Claudician

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.