SentinelRx

An AI-powered pharmacovigilance engine that uses MedGemma-7B for clinical grounding and SIDER benchmarking to detect real-time drug safety signals.

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Inspiration

Traditional pharmacovigilance (drug safety monitoring) is often reactive and slow, relying on manual reports that take months to process. We were inspired to build SentinelRx to create a "Real-Time Nervous System" for drug safety—one that identifies emerging side effects from both official FDA streams and social medical discussions (/r/Medicine) before they become public health crises.

What it does

SentinelRx is an autonomous surveillance engine that:

Ingests massive streams of data from Kafka, including FDA FAERS reports and medical social media. Analyzes text using BioBERT to extract specific drug-adverse event pairs with clinical precision. Cross-references signals against the SIDER dataset to distinguish between "Known" side effects and "Novel" emerging risks. Generates high-fidelity clinical narratives using MedGemma-7B reasoning, providing doctors with pharmacological explanations for why a specific reaction is occurring.

How we built it

We architected a high-concurrency pipeline:

Back-end: Python and FastAPI coupled with Kafka for stream processing. Analytics: DuckDB for lightning-fast disproportionality calculations (PRR/ROR). AI Stack: BioBERT for Named Entity Recognition, GPT-4o for narrative synthesis, and a simulated MedGemma-7B layer for specialized clinical grounding. Front-end: A premium Glassmorphism Neon Dashboard built with Vanilla CSS/JS for a high-performance, immersive medical monitoring experience.

Challenges we ran into

One of our biggest hurdles was "Signal Noise"—differentiating between a random report and a statistically significant safety signal. We solved this by implementing a rigorous disproportionality analysis engine. Additionally, simulating the specialized clinical reasoning of MedGemma without requiring massive 15GB local downloads was a complex engineering feat that involved grounding our primary LLM with curated medical knowledge-bases.

Accomplishments that we're proud of

We are particularly proud of our "Uniqueness Pipeline": the integration of MedGemma reasoning and SIDER benchmarking. Seeing the system correctly identify a "Novel" signal and then explain the underlying cytokine modulation or receptor pathway feels like looking into the future of automated medicine.

What we learned

Building SentinelRx taught us that for high-stakes fields like healthcare, generic AI isn't enough. Domain-specific grounding (like BioBERT and MedGemma) is essential for accuracy. We also learned how to bridge the gap between "Big Data" (millions of FDA records) and "Human-Readable Insights" (a 1-page clinical narrative).

What's next for SentinelRx

The next step for SentinelRx is deep integration with Private Electronic Health Records (EHR) to move from "population-level" signals to "person-specific" safety alerts. We also plan to deploy the full MedGemma-7B model on dedicated medical hardware to further heighten the precision of our clinical reasoning engine.

Built With

  • api
  • containerization:
  • css
  • datasets:
  • docker
  • duckdb-(olap-analytics)-frameworks:-fastapi
  • external
  • gpt-4o-(synthesis)
  • html-ai/nlp-models:-biobert-(ner)
  • javascript
  • langchain-apis:-fda-openapi
  • medgemma-7b-(clinical-reasoning)-data-infrastructure:-apache-kafka-(streaming)
  • praw
  • python
  • reddit
  • sider
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