Biostream-ML(Real-Time Physiological Anomaly Detection Engine)

A high-throughput, clinically-grounded ML pipeline for detecting physiological deterioration in operating room (OR) settings.

Status: Production-Grade | Verified on VitalDB (23 Cases) | AUC: 0.99

Key Features

• Real-World Data Integration: Integrated with VitalDB, an open dataset of intraoperative metrics.
  • Automated Pipeline: Downloads, caches (Parquet), and normalizes data (HR, BP, SpO2) concurrently.
  • Clinical Ground Truth: Evaluation uses ICU-standard alarm thresholds (e.g., HR > 140, SpO2 < 90) to strictly validate ML predictions.
• Unsupervised Anomaly Detection:
  • Algorithm: Isolation Forest (tuned for high-dimensional physiological time-series).
  • Performance: AUC 0.99, Precision 0.97 on real surgical data.
• Production Architecture:
  • Event-Driven: Redis Streams + Async Workers (Scalable).
  • Idempotency: Strictly enforces 100% data integrity even during failures.
  • Resilience: Survives unexpected container kills with <5ms latency impact.

Architecture

graph TD
    A[VitalDB / Monitor] -->|Stream| B[Ingestion API-FastAPI]
    B -->|Event| C[Redis Streams]
    C -->|Consume| D[Worker Async]
    D -->|Inference| E[Isolation Forest Model]
    E -->|Anomaly Score| F[PostgreSQL-TimescaleDB]
    D -->|Persistence| F

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Installation

Prerequisites: Docker & Docker Compose.

1. Clone & Setup:

   git clone <repo>
   cd physio-engine
   cp .env.example .env

2. Start Services:

   docker compose up --build -d

Training & Evaluation (VitalDB)

The system is pre-configured to train on real surgical data.

1. Download & Cache Data: Fetches 30+ cases from VitalDB and saves to data/vitaldb_train.parquet.

   python3 data/vitaldb_loader.py

2. Train & Verify: Trains the Isolation Forest and evaluates AUC/Precision/Recall against clinical rules.

   python3 model/train.py --source vitaldb

Benchmarks

Metric	verified Value	Context
Throughput	~3,000 eps	Simulated Load
API Latency	2.8 ms (P95)	Under Stress
Data Integrity	100%	Zero Duplicates
ML AUC	0.991	VitalDB (Real)
Precision	0.973	VitalDB (Real)

Repo Structure

├── api/                 # FastAPI Ingestion Layer
├── worker/              # Async Processor (ML Inference)
├── model/               # ML Training & Evaluation Logic
│   └── train.py         # Main training script (VitalDB support)
├── data/                # Data Loaders
│   └── vitaldb_loader.py # Caching/Parallel Data Acquisition
├── db/                  # Database Schema
├── scripts/             # Integration Tests & Chaos Engineering
│   ├── chaos_test.sh    # Resilience Verification
│   └── verify_idempotency.py
├── docker-compose.yml   # Infrastructure orchestration
└── requirements.txt     # Python Dependencies

License

MIT License. Data provided via VitalDB (Creative Commons).