Anti-Inflammatory Cardiovascular Trial Matching with LangChain and LangGraph
Targeted anti-inflammatory cardiovascular therapies require precise patient selection based on biomarker thresholds like IL-6 and hsCRP. Build a stateful trial-matching agent using LangChain and LangGraph to evaluate EHR data against eligibility criteria. Achieve 95% precision on clinical trial inclusion/exclusion evaluations.
What you are building
The core problem, expected build, and operating context for this challenge.
Build a stateful EHR trial matching agent using LangChain and LangGraph to identify anti-inflammatory cardiovascular therapy candidates.
How work is evaluated
Evaluates accuracy and precision of patient eligibility decisions against clinical trial protocols.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
decision_present
Output contains clear boolean eligible flag.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
precision
Precision of patient inclusion determinations • target: 0.95 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Build complex multi-step decision state graphs using LangGraph and LangChain
Parse and evaluate temporal biomarker trends (hsCRP, IL-6) from EHR data
Implement conditional branching for inclusion vs exclusion rule evaluation
Export transparent audit logs of trial eligibility determinations
Reference links and supporting material
Synthetic EHR dataset of 250 adult cardiac patients with longitudinal lab measurements for inflammatory markers.
Challenge setup
Evaluates accuracy and precision of patient eligibility decisions against clinical trial protocols.
Challenge input
JSON patient record containing medical history, hsCRP, IL-6 levels, and eGFR rates.
LangChain
Framework required for prompt chaining and tool integration.
LangGraph
Enables creation of state machines with dynamic branching for clinical logic.
Evaluated output
JSON object with eligible boolean, matched_criteria list, excluded_criteria list, and reason.
- Output contains clear boolean eligible flag.
- Precision of patient inclusion determinations • target: 0.95 • range: 0-1
- Benchmark: Clinical Trial Patient Eligibility Benchmark
- Precision target: 0.95
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
- Langchain · Required
- LangGraph · Optional
- HeyBoss AI · Optional
- Langchain · Required
- LangGraph · Optional
Evaluation contract
- decision_present · Weight 1
- precision · Weight 1
Metrics
- precision · Target 0.95
Benchmarks
- Clinical Trial Patient Eligibility Benchmark
- biotech
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON patient record containing medical history, hsCRP, IL-6 levels, and eGFR rates.
Output: JSON object with eligible boolean, matched_criteria list, excluded_criteria list, and reason.
Execution harness: Python
Evaluation tools
- LangChain · LLM Orchestration Toolkit
- LangGraph · Stateful Agent Workflow Engine
Recipe freeze marker
No recipe freeze time is recorded.