100,000 €47.6 billion 291 160 + years 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: Vital signs, lab results, and trial data lived in separate siloes making doctors ignorant about cross-source trends in time “Positive” patient labels varied by unit and study indicated lack of clear rules causing noisy training targets Only ~4 % of records showed early signs of decline featuring standard models that over-fit to the safe majority Impact Delivered Early alert system flags high-risk patients ~180 days sooner Fewer emergencies and unplanned ER visits within six months Ensured end-to-end lineage and systematic audit logs 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 We standardized units, aligned IDs, and filled important gaps to give the model clean, consistent inputs. Sound statistical methods replaced blanks, keeping key signals intact while avoiding bias. We adjusted training samples so early-risk cases stayed visible, improving the model’s sensitivity. Rate-of-change, rolling average, and trend indicators captured subtle shifts in patient status. An iteratively tuned ensemble found patterns that generalize well to new patients. Clear factor-importance views help clinicians see why each alert fires. Cross-validation plus a reserved test set confirmed strength before rollout.
employees worldwide
in global sales
subsidiaries in 80 countries
of pharmaceutical & science leadership Business Case
Fragmented Inputs
Fuzzy Definitions
Class Imbalance
150+ vital-sign features
17% drop in pilot
sites8 million patient records cleaned and merged
Our Solution
Data preparation
Handling missing data
Balancing rare events
Feature engineering
Model development
Interpretability layer
Validation
Tech Stack
PostgreSQL
Python
AWS
