# Claudician

- **Event:** [Built with Claude: Life Sciences](https://cerebralvalley.ai/e/built-with-claude-life-sciences)
- **When:** Jul 7 at 12:00 PM – Jul 14 at 12:00 AM (EDT)
- **Where:** Online
- **Team:** [Daniel Gallardo](https://cerebralvalley.ai/u/dgalgom)
- **GitHub:** https://github.com/dgalgom/clinical-digital-twin
- **Demo video:** https://youtu.be/u8PsLidXqMY
- **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/51

I built Clinical Digital Twin, an open-source R system (Shiny/plotly dashboard, plumber API, SQLite, and a Telegram bot powered by Claude) that gives clinicians in elder-care institutions a personalized, interpretable model of each resident's fall risk. An engineered pooled-logistic-regression digital twin ingests simulated wearable data (heart rate, blood pressure, steps, sedentary time) and clinical history to predict fall probability at 24 hours and 7 days, and answers counterfactual "what-if" questions in real time (e.g., what happens to risk if mobility increases or a medication is deprescribed).
I found that a single, transparent model can serve two very different interfaces (a visual dashboard and a natural-language Telegram bot) without sacrificing interpretability, and that grounding Claude's replies in real model output rather than free generation keeps the bot clinically trustworthy. We also surfaced a modeling caveat worth reporting: under our synthetic data-generating process, static risk factors are only identifiable through their effect on sensor trends, not directly; a reminder that interpretability claims need statistical scrutiny, not just a simple model.
This matters because falls are a leading cause of injury death in older adults, and institutional fall-prevention often fails not from data scarcity but from data never reaching a clinician in time.

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