About: Joint Prediction and Time Estimation of COVID-19 Developing Severe Symptoms using Chest CT Scan

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Joint Prediction and Time Estimation of COVID-19 Developing Severe Symptoms using Chest CT Scan Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Joint Prediction and Time Estimation of COVID-19 Developing Severe Symptoms using Chest CT Scan
Creator	Li, Man Shi, Feng Song, Bin Shan, Fei Gao, Yaozong Shen, Dinggang Chen, Yanbo Gan, Jiangzhang Hu, Rongyao Wang, Liye Zhang, Wenhai Zhu, Xiaofeng
source	ArXiv
abstract	With the rapidly worldwide spread of Coronavirus disease (COVID-19), it is of great importance to conduct early diagnosis of COVID-19 and predict the time that patients might convert to the severe stage, for designing effective treatment plan and reducing the clinicians' workloads. In this study, we propose a joint classification and regression method to determine whether the patient would develop severe symptoms in the later time, and if yes, predict the possible conversion time that the patient would spend to convert to the severe stage. To do this, the proposed method takes into account 1) the weight for each sample to reduce the outliers' influence and explore the problem of imbalance classification, and 2) the weight for each feature via a sparsity regularization term to remove the redundant features of high-dimensional data and learn the shared information across the classification task and the regression task. To our knowledge, this study is the first work to predict the disease progression and the conversion time, which could help clinicians to deal with the potential severe cases in time or even save the patients' lives. Experimental analysis was conducted on a real data set from two hospitals with 422 chest computed tomography (CT) scans, where 52 cases were converted to severe on average 5.64 days and 34 cases were severe at admission. Results show that our method achieves the best classification (e.g., 85.91% of accuracy) and regression (e.g., 0.462 of the correlation coefficient) performance, compared to all comparison methods. Moreover, our proposed method yields 76.97% of accuracy for predicting the severe cases, 0.524 of the correlation coefficient, and 0.55 days difference for the converted time.
has issue date	2020-05-07(xsd:dateTime)
has license	arxiv
sha1sum (hex)	58b73c1e440d0a25a99bd36468b32ad937a71fdd
resource representing a document's title	Joint Prediction and Time Estimation of COVID-19 Developing Severe Symptoms using Chest CT Scan
resource representing a document's body	covid:58b73c1e440d0a25a99bd36468b32ad937a71fdd#body_text
is schema:about of	named entity 'hospitals' named entity 'admission' named entity 'severe' named entity 'correlation coefficient' named entity 'learn' named entity 'redundant' named entity 'joint' named entity 'COVID-19' named entity 'Prediction' named entity 'ANALYSIS' named entity 'TERM' named entity 'ACCOUNT' named entity 'WORK' named entity 'POSSIBLE' named entity 'DAYS' named entity 'DATA' named entity 'CONDUCT' named entity 'SAVE' named entity 'COVID-19' named entity 'STUDY' named entity 'LIVES' named entity 'SINA' named entity 'REGRESSION METHOD' named entity 'WHERE' named entity 'DATA SET' named entity 'PERFORMANCE' named entity 'CONVERSION' covid:arg/58b73c1e440d0a25a99bd36468b32ad937a71fdd named entity 'time' named entity 'influence' named entity 'explore' named entity 'problem' named entity 'disease' named entity 'feature' named entity 'predict' named entity 'weight' named entity 'days' named entity 'computed tomography' named entity 'scans' named entity 'data set' named entity 'Zhu' named entity 'chest CT scan' named entity 'SVM' named entity 'feature selection' named entity 'CT images' named entity 'binary classification' named entity 'lung' named entity 'CDC' named entity 'PCR tests' named entity 'cost-sensitive' named entity 'COVID' named entity 'Pontil' named entity 'COVID' named entity 'regression task' named entity 'optimization problem' named entity 'ground truth' named entity 'COVID' named entity 'feature selection' named entity 'symptom' named entity 'SFS' named entity 'convolutional neural network' named entity 'deep learning' named entity 'data set' named entity 'ridge regression' named entity 'infection' named entity 'α, β' named entity 'Hitachi' named entity 'Coronavirus disease' named entity 'feature selection' named entity 'trace operator' named entity 'ethics' named entity 'left lung' named entity 'feature selection' named entity 'Wuhan' named entity 'feature selection' named entity 'random forest'

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2025 OpenLink Software