Live Healthcare Prediction Models

Clinical Decision Support Model

A proof-of-concept Clinical Decision Support (CDS) system that estimates individualized treatment benefit using causal machine learning. This demonstration illustrates how a T-learner can predict patient-specific treatment effects to support clinical decision making using synthetic patient data. Read more

Breast Cancer Diagnostic Model

A proof-of-concept neural network trained to predict breast cancer outcomes using key clinical features. This example shows how machine learning can support early detection and assist clinical decision making by learning patterns from labeled diagnostic data. Read more

Healthcare ML (HML) Models

Healthcare data is complex, high dimensional, and often non-linear. Neural networks provide a powerful way to learn patterns across clinical features and support predictive models that assist diagnosis, risk assessment, and treatment planning.

When deployed responsibly, machine learning systems can augment clinical expertise, improve consistency, and help surface insights that may be difficult to identify through manual analysis alone.

These demonstrations are designed to show how predictive models can be delivered securely and reliably through a browser, making advanced analytics accessible without disrupting existing clinical workflows.

Machine learning has the potential to improve healthcare outcomes by enabling earlier insight, better risk stratification, and more informed clinical decisions.

Note: This model is built using open-source data for demonstration purposes only. Production healthcare deployments require secure infrastructure, regulatory compliance, and careful validation using anonymized patient data.