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Accurately Differentiating Between Patients With COVID-19, Patients With Other Viral Infections, and Healthy Individuals: Multimodal Late Fusion Learning Approach.
Xu, Ming; Ouyang, Liu; Han, Lei; Sun, Kai; Yu, Tingting; Li, Qian; Tian, Hua; Safarnejad, Lida; Zhang, Hengdong; Gao, Yue; Bao, Forrest Sheng; Chen, Yuanfang; Robinson, Patrick; Ge, Yaorong; Zhu, Baoli; Liu, Jie; Chen, Shi.
  • Xu M; Department of Occupational Disease Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.
  • Ouyang L; Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
  • Han L; Department of Public Health Sciences, College of Health and Human Services, University of North Carolina Charlotte, Charlotte, NC, United States.
  • Sun K; Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Yu T; Department of Occupational Disease Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.
  • Li Q; Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
  • Tian H; Department of Emergency Medicine, The First Hospital of Nanjing Medical University, Nanjing, China.
  • Safarnejad L; Department of Medical Genetics, School of Basic Medical Science Jiangsu Key Laboratory of Xenotransplantation, Nanjing Medical University, Nanjing, China.
  • Zhang H; Department of Pediatrics, Affiliated Kunshan Hospital of Jiangsu University, Kunshan, China.
  • Gao Y; Department of Acute Infectious Disease Control and Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.
  • Bao FS; School of Medicine, Stanford University, Stanford, CA, United States.
  • Chen Y; Department of Software and Information Systems, College of Computing and Informatics, University of North Carolina Charlotte, Charlotte, NC, United States.
  • Robinson P; Department of Occupational Disease Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.
  • Ge Y; Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
  • Zhu B; Department of Occupational Disease Prevention, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.
  • Liu J; Department of Computer Science, Iowa State University, Ames, IA, United States.
  • Chen S; Institute of HIV/AIDS/STI Prevention and Control, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing, China.
J Med Internet Res ; 23(1): e25535, 2021 01 06.
Artículo en Inglés | MEDLINE | ID: covidwho-1011363
ABSTRACT

BACKGROUND:

Effectively identifying patients with COVID-19 using nonpolymerase chain reaction biomedical data is critical for achieving optimal clinical outcomes. Currently, there is a lack of comprehensive understanding in various biomedical features and appropriate analytical approaches for enabling the early detection and effective diagnosis of patients with COVID-19.

OBJECTIVE:

We aimed to combine low-dimensional clinical and lab testing data, as well as high-dimensional computed tomography (CT) imaging data, to accurately differentiate between healthy individuals, patients with COVID-19, and patients with non-COVID viral pneumonia, especially at the early stage of infection.

METHODS:

In this study, we recruited 214 patients with nonsevere COVID-19, 148 patients with severe COVID-19, 198 noninfected healthy participants, and 129 patients with non-COVID viral pneumonia. The participants' clinical information (ie, 23 features), lab testing results (ie, 10 features), and CT scans upon admission were acquired and used as 3 input feature modalities. To enable the late fusion of multimodal features, we constructed a deep learning model to extract a 10-feature high-level representation of CT scans. We then developed 3 machine learning models (ie, k-nearest neighbor, random forest, and support vector machine models) based on the combined 43 features from all 3 modalities to differentiate between the following 4 classes nonsevere, severe, healthy, and viral pneumonia.

RESULTS:

Multimodal features provided substantial performance gain from the use of any single feature modality. All 3 machine learning models had high overall prediction accuracy (95.4%-97.7%) and high class-specific prediction accuracy (90.6%-99.9%).

CONCLUSIONS:

Compared to the existing binary classification benchmarks that are often focused on single-feature modality, this study's hybrid deep learning-machine learning framework provided a novel and effective breakthrough for clinical applications. Our findings, which come from a relatively large sample size, and analytical workflow will supplement and assist with clinical decision support for current COVID-19 diagnostic methods and other clinical applications with high-dimensional multimodal biomedical features.
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Texto completo: Disponible Colección: Bases de datos internacionales Base de datos: MEDLINE Asunto principal: Neumonía Viral / Salud / Sistemas de Apoyo a Decisiones Clínicas / Aprendizaje Automático / COVID-19 Tipo de estudio: Estudios diagnósticos / Estudio pronóstico / Ensayo controlado aleatorizado Tópicos: Covid persistente Límite: Humanos / Middle aged Idioma: Inglés Revista: J Med Internet Res Asunto de la revista: Informática Médica Año: 2021 Tipo del documento: Artículo País de afiliación: 25535

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Texto completo: Disponible Colección: Bases de datos internacionales Base de datos: MEDLINE Asunto principal: Neumonía Viral / Salud / Sistemas de Apoyo a Decisiones Clínicas / Aprendizaje Automático / COVID-19 Tipo de estudio: Estudios diagnósticos / Estudio pronóstico / Ensayo controlado aleatorizado Tópicos: Covid persistente Límite: Humanos / Middle aged Idioma: Inglés Revista: J Med Internet Res Asunto de la revista: Informática Médica Año: 2021 Tipo del documento: Artículo País de afiliación: 25535