Tracing the Heart: An Evidence-Linked Pipeline for Heart-Failure Feature Engineering
Automating the detective work that turns messy medical records into AI-ready data
Researchers built an AI system that automatically extracts and structures heart-failure data from fragmented medical records, a task that currently consumes nearly half of clinical data scientists' time. Testing on 500 patient records, the system created features that boosted predictive accuracy from 89.5% to 96.3% for one heart-failure type, while leaving an auditable trail showing exactly where each piece of data came from and why it was included.
Heart failure affects 6.7 million Americans, and developing better predictive AI requires months of tedious manual data work that slows research. If this approach scales, it could cut months off the time needed to build and validate heart-failure detection tools, freeing data scientists to focus on clinical strategy rather than data plumbing. The system's transparency—showing evidence and reasoning for every data choice—also matters for hospitals that need to defend AI decisions to regulators and doctors.