Predictive Analytics of COVID-19 with Neural Networks

被引:0
作者
Fung, Daryl L. X. [1 ]
Hoi, Calvin S. H. [1 ]
Leung, Carson K. [1 ]
Zhang, Christine Y. [2 ]
机构
[1] Univ Manitoba, Dept Comp Sci, Winnipeg, MB, Canada
[2] Univ Manitoba, Max Rady Coll Med, Winnipeg, MB, Canada
来源
2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2021年
基金
加拿大自然科学与工程研究理事会;
关键词
autoencoder; COVID-19; data science; few-shot learning; healthcare data; machine learning; neural network; prediction; EDITORIAL SPECIAL-ISSUE; BIG DATA; DIAGNOSIS;
D O I
10.1109/IJCNN52387.2021.9534188
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Neural networks (NNs) have been applied in numerous real-life applications and services. These include the applications in disease and healthcare analytics, such as identification and predictive analytics of coronavirus disease 2019 (COVID-19). However, many existing NN-based solutions train the models based on data (e.g., computed tomography (CT) scan images, viral/molecular test results) that can be expensive to produce and/or not easily accessible. They also require large volumes of these data for training. However, partially due to privacy concerns and other factors, the volume of available COVID-19 data can be limited. Hence, in this paper, we present a solution for predictive analytics of COVID-19 with NNs. Our solution consists of three algorithms, which make good use of autoencoder and few-shot learning, to train the prediction model with only a few samples of more accessible and less expensive types of data (e.g., serology/antibody test results from blood samples). Evaluation results on a real-life Brazilian COVID-19 dataset demonstrate the effectiveness of our solution in predictive analytics of COVD-19 with NNs.
引用
收藏
页数:8
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