no code implementations • 11 Dec 2023 • Will E. Thompson, David M. Vidmar, Jessica K. De Freitas, John M. Pfeifer, Brandon K. Fornwalt, Ruijun Chen, Gabriel Altay, Kabir Manghnani, Andrew C. Nelsen, Kellie Morland, Martin C. Stumpe, Riccardo Miotto
Identifying disease phenotypes from electronic health records (EHRs) is critical for numerous secondary uses.
no code implementations • 15 Apr 2019 • Sushravya Raghunath, Alvaro E. Ulloa Cerna, Linyuan Jing, David P. vanMaanen, Joshua Stough, Dustin N. Hartzel, Joseph B. Leader, H. Lester Kirchner, Christopher W. Good, Aalpen A. Patel, Brian P. Delisle, Amro Alsaid, Dominik Beer, Christopher M. Haggerty, Brandon K. Fornwalt
The electrocardiogram (ECG) is a widely-used medical test, typically consisting of 12 voltage versus time traces collected from surface recordings over the heart.
no code implementations • 23 Jan 2019 • Alvaro E. Ulloa Cerna, Marios Pattichis, David P. vanMaanen, Linyuan Jing, Aalpen A. Patel, Joshua V. Stough, Christopher M. Haggerty, Brandon K. Fornwalt
We present an interpretable neural network for predicting an important clinical outcome (1-year mortality) from multi-modal Electronic Health Record (EHR) data.
no code implementations • 26 Nov 2018 • Alvaro Ulloa, Linyuan Jing, Christopher W. Good, David P. vanMaanen, Sushravya Raghunath, Jonathan D Suever, Christopher D Nevius, Gregory J Wehner, Dustin Hartzel, Joseph B. Leader, Amro Alsaid, Aalpen A. Patel, H. Lester Kirchner, Marios S. Pattichis, Christopher M. Haggerty, Brandon K. Fornwalt
We show that a large dataset of 723, 754 clinically-acquired echocardiographic videos (~45 million images) linked to longitudinal follow-up data in 27, 028 patients can be used to train a deep neural network to predict 1-year mortality with good accuracy (area under the curve (AUC) in an independent test set = 0. 839).