1,329 calculators and references across 60 specialties, with formula confidence shown up front.
— Explanation and Clinical Context The DeepCAD Risk Model here is presented as a transparent prototype intended to illustrate how an AI-based coronary artery disease (CAD) mortality predictor can be embedded into a clinical website tool. This prototype combines widely reported clinical predictors (age, sex, left ventricular ejection fraction, serum creatinine, diabetes, prior myocardial infarction, active smoking, an
DeepHeart (approximation) — Explanation and Clinical Context DeepHeart in the original publications is a semi-supervised deep learning pipeline trained on large amounts of wearable heart-rate time series and limited labeled clinical data to predict multiple cardiometabolic conditions and to flag abnormal rhythms. The model learns features from continuous heart rate and activity signals rather than relying solely on h
DeepSurv HF Prognostic Model — Explanation and Clinical Context DeepSurv is a Cox-proportional-hazards deep neural network that learns a nonlinear risk function from patient covariates and outputs individualized survival/hazard estimates. Applied to heart failure (HF), DeepSurv-based models can integrate many continuous and interacting predictors (clinical variables, biomarkers, and high-resolution signals) and may i