Due to necessary scheduled maintenance, the JMIR Publications website will be unavailable from Wednesday, July 01, 2020 at 8:00 PM to 10:00 PM EST. We apologize in advance for any inconvenience this may cause you.
Who will be affected?
Readers: No access to all 28 journals. We recommend accessing our articles via PubMed Central
Authors: No access to the submission form or your user account.
Reviewers: No access to your user account. Please download manuscripts you are reviewing for offline reading before Wednesday, July 01, 2020 at 7:00 PM.
Editors: No access to your user account to assign reviewers or make decisions.
Copyeditors: No access to user account. Please download manuscripts you are copyediting before Wednesday, July 01, 2020 at 7:00 PM.
Nguyen KAN, Tandon P, Ghanavati S, Cheetirala S, Timsina P, Freeman R, Reich DL, Levin MA, Mazumdar M, Fayad ZA, Kia A
A Hybrid Decision Tree and Deep Learning Approach Combining Medical Imaging and Electronic Medical Records to Predict Intubation Among Hospitalized Patients With COVID-19: Algorithm Development and Validation
A Hybrid Decision Tree/Deep Learning Approach Combining Medical Imaging and Electronic Medical Records to Predict Intubation among Hospitalized Patients with COVID-19: A Retrospective Study
Kim-Anh-Nhi Nguyen;
Pranai Tandon;
Sahar Ghanavati;
Satya Cheetirala;
Prem Timsina;
Robert Freeman;
David L Reich;
Matthew A Levin;
Madhu Mazumdar;
Zahi A Fayad;
Arash Kia
ABSTRACT
Background:
Chest radiographs (CXRs) and electronic medical records (EMR), typically obtained early in patients admitted with COVID-19, are key to deciding whether they need mechanical ventilation.
Objective:
The objective of this study is to evaluate the use of a machine learning model to predict the need for intubation using a combination of CXR and EMR data. We included historical data from 2481 hospitalizations at The Mount Sinai Hospital in New York City.
Methods:
We first developed an image classifier by retraining a pre-trained DenseNet model (transfer learning). Then, in the final fusion model, we used a Random Forest (RF) algorithm taking 41 input variables coming from EMR and the probability score from the image classifier.
Results:
At a prediction probability threshold of 0.5, the fusion model provided 82.2% (95% CI 68%-94%) sensitivity, 82.9% (95% CI 79%-87%) specificity, 82.6% (95% CI 78%- 87%) accuracy, and 0.87 (95% CI 0.80-0.94) area under the receiver operating characteristics curve (AUROC) on the test set. Compared to the image classifier alone, which had an AUROC of 0.62 (95% CI 0.50-0.74), the fusion model showed significant improvement.
Conclusions:
We show that we can predict the need for invasive mechanical ventilation in patients with COVID-19 in a robust and automated manner by combining routinely available chest radiography images along with longitudinal clinical data. Such a model may assist risk assessment and optimize clinical decision making in choosing the best care plan during the critical stages of COVID-19.: COVID-19; medical imaging; machine learning; chest x-ray; mechanical ventilation; electronic health records; intubation; decision trees; hybrid model; clinical informatics
Citation
Please cite as:
Nguyen KAN, Tandon P, Ghanavati S, Cheetirala S, Timsina P, Freeman R, Reich DL, Levin MA, Mazumdar M, Fayad ZA, Kia A
A Hybrid Decision Tree and Deep Learning Approach Combining Medical Imaging and Electronic Medical Records to Predict Intubation Among Hospitalized Patients With COVID-19: Algorithm Development and Validation