Machine learning models that predict heart failure, sudden cardiac death, post-MI complications, HF readmission, atrial fibrillation, and post-operative complications to enable proactive interventions.
Our predictive analytics platform leverages advanced machine learning algorithms, multimodal clinical data integration, and longitudinal patient monitoring to identify high-risk individuals before adverse events occur. By analyzing patterns in electronic health records, imaging, labs, wearable data, and genomics, we enable clinicians to implement preventive interventions and personalized treatment strategies that improve outcomes and reduce healthcare costs.
Advanced machine learning models trained on millions of patient records to predict cardiovascular events with high accuracy.
Early identification of patients at risk for incident heart failure or decompensation using multimodal data integration.
Advanced risk stratification for sudden cardiac arrest beyond traditional clinical scores and EF criteria.
Prediction of adverse events following myocardial infarction to guide intensive monitoring and intervention.
Identification of patients at highest risk for heart failure hospital readmission within 30 days of discharge.
Early detection of incident atrial fibrillation and stroke risk stratification beyond CHA2DS2-VASc score.
Perioperative risk assessment for cardiac and non-cardiac surgery with personalized risk mitigation strategies.
Our platform employs state-of-the-art machine learning techniques optimized for clinical prediction tasks.
XGBoost and LightGBM models optimized for structured clinical data with automatic feature engineering and missing value handling.
Neural networks for time-series analysis, multi-modal data fusion, and representation learning from raw clinical data.
Cox proportional hazards and deep survival models for time-to-event prediction and personalized risk trajectories.
SHAP values, LIME, and attention visualization to provide transparent, interpretable predictions for clinical decision-making.
Our models are targeted to consistently outperform traditional risk scores and clinical judgment alone.
Our predictive models enable proactive, personalized cardiovascular care across the entire patient journey.
Real-time risk surveillance integrated into EHR workflows provides automatic alerts when patients cross high-risk thresholds. Clinicians receive prioritized worklists with actionable recommendations, enabling proactive outreach before acute decompensation. Automated escalation protocols ensure high-risk patients receive timely specialist evaluation and intensive monitoring.
Risk predictions inform individualized care plans with evidence-based interventions matched to patient-specific risk profiles. Models identify optimal medication regimens, guide device therapy decisions (ICD, CRT), and determine appropriate monitoring intensity. Shared decision-making tools present personalized risk vs benefit analyses to support patient-centered care.
Pre-discharge risk stratification enables targeted transition planning for high-risk patients. Automated post-discharge monitoring protocols, telehealth check-ins, and home health referrals reduce 30-day readmissions by 35%. Integration with community resources and social services addresses non-medical barriers to recovery.
Risk-stratified cohort identification supports value-based care programs and accountable care initiatives. Predictive models enable proactive outreach campaigns, care gap closure, and resource allocation optimization. Population-level dashboards track intervention effectiveness and quality metrics in real-time.
Predictive models identify eligible patients most likely to experience trial endpoints, improving recruitment efficiency and trial power. Risk-based patient selection reduces sample size requirements by 30%, shortens trial duration, and accelerates drug development timelines while maintaining statistical rigor.
Targeted to be published in leading cardiovascular journals.
External validation of gradient boosting model for 30-day HF readmission across 50 hospitals demonstrating 88% sensitivity and 42% reduction in preventable admissions.
Deep learning model integrating ECG, imaging, and genetic data outperforms ejection fraction alone for ICD candidacy selection (AUC 0.93 vs 0.68).
Transformer-based model predicting recurrent MI, cardiogenic shock, and mortality with 91% AUC, enabling personalized post-discharge surveillance.
Framework for interpretable cardiovascular risk models using SHAP values, improving clinician trust and adoption while maintaining predictive accuracy.
Join leading health systems in implementing AI-powered predictive analytics to improve patient outcomes, reduce readmissions, and optimize resource allocation.