%0 Journal Article
%T A Comparison of Patient History- and EKG-based Cardiac Risk Scores
%J Archive of "AMIA Summits on Translational Science Proceedings".
%D 2019
%X Patient-specific risk scores are used to identify individuals at elevated risk for cardiovascular disease. Typically, risk scores are based on patient habits and medical history ¡ª age, sex, race, smoking behavior, and prior vital signs and diagnoses. We explore an alternative source of information, a patient¡¯s raw electrocardiogram recording, and develop a score of patient risk for various outcomes. We compare models that predict adverse cardiac outcomes following an emergency department visit, and show that a learned representation (e.g. deep neural network) of raw EKG waveforms can improve prediction over traditional risk factors. Further, we show that a simple model based on segmented heart beats performs as well or better than a complex convolutional network recently shown to reliably automate arrhythmia detection in EKGs. We analyze a large cohort of emergency department patients and show evidence that EKG-derived scores can be more robust to patient heterogeneity
%U https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6568098/