no code implementations • 19 Sep 2021 • Moumita Bhattacharya, Dai-Yin Lu, Ioannis Ventoulis, Gabriela V. Greenland, Hulya Yalcin, Yufan Guan, Joseph E. Marine, Jeffrey E. Olgin, Stefan L. Zimmerman, Theodore P. Abraham, M. Roselle Abraham, Hagit Shatkay
Specifically, an ensemble of logistic regression and naive Bayes classifiers, trained based on the 18 variables and corrected for data imbalance, proved most effective for separating AF from No-AF cases (sensitivity = 0. 74, specificity = 0. 70, C-index = 0. 80).
no code implementations • 14 Jun 2021 • Robert Avram, Jeffrey E. Olgin, Alvin Wan, Zeeshan Ahmed, Louis Verreault-Julien, Sean Abreau, Derek Wan, Joseph E. Gonzalez, Derek Y. So, Krishan Soni, Geoffrey H. Tison
Our results demonstrate that multiple purpose-built neural networks can function in sequence to accomplish the complex series of tasks required for automated analysis of real-world angiograms.
no code implementations • 3 Dec 2018 • J. Weston Hughes, Taylor Sittler, Anthony D. Joseph, Jeffrey E. Olgin, Joseph E. Gonzalez, Geoffrey H. Tison
We develop a multi-task convolutional neural network (CNN) to classify multiple diagnoses from 12-lead electrocardiograms (ECGs) using a dataset comprised of over 40, 000 ECGs, with labels derived from cardiologist clinical interpretations.
no code implementations • 7 Feb 2018 • Brandon Ballinger, Johnson Hsieh, Avesh Singh, Nimit Sohoni, Jack Wang, Geoffrey H. Tison, Gregory M. Marcus, Jose M. Sanchez, Carol Maguire, Jeffrey E. Olgin, Mark J. Pletcher
We train and validate a semi-supervised, multi-task LSTM on 57, 675 person-weeks of data from off-the-shelf wearable heart rate sensors, showing high accuracy at detecting multiple medical conditions, including diabetes (0. 8451), high cholesterol (0. 7441), high blood pressure (0. 8086), and sleep apnea (0. 8298).