The Impact of Extraneous Variables on the Performance of Recurrent Neural Network Models in Clinical Tasks

04/01/2019
by   Eugene Laksana, et al.
0

Electronic Medical Records (EMR) are a rich source of patient information, including measurements reflecting physiologic signs and administered therapies. Identifying which variables are useful in predicting clinical outcomes can be challenging. Advanced algorithms such as deep neural networks were designed to process high-dimensional inputs containing variables in their measured form, thus bypass separate feature selection or engineering steps. We investigated the effect of extraneous input variables on the predictive performance of Recurrent Neural Networks (RNN) by including in the input vector extraneous variables randomly drawn from theoretical and empirical distributions. RNN models using different input vectors (EMR variables; EMR and extraneous variables; extraneous variables only) were trained to predict three clinical outcomes: in-ICU mortality, 72-hour ICU re-admission, and 30-day ICU-free days. The measured degradations of the RNN's predictive performance with the addition of extraneous variables to EMR variables were negligible.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
07/28/2020

Improving Recurrent Neural Network Responsiveness to Acute Clinical Events

Predictive models in acute care settings must be able to immediately rec...
research
01/23/2017

Dynamic Mortality Risk Predictions in Pediatric Critical Care Using Recurrent Neural Networks

Viewing the trajectory of a patient as a dynamical system, a recurrent n...
research
05/23/2019

Interpreting a Recurrent Neural Network Model for ICU Mortality Using Learned Binary Masks

An attribution method was developed to interpret a recurrent neural netw...
research
12/03/2019

Explainable artificial intelligence model to predict acute critical illness from electronic health records

We developed an explainable artificial intelligence (AI) early warning s...
research
05/08/2023

Autoencoder-based prediction of ICU clinical codes

Availability of diagnostic codes in Electronic Health Records (EHRs) is ...
research
03/03/2019

Understanding Feature Selection and Feature Memorization in Recurrent Neural Networks

In this paper, we propose a test, called Flagged-1-Bit (F1B) test, to st...
research
11/14/2020

Discovery of the Hidden State in Ionic Models Using a Domain-Specific Recurrent Neural Network

Ionic models, the set of ordinary differential equations (ODEs) describi...

Please sign up or login with your details

Forgot password? Click here to reset