metadata
sdk: gradio
title: 🔬 Forensic Triage & Postmortem Intelligence System
emoji: 🔬
colorFrom: red
colorTo: gray
sdk_version: 6.14.0
python_version: '3.13'
app_file: app.py
pinned: false
tags:
- ml-intern
- forensics
- nlp
- investigation
- medical
short_description: AI-powered forensic investigation support system
🔬 AI-Powered Forensic Triage & Postmortem Intelligence System
An intelligent investigative support system that integrates AI, NLP, and data correlation techniques to assist forensic investigations.
Key Features
| Module | Description |
|---|---|
| 📄 Autopsy Report NLP | Extracts forensic entities (cause of death, injuries, toxicology) from unstructured reports |
| ⏱️ Time-of-Death Estimation | Henssge nomogram + postmortem indicators for PMI calculation |
| 📱 Digital Evidence Correlation | Correlates CCTV, mobile metadata, and geolocation data |
| ⚠️ Risk Scoring & Anomaly Detection | Multi-factor risk assessment with pattern identification |
| 📅 Investigation Timeline | Integrated visualization combining all evidence sources |
How to Use
- Autopsy Report: Upload or paste an autopsy report → system extracts key forensic entities
- Time of Death: Enter body temperature and postmortem signs → get PMI estimate with cooling curve
- Digital Evidence: Upload CCTV/mobile logs as CSV → correlations and patterns identified
- Risk Score: After analyzing evidence, compute multi-factor risk score
- Timeline: Build an integrated timeline from all evidence sources
Methodology
- Henssge Nomogram (1988): Double-exponential body cooling model for PMI
- Pattern-based NLP: Forensic-domain entity recognition with confidence scoring
- Multi-factor Risk Engine: Weighted scoring across violence, toxicology, digital evidence, temporal consistency
- Anomaly Detection: Identifies cross-factor inconsistencies (e.g., defensive wounds without homicide classification)
⚠️ Disclaimer
This system is strictly an investigative assistance platform. It is NOT a replacement for forensic experts, medical professionals, or legal authorities. All outputs are intended to support human decision-making, not automate legal conclusions.
Technical Stack
- Frontend: Gradio
- NLP: Regex-based forensic entity extraction
- Math: Scipy (Henssge solver), NumPy
- Visualization: Plotly (interactive charts, timelines, radar plots)
- Data: Pandas for evidence processing