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title: Clinical Trial Patient Screening
emoji: 🧪
colorFrom: green
colorTo: indigo
sdk: docker
pinned: false
app_port: 8000
tags:
- openenv
short_description: RL environment for clinical trial patient screening.
---
# Clinical Trial Patient Screening Environment
OpenEnv environment for clinical trial patient screening with three deterministic healthcare tasks:
- `easy`: binary eligibility screening against 5 criteria
- `medium`: ranking 3 patients by protocol fit
- `hard`: protocol deviation and exclusion detection from unstructured clinical text
This environment is designed as a real-world screening workflow rather than a toy game. It uses typed Pydantic models, deterministic programmatic graders, incremental reward shaping for correct data extraction, and terminal rewards for correct screening outcomes.
## Task Overview
### Easy
EGFR-mutated metastatic NSCLC eligibility check using structured oncology data.
### Medium
Rank 3 HER2-positive metastatic breast cancer candidates by fit for a trial.
### Hard
Detect subtle exclusions from an AML screening note, including protocol deviations such as recent investigational treatment, active infection, QTc prolongation, and CYP3A4 inhibitor exposure.
## Reward Design
- `+0.20` for each correct clinical data point or valid deviation extracted
- `+1.00` for a correct final screening decision
- `-0.50` for hallucinated fields, invalid deviation claims, or destructive actions
Each task also produces a deterministic grader score in `(0.0, 1.0)`.
## Project Structure
```text
clinical_trial_env/
├── .env
├── Dockerfile
├── README.md
├── __init__.py
├── client.py
├── env.py
├── inference.py
├── models.py
├── openenv.yaml
├── pyproject.toml
└── server/
├── __init__.py
├── app.py
└── clinical_trial_env_environment.py
```
## Build And Run
Build the container from the project root:
```bash
docker build -t clinical-trial-env:latest .
```
Run the server locally:
```bash
docker run --rm -p 8000:8000 clinical-trial-env:latest
```
Validate the environment:
```bash
openenv validate .
```
## Inference
The root [inference.py](/Users/abhishekkanade/Desktop/Hackathon/OpenEnv/clinical_trial_env/inference.py) uses the OpenAI client and emits exactly:
- `[START]`
- `[STEP]`
- `[END]`
Required environment variables:
- `HF_TOKEN`
- `LOCAL_IMAGE_NAME` or `ENV_BASE_URL`
- `API_BASE_URL` optional, defaults to Hugging Face router
- `MODEL_NAME` optional
- `CLINICAL_TRIAL_TASK` with values `easy`, `medium`, or `hard`
Example:
```bash
set -a
source .env
set +a
python3 inference.py
```
## Python Usage
```python
import asyncio
from clinical_trial_env import ClinicalTrialAction, ClinicalTrialEnv
async def main() -> None:
env = await ClinicalTrialEnv.from_docker_image("clinical-trial-env:latest")
try:
result = await env.reset(task_id="easy")
result = await env.step(
ClinicalTrialAction(action_type="extract_data", field_name="age", value="56")
)
print(result.reward, result.observation.reward_details.grader_score)
finally:
await env.close()
asyncio.run(main())
```
## Audit Notes
- `reset()`, `step(action)`, and OpenEnv `state` access are implemented in [env.py](/Users/abhishekkanade/Desktop/Hackathon/OpenEnv/clinical_trial_env/env.py).
- Gold answers are not exposed through observation metadata.
- Graders are deterministic and bounded in `(0.0, 1.0)`.
- The hard task uses unstructured medical text and clinically realistic exclusion criteria.
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