Neural networks for complex health data: high-dimensional analysis, multimodal inputs, and pattern recognition.
Advanced predictive models for problems where standard approaches are not enough. We use deep learning when the data and the use case justify the added complexity.
Deep learning must match the data, compute, and governance reality on the ground. We design architectures that work within those constraints.
Frame the problem and confirm the data is sufficient
Design architectures that match the modality and delivery need
Train, validate, and benchmark against strong simpler baselines
Translate outputs into usable research or product workflows
Risk Analytics
Predict Type II diabetes risk from clinical biomarkers. Enter patient data and get a risk score with clear review bands.
Risk Analytics
Estimate breast cancer recurrence risk from clinical and treatment data. Built for registry analysis and research teams.
Risk Analytics
Predict osteoporosis risk from demographic and clinical data. Designed for prevention planning and research teams.