518 citations · 869 across the 11 of their papers we have counts for
11 papers
Predicting High-Flow Nasal Cannula Failure in an ICU Using a Recurrent Neural Network with Transfer Learning and Input Data Perseveration: A Retrospective Analysis
George A. Pappy, Melissa D. Aczon, Randall C. Wetzel +1
High Flow Nasal Cannula (HFNC) provides non-invasive respiratory support for critically ill children who may tolerate it more readily than other Non-Invasive (NIV) techniques. Time…
Improving Recurrent Neural Network Responsiveness to Acute Clinical Events
David Ledbetter, Eugene Laksana, Melissa Aczon +1
Predictive models in acute care settings must be able to immediately recognize precipitous changes in a patient's status when presented with data reflecting such changes. Recurrent…
Interpreting a Recurrent Neural Network's Predictions of ICU Mortality Risk
Long V. Ho, Melissa D. Aczon, David Ledbetter +1
Deep learning has demonstrated success in many applications; however, their use in healthcare has been limited due to the lack of transparency into how they generate predictions. A…
The Impact of Extraneous Variables on the Performance of Recurrent Neural Network Models in Clinical Tasks
Eugene Laksana, Melissa Aczon, Long Ho +3
Electronic Medical Records (EMR) are a rich source of patient information, including measurements reflecting physiologic signs and administered therapies. Identifying which variabl…
Predicting Individual Responses to Vasoactive Medications in Children with Septic Shock
Nicole Fronda, Jessica Asencio, Cameron Carlin +4
Objective: Predict individual septic children's personalized physiologic responses to vasoactive titrations by training a Recurrent Neural Network (RNN) using EMR data. Materials a…
Predicting Individual Physiologically Acceptable States for Discharge from a Pediatric Intensive Care Unit
Cameron Carlin, Long Van Ho, David Ledbetter +2
Objective: Predict patient-specific vitals deemed medically acceptable for discharge from a pediatric intensive care unit (ICU). Design: The means of each patient's hr, sbp and dbp…
The Dependence of Machine Learning on Electronic Medical Record Quality
Long Ho, David Ledbetter, Melissa Aczon +1
There is growing interest in applying machine learning methods to Electronic Medical Records (EMR). Across different institutions, however, EMR quality can vary widely. This work i…
Dynamic Mortality Risk Predictions in Pediatric Critical Care Using Recurrent Neural Networks
M Aczon, D Ledbetter, L Ho +4
Viewing the trajectory of a patient as a dynamical system, a recurrent neural network was developed to learn the course of patient encounters in the Pediatric Intensive Care Unit (…
Modeling Missing Data in Clinical Time Series with RNNs
Zachary C. Lipton, David C. Kale, Randall Wetzel
We demonstrate a simple strategy to cope with missing data in sequential inputs, addressing the task of multilabel classification of diagnoses given clinical time series. Collected…
Learning to Diagnose with LSTM Recurrent Neural Networks
Zachary C. Lipton, David C. Kale, Charles Elkan +1
Clinical medical data, especially in the intensive care unit (ICU), consist of multivariate time series of observations. For each patient visit (or episode), sensor data and lab te…
Phenotyping of Clinical Time Series with LSTM Recurrent Neural Networks
Zachary C. Lipton, David C. Kale, Randall C. Wetzel
We present a novel application of LSTM recurrent neural networks to multilabel classification of diagnoses given variable-length time series of clinical measurements. Our method ou…