About: Accurately Differentiating COVID-19, Other Viral Infection, and Healthy Individuals Using Multimodal Features via Late Fusion Learning

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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
has title	Accurately Differentiating COVID-19, Other Viral Infection, and Healthy Individuals Using Multimodal Features via Late Fusion Learning
Creator	Liu, Jie Liu, Yang Zhu, Baoli Gao, Yan Jiang, Chao Li, Qian Sun, Kai Liu, Xiaowen Xu, Ming Chen, Shi Ouyang, Liu Gao, Yue Wen, Jing Chen, Yuanfang Ge, Yaorong Han, Lei Sheng Bao, Forrest Robinson, Patrick Yu, Tingting Chen, Tianyang Li, Hebi Safarnejad, Lida Yan, Tianyu Yu, Zhengmin Zhang, Hengdong Zhang, Zengli
Source	MedRxiv
abstract	Effectively identifying COVID-19 patients using non-PCR clinical data is critical for the optimal clinical outcomes. Currently, there is a lack of comprehensive understanding of various biomedical features and appropriate technical approaches to accurately detecting COVID-19 patients. In this study, we recruited 214 confirmed COVID-19 patients in non-severe (NS) and 148 in severe (S) clinical type, 198 non-infected healthy (H) participants and 129 non-COVID viral pneumonia (V) patients. The participants' clinical information (23 features), lab testing results (10 features), and thoracic CT scans upon admission were acquired as three input feature modalities. To enable late fusion of multimodality data, we developed a deep learning model to extract a 10-feature high-level representation of the CT scans. Exploratory analyses showed substantial differences of all features among the four classes. Three machine learning models (k-nearest neighbor kNN, random forest RF, and support vector machine SVM) were developed based on the 43 features combined from all three modalities to differentiate four classes (NS, S, V, and H) at once. All three models had high accuracy to differentiate the overall four classes (95.4%-97.7%) and each individual class (90.6%-99.9%). Multimodal features provided substantial performance gain from using any single feature modality. Compared to existing binary classification benchmarks often focusing on single feature modality, this study provided a novel and effective breakthrough for clinical applications. Findings and the analytical workflow can be used as clinical decision support for current COVID-19 and other clinical applications with high-dimensional multimodal biomedical features.
has issue date	2020-08-21(xsd:dateTime)
bibo:doi	10.1101/2020.08.18.20176776
has license	medrxiv
sha1sum (hex)	ae05274f4d697a91934241401db6606783cc75f8
schema:url	https://doi.org/10.1101/2020.08.18.20176776
resource representing a document's title	Accurately Differentiating COVID-19, Other Viral Infection, and Healthy Individuals Using Multimodal Features via Late Fusion Learning
resource representing a document's body	covid:ae05274f4d697a91934241401db6606783cc75f8#body_text
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