Work
Each project has a write-up covering the problem, the data, the decisions I made and why, and what I would do differently. Status reflects what is built, not what is planned.
A machine learning system that reads an ECG and flags what warrants a clinician's attention. Version one detects atrial fibrillation from rhythm irregularity, built end to end from raw PhysioNet waveforms to a deployed application. The roadmap extends to ischemic ST changes, conduction abnormalities, and ECGs captured as photographs rather than digital signals.
Read the write-upHospitals must now publish their negotiated rates in a standardized format, but a rate alone cannot answer a revenue question — a high price on a procedure nobody orders is worth nothing. This project joins those rates to CMS utilization data to estimate what cardiac diagnostics actually earn across Washington State, and where hospitals negotiated below what regional peers secured.
Read the write-upThe ECG toolkit is at v1 and reads rhythm only. The next version adds image digitization, so a photograph of a paper ECG can be interpreted, along with morphology features that extend detection beyond rhythm to ischemia and conduction disease. Both are described in the write-up.