EHR-AI Heart Failure Onset Predictor

EHR–AI Heart Failure Onset Predictor — Prototype Explanation and Clinical Context This page implements a transparent, explainable prototype risk-index (0–100) that combines common, routinely recorded EHR variables (age, sex, BMI, systolic BP, heart rate, hypertension, diabetes, coronary disease, atrial fibrillation, renal function, smoking and optional natriuretic peptide level). The index is computed as a weighted sum of risk contributors and normalized to 0–100 for easy interpretation; categories are provided as Low / Moderate / High (prototype only). This tool is intentionally not a trained machine-learning model and does not provide a calibrated probability estimate.

Modern EHR–AI heart failure onset predictors reported in the literature typically use large longitudinal EHR cohorts, temporal models (LSTM/GRU/transformers), code embeddings or combined structured + unstructured data, and require systematic external validation and calibration before clinical use. The weights in this prototype were chosen to reflect direction and relative importance reported across multiple studies but were not learned from a labeled dataset; therefore, the output should be treated only as an illustrative risk index for educational / prototyping purposes. Key implementation & clinical notes: • Use only for demonstration, research prototyping, or to support generation of hypotheses—do not use for clinical decisions. • For a deployable EHR–AI predictor you need: (1) a large longitudinal labeled cohort, (2) proper feature engineering (temporal windows, code embeddings), (3) model training/validation with held-out sites, (4) calibration and decision-curve analysis, and (5) prospective clinical impact assessment.

See references below for exemplar approaches and considerations.