Business Case
The client held millions of trial, lab, and EHR records but no single view to run predictive risk analytics. The gap slowed their AI in healthcare push and let early warning signs slip past care teams. It faced certain issues like:
- Fragmented Inputs Vital signs, lab results, and trial data lived in separate siloes making doctors ignorant about cross-source trends in time
- Fuzzy Definitions “Positive” patient labels varied by unit and study indicated lack of clear rules causing noisy training targets
- Class Imbalance Only ~4 % of records showed early signs of decline featuring standard models that over-fit to the safe majority
Our Solution
We built a cloud data hub that pulls trial, lab, and EHR records into one stream, ready for agentic AI agents to scan in near real time. We also trained a risk model that fuels an early alert system, sending care teams clear flags up to six months before trouble hits
- Data preparation We standardized units, aligned IDs, and filled important gaps to give the model clean, consistent inputs.
- Balancing rare events We adjusted training samples so early-risk cases stayed visible, improving the model’s sensitivity.
- Model development An iteratively tuned ensemble found patterns that generalize well to new patients.
- Validation Cross-validation plus a reserved test set confirmed strength before rollout.
- Handling missing data Sound statistical methods replaced blanks, keeping key signals intact while avoiding bias.
- Feature engineering Rate-of-change, rolling average, and trend indicators captured subtle shifts in patient status.
- Interpretability layer Clear factor-importance views help clinicians see why each alert fires.