaluates Pydantic schema validation accuracy and correct clinical eligibility matching.
Datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
Pydantic Parsing Validation
Verifies output parses cleanly into target Pydantic BaseModel.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Matching Accuracy
Percentage of correct inclusion/exclusion patient categorizations. • target: 0.98 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Learning goals
Implement type-safe AI agents with Pydantic AI and validated Pydantic output models.
Integrate OpenHands autonomous execution environments for data verification tasks.
Parse clinical trial inclusion/exclusion criteria into strict rule validation logic.
Evaluate patient biomarker levels (hsCRP, IL-6) against eligibility thresholds.
Resources
EHR cohort dataset containing patient laboratory records (hsCRP, IL-6, renal function) and clinical history for trial matching.
Challenge setup
Evaluates Pydantic schema validation accuracy and correct clinical eligibility matching.
Challenge input
JSON patient record containing hsCRP level, eGFR, prior ASCVD history
Pydantic AI
Provides type-safe structured outputs and validation.
OpenHands
Enables reproducible agent execution and environment management.
Evaluated output
JSON verified by Pydantic model containing is_eligible, failed_criteria, match_confidence
- Verifies output parses cleanly into target Pydantic BaseModel.
- Percentage of correct inclusion/exclusion patient categorizations. • target: 0.98 • range: 0-1
- Benchmark: TrialMatch-Bench
- Matching Accuracy target: 0.98
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
Shows the first task, up to three tools, and up to three evidence items.
View technical recipe
Configured tools
- OpenHands · Optional
- Pydantic AI · Required
- OpenHands · Optional
- Darts (Time Series) · Optional
- Pydantic AI · Required
- OpenHands · Optional
Evaluation contract
- Pydantic Parsing Validation · Weight 1
- Matching Accuracy · Weight 1
Metrics
- Matching Accuracy · Target 0.98
Benchmarks
- TrialMatch-Bench
- biotech
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON patient record containing hsCRP level, eGFR, prior ASCVD history
Output: JSON verified by Pydantic model containing is_eligible, failed_criteria, match_confidence
Execution harness: Python
Evaluation tools
- Pydantic AI · Agent Framework
- OpenHands · Agent Execution Sandbox
Recipe freeze marker
No recipe freeze time is recorded.