BioSentinel: Predictive Sepsis Early Warning Patch

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  Bio Sentinel

BioSentinel: Predictive Sepsis Early Warning Patch

Inspiration

Sepsis causes approximately deaths annually worldwide. What makes it deadly is not complexity — it is delayed detection.

Early physiological deterioration follows nonlinear multivariate trends:

Risk \propto f(HRV \downarrow, Temp \uparrow, RR \uparrow, BP \downarrow)

Yet current systems rely on threshold-based alerts:

Alert = \begin{cases} 1 & \text{if value} > threshold \ 0 & \text{otherwise} \end{cases}

We were inspired to shift from reactive alerts to predictive intelligence.

BioSentinel was created to detect deviation before collapse — turning subtle instability into actionable insight.

What it does

BioSentinel is a predictive AI-powered clinical intelligence system.

It continuously interprets multivariate physiological data such as:

Heart Rate Variability

Temperature trends

Respiratory rate

Blood pressure

Oxygen saturation

Instead of static thresholds, it analyzes deviation from patient baseline:

Deviation = |X_{current} - X_{baseline}|

It generates:

Differential diagnosis

Risk stratification

Feature attribution

Recommended clinical protocol

All powered by Gemini AI integration.

How we built it

We built BioSentinel using:

React + Vite frontend for real-time monitoring UI

Express backend for secure AI routing

Gemini API for multivariate clinical reasoning

Node.js for stable API communication

Our AI inference pipeline:

Input \rightarrow Prompt Structuring \rightarrow Gemini Model \rightarrow Clinical Output

We designed a ward-level dashboard to reduce cognitive overload and provide predictive prioritization.

Challenges we ran into

We encountered several major challenges:

1. API Model Compatibility

Different Gemini models supported different endpoints.

1. Transmission Stability

We implemented retry logic:

Retry(n) = 2^n \text{ seconds delay}

1. CORS & Backend Security

Direct frontend calls caused transmission failures, requiring backend architecture.

1. Model Access Restrictions

Some model IDs returned due to project-level access constraints.

Each obstacle strengthened our system reliability.

Accomplishments that we're proud of

Built a full-stack predictive diagnostic system in limited hackathon time

Implemented AI-based differential reasoning

Designed an intuitive ward overview interface

Stabilized backend with retry + timeout protection

Created structured clinical output formatting

We are proud that BioSentinel moves beyond monitoring into predictive intelligence.

What we learned

We learned that:

1. Clinical AI must be assistive, not autonomous.

2. Infrastructure stability is as important as model intelligence.

3. Baseline deviation modeling is more powerful than static threshold alerts.

4. Responsible AI requires transparency and structured reasoning.

We also learned the importance of system resilience:

Stability = Robust\ Code + Error\ Handling + Model\ Compatibility

What's next for BioSentinel: Predictive Sepsis Early Warning Patch

Our next steps include:

Clinical validation trials

Integration with electronic health records

Expansion to maternal and post-operative sepsis

Hardware wearable prototype development

Regulatory pathway under EU MDR

Long-term vision:

Healthcare_{future} = Predictive \quad > \quad Reactive

BioSentinel aims to ensure no patient deteriorates silently.

Built With

• express.js
• git
• node.js
• react.js
• rest-apis
• typescript
• vite