How AdmitGuard Works
A transparent look into the machine learning pipeline, data flow, and deployment architecture powering the analytics engine.
The Inference Pipeline
Data Validation
Incoming patient records are strictly validated via Pydantic schemas. Missing values are imputed, and categorical variables are encoded using leakage-safe pipelines.
Readmission Modeling
A Logistic Regression model evaluates the patient's vitals, lab results, and encounter history to output a probability of 30-day readmission.
Claim Estimation
An independent Random Forest Regressor estimates potential insurance claim amounts based on demographic and historical features.
Explainability
SHAP values are computed to identify exactly which patient features drove the specific risk score.
Deployment Architecture
Next.js on Vercel
Handles UI rendering, state, and responsive data visualizations via Tailwind CSS.
FastAPI on Render
Manages API versioning, input validation, and executes the Joblib ML artifacts.
Scikit-Learn Joblib
Pre-trained on historical CSV dumps, serialized, and loaded into backend memory.