README.md

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title: Oncology AI Insights emoji: 🧬 colorFrom: blue colorTo: green sdk: streamlit app_file: dashboard/app.py python_version: 3.9 tags: - streamlit - oncology - ai - dashboard - real-world-data license: mit

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AI-Powered Oncology Insights Platform

Overview

Live Demo: Hugging Face Space

This project demonstrates how AI and real-world data (RWD) can be leveraged to extract actionable insights in oncology. It simulates a workflow for clinical decision support, drug development, and patient outcome prediction using synthetic data and modern AI tools.

Project Workflow

flowchart TD
    A[Synthetic RWD Data] --> B[Data Preprocessing - Python Scripts]
    B --> C[Exploratory Analysis - Jupyter Notebooks]
    C --> D[AI Modeling - Notebooks and Scripts]
    D --> E[Model Outputs and Metrics]
    C --> F[Dashboard Visualization - Streamlit App]
    D --> F
    A -.->|Upload or Update| F

Features

• Synthetic Oncology Data: Simulated EHR, genomics, and treatment datasets.
• NLP Pipelines: Extract cancer staging, treatments, and adverse events from unstructured clinical notes.
• Predictive Modeling: Forecast patient survival or treatment response using XGBoost/LightGBM.
• Clustering: Identify patient subgroups with similar biomarker or treatment profiles.
• Interactive Dashboards: Visualize trends and model outputs with Streamlit or Dash.

Directory Structure

📦 oncology-ai-insights/
├── README.md                # Project overview, goals, and demo instructions
├── data/                    # Synthetic or anonymized RWD samples
├── notebooks/               # Jupyter notebooks for analysis and modeling
├── src/                     # Python scripts for preprocessing, modeling, and visualization
├── models/                  # Saved model files and evaluation metrics
├── dashboard/               # Streamlit or Dash app code
└── docs/                    # Documentation and references

Getting Started

1. Clone the repository.
2. Install requirements (see docs/requirements.txt or notebook cells).
3. Explore the notebooks for data analysis and modeling.
   • View the static data exploration notebook (HTML, GitHub Pages)
   • Download the HTML notebook (raw file)
4. Run the dashboard app for interactive visualizations.

AI Tools & Techniques

Tool/Technique	Purpose
NLP	Extract clinical features from text
XGBoost/LightGBM	Predict outcomes
K-Means/DBSCAN	Patient subgroup discovery
Plotly/Streamlit	Data visualization and dashboards
Faker/Synthea	Synthetic data generation

Impact

This project illustrates how AI can:

• Support oncologists in making data-driven decisions
• Identify clinical trial candidates
• Uncover hidden patterns in oncology RWD

Credits

Inspired by real-world oncology data science and Ontada-style data models.

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For demo purposes only. No real patient data is used.