Deep neural networks can predict mortality from 12-lead electrocardiogram voltage data

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.

Mortality Prediction

Interpretable Neural Networks for Predicting Mortality Risk using Multi-modal Electronic Health Records

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.

A deep neural network to enhance prediction of 1-year mortality using echocardiographic videos of the heart

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).

Mortality Prediction