MODULE 1: PREDICTIVE DIAGNOSTICS
Predict individual breast cancer risk 8-10 years before clinical manifestation using multi-omics data, behavioral analysis, and explainable AI.
William Hayes
Clinical Data Scientist
Early prediction saves lives
Predictive diagnostics uses advanced AI to analyze multi-omics data, non-invasive biomarkers, and behavioral patterns to forecast cancer risk years before symptoms appear—creating critical time for prevention and intervention.
Our approach goes beyond traditional screening by identifying biological changes at their earliest stages, when lifestyle modifications and targeted interventions can still prevent disease development.
What makes it work
Module 1 combines cutting-edge technology with multiple data sources to create comprehensive risk profiles that traditional screening methods miss.
Multi-omics integration: genomics, microbiome, metabolomics
Non-invasive methods: infrared thermography (DITI), liquid biopsy
Behavioral and psycho-emotional factors
Explainable AI (XAI) and causal inference models
Technologies and Approaches
Our predictive platform analyzes patterns across multiple biological systems to identify precancerous states 8-10 years in advance. This includes hormonal profiles, epigenetic markers, proteomic panels (250+ proteins), and digital skin biomarkers.
The system integrates with EMR and HL7/FHIR standards, ensuring compatibility with existing clinical workflows. Every prediction includes XAI visualizations that clinicians can understand and trust, alongside confidence scores and time-frame estimates.
