README.md

metadata

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

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:

docker build -t clinical-trial-env:latest .

Run the server locally:

docker run --rm -p 8000:8000 clinical-trial-env:latest

Validate the environment:

openenv validate .

Inference

The root 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:

set -a
source .env
set +a
python3 inference.py

Python Usage

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.
• 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.