The "Predicting Hypertension Onset" solution leverages advanced machine learning techniques to analyze longitudinal electronic health records (EHRs and predict the onset of hypertension. By integrating deep learning models, particularly Long Short-Term Memory (LSTM networks, this tool processes temporal sequences of patient data to identify patterns indicative of future hypertension development. This predictive capability enables healthcare providers to implement early interventions, potentially reducing the risk of cardiovascular diseases associated with high blood pressure. Key Features and Functionality: - Advanced Machine Learning Models: Utilizes LSTM networks to capture temporal dependencies in patient data, enhancing prediction accuracy. - Comprehensive Data Integration: Combines various EHR components, including laboratory results, vital signs, demographics, diagnosis codes, medications, and procedures, to provide a holistic analysis. - Performance Metrics: Demonstrates high predictive performance with an Area Under the Receiver Operating Characteristic Curve (AUROC of up to 0.94, indicating strong discriminative ability. - Feature Importance Analysis: Employs SHapley Additive exPlanations (SHAP to interpret model predictions, highlighting key factors such as triglyceride levels and body mass index (BMI that contribute to hypertension risk. Primary Value and User Benefits: This solution addresses the critical need for early detection of hypertension by providing healthcare professionals with a predictive tool that analyzes patient data over time. By identifying individuals at risk before the onset of hypertension, it facilitates proactive management strategies, personalized treatment plans, and targeted lifestyle interventions. Ultimately, this approach aims to improve patient outcomes, reduce the incidence of hypertension-related complications, and optimize healthcare resource utilization.