DeepPredict: A deep predictive intelligence platform for patient monitoring

A novel platform, DeepPredict, for predicting hospital bed exit events from video camera systems is proposed. DeepPredict processes video data with a deep convolutional neural network consisting of five main layers: a 1 × 1 3D convolutional layer used for generating feature maps from raw video data,...

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Bibliographic Details
Main Authors: Chwyl, Brendan, Chung, Audrey G., Shafiee, Mohammad Javad, Yongji Fu, Wong, Alexander
Format: Conference Proceeding
Language:English
Subjects:
Cameras
Feature extraction
Hospitals
Sensitivity
Three-dimensional displays
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Summary:A novel platform, DeepPredict, for predicting hospital bed exit events from video camera systems is proposed. DeepPredict processes video data with a deep convolutional neural network consisting of five main layers: a 1 × 1 3D convolutional layer used for generating feature maps from raw video data, a context-aware pooling layer used for rectifying data from different camera angles, two fully connected layers used for applying pre-trained deep features, and an output layer used to provide a likelihood of a bed exit event. Results for a model trained on 180 hours of data demonstrate accuracy, sensitivity, and specificity of 86.47%, 78.87%, and 94.07%, respectively, when predicting a bed exit event up to seven seconds in advance.
ISSN:1557-170X
2694-0604
DOI:10.1109/EMBC.2017.8037809