L
Liu Ouyang
Researcher at Huazhong University of Science and Technology
Publications - 10
Citations - 168
Liu Ouyang is an academic researcher from Huazhong University of Science and Technology. The author has contributed to research in topics: Retrospective cohort study & Feature (computer vision). The author has an hindex of 4, co-authored 10 publications receiving 93 citations.
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Epidemiological, Clinical Characteristics and Outcome of Medical Staff Infected with COVID-19 in Wuhan, China: A Retrospective Case Series Analysis
Jie Liu,Liu Ouyang,Pi Guo,Hai sheng Wu,Peng Fu,Yu liang Chen,Dan Yang,Xiao yu Han,Yu kun Cao,Osamah Alwalid,Juan Tao,Shu yi Peng,He shui Shi,Fan Yang,Chuan sheng Zheng +14 more
TL;DR: In this study, medical staff infected with COVID-19 have relatively milder symptoms and favorable clinical course, which may be partly due to their medical expertise, younger age and less underlying diseases.
Journal ArticleDOI
Accurately Differentiating Between Patients With COVID-19, Patients With Other Viral Infections, and Healthy Individuals: Multimodal Late Fusion Learning Approach.
Ming Xu,Liu Ouyang,Lei Han,Kai Sun,Tingting Yu,Qian Li,Hua Tian,Lida Safarnejad,Hengdong Zhang,Yue Gao,Forrest Sheng Bao,Yuanfang Chen,Patrick Robinson,Yaorong Ge,Baoli Zhu,Jie Liu,Shi Chen +16 more
TL;DR: Wang et al. as discussed by the authors proposed a hybrid deep learning-machine learning framework to detect patients with COVID-19 using low-dimensional clinical and lab testing data, as well as high-dimensional computed tomography imaging data.
Journal ArticleDOI
An Interpretable Machine Learning Framework for Accurate Severe vs Non-severe COVID-19 Clinical Type Classification
Yuanfang Chen,Liu Ouyang,Sheng Bao,Qian Li,Lei Han,Hengdong Zhang,Baoli Zhu,Ming Xu,Jie Liu,Yaorong Ge,Shi Chen +10 more
TL;DR: It is suggested that symptoms and comorbidities can be used as an initial screening tool for triaging, while biochemistry and features combined are applied when accuracy is the priority.
Journal ArticleDOI
A Multimodality Machine Learning Approach to Differentiate Severe and Nonsevere COVID-19: Model Development and Validation.
Yuanfang Chen,Liu Ouyang,Forrest Sheng Bao,Qian Li,Lei Han,Lei Han,Hengdong Zhang,Hengdong Zhang,Baoli Zhu,Baoli Zhu,Baoli Zhu,Yaorong Ge,Patrick Robinson,Ming Xu,Ming Xu,Ming Xu,Jie Liu,Shi Chen +17 more
TL;DR: In this paper, a machine learning approach was used to understand COVID-19 more comprehensively, accurately differentiate severe and nonsevere clinical types based on multiple medical features, and provide reliable predictions of the clinical type of the disease.
Posted ContentDOI
Accurately Differentiating COVID-19, Other Viral Infection, and Healthy Individuals Using Multimodal Features via Late Fusion Learning
Xu Ming,Xu Ming,Liu Ouyang,Yan Gao,Yuanfang Chen,Yuanfang Chen,Tingting Yu,Qian Li,Kai Sun,Forrest Sheng Bao,Lida Safarnejad,Jing Wen,Chao Jiang,Tianyang Chen,Han Lei,Zhang Hengdong,Yue Gao,Yu Zhengmin,Liu Xiaowen,Tianyu Yan,Hebi Li,Patrick Robinson,Baoli Zhu,Jie Liu,Yang Liu,Zengli Zhang,Yaorong Ge,Shi Chen +27 more
TL;DR: This study recruited 214 confirmed COVID-19 patients, developed a deep learning model to extract a 10-feature high-level representation of the CT scans, and developed three machine learning models based on the 43 features combined from all three modalities to differentiate four classes at once.