Early semi-supervised sequence model for estimating cardiovascular and metabolic risk directly from consumer wearable sensor data (heart rate, step count, and activity level), rather than from clinical-grade ECG or imaging. A multi-task long short-term memory (LSTM) network is first pretrained using semi-supervised sequence learning or heuristic pretraining on unlabeled wearable time series, then fine-tuned to jointly predict four self-reported conditions: diabetes, high cholesterol, high blood pressure, and sleep apnea. Trained and validated on 57,675 person-weeks of data from participants in UCSF's Health eHeart study using the Cardiogram app on Fitbit, Apple Watch, or Android Wear devices, DeepHeart outperformed hand-engineered heart-rate-variability biomarkers from the medical literature, reaching AUROCs of 0.845 (diabetes), 0.744 (high cholesterol), 0.809 (high blood pressure), and 0.830 (sleep apnea). The paper was an early demonstration that population-scale, passively-collected wearable heart-rate data could support cardiometabolic risk screening without any dedicated clinical measurement.