I-Corps: Machine Learning Algorithm for Cardiovascular Disease Diagnosis

The broader impact/commercial potential of this I-Corps project is the development of a cardiovascular screening/diagnostic software tool that empowers clinicians to deliver higher quality care to all patient populations, including the historically underserved and those across the spectrum of cardiovascular disease risk. Cardiovascular disease misdiagnosis is as much as 50% higher in women than men The proposed software is designed to improve diagnostic accuracy for clinicians and provide better cardiovascular health outcomes for patients. Cardiovascular disease is the leading cause of death globally, and this software has the potential to reduce cardiovascular healthcare costs by reducing spending on unnecessary testing, procedures, and illness that occurs when patients are not screened early and diagnosed quickly and accurately. This technology focuses on those cardiovascular conditions that are misdiagnosed frequently and aims to improve health equity given the increased risk of misdiagnosis for women and the contribution of these conditions to the ongoing burden of cardiovascular health inequities that affect patients from minoritized genders and ethnicities. This I-Corps project is based on the development of a machine learning-based algorithm to assist clinicians in identifying which patients are at high risk of developing or worsening cardiovascular diseases. The proposed technology uses information in patients’ electronic health records, and the algorithm focuses on cardiovascular diseases that are not well understood, are often missed, and/or disproportionately affect women. The machine learning (ML) algorithm was trained and tested on the digital health records of a highly diverse group of patients and may more accurately provide cardiovascular disease (CVD) diagnoses for women and ethnic minorities than the current standard of care. Most ML tools for diagnosing CVD use deep learning to automate the interpretation of images and to interpret electrocardiogram (ECG) signals with similar or superior accuracy to specialist physicians. The training model used for this technology is designed to catch missed cases of CVD and is based on information that is commonly in patients’ electronic health records. This is because in many cases of misdiagnosis, CVD is not suspected by the clinician and CVD-specific tests/scans are not ordered or signals appear normal. The technology leverages physiological differences in patients and is developed with the aim of improving the accuracy of triaging patients and being able to identify patients with CVD earlier than with the existing rule-based systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.