Supplement 3 (2021-2024) A Machine Learning Platform and Database Linking Digitized Electrocardiogram Waveforms with Hospital Electronic Health Records

This project creates a deidentified and highly curated dataset of digitized electrocardiogram (ECG) waveforms linked to other electronic health records (EHR) from a major research hospital. These records include elaborate clinical information on diagnoses, procedures, treatments, medications, vital signs, lab tests, physician notes, and outcomes over a 5-year longitudinal timeframe. The integrated ECG-EHR dataset will be housed in a secure, monitored cloud platform, and available for non-commercial research applications. An established legal framework is in place for sharing the dataset with a broad coalition of researchers, and for using the data in cutting-edge research applications.

The project supplements a large multi-component research grant on Improving Health Outcomes for an Aging Population (P01-AG05842), and more specifically, project 4 of the parent grant, on Assessing the Overuse and Underuse of Diagnostic Testing. The use of machine learning algorithms to improve health care decision making is a major over-arching theme of the overall parent grant, and project 4 draws on advances in machine learning technology to gauge the extent of over- and under-use of diagnostic testing. The incremental database development proposed here builds directly from the work being conducted in project 4.

Machine learning algorithms have the potential to identify patterns from historical patient records, and to use those patterns to improve the real-time decisions that physician need to make when diagnosing and treating patients in clinical settings. The proposed database will be used initially to study the diagnostic and treatment decisions made for patients presenting with acute coronary symptoms in hospital emergency rooms. The data will be used, specifically, to predict adverse events like positive troponin, and positive catheterization results from the digitized ECG waveforms. There is no existing equivalent to the platform and dataset we propose to assemble and make available to the scientific community.