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Chengxi Zang

Showing results (21-30 of 34) with videos related to

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Nature Communications|April 7, 2023
Data-driven analysis to understand long COVID using electronic health records from the RECOVER initiativeChengxi Zang, Yongkang Zhang, Jie Xu, et al.
Patterns (New York, N.Y.)|November 1, 2021
Contrastive learning improves critical event prediction in COVID-19 patientsTingyi Wanyan, Hossein Honarvar, Suraj K Jaladanki, et al.
Environmental Advances|February 14, 2023
Identifying environmental risk factors for post-acute sequelae of SARS-CoV-2 infection: An EHR-based cohort study from the recover programYongkang Zhang, Hui Hu, Vasilios Fokaidis, et al.
Research Square|March 22, 2023
Risk Factors and Predictive Modeling for Post-Acute Sequelae of SARS-CoV-2 Infection: Findings from EHR Cohorts of the RECOVER InitiativeChengxi Zang, Yu Hou, Edward Schenck, et al.
Scientific Reports|May 19, 2023
Comparing the effects of four common drug classes on the progression of mild cognitive impairment to dementia using electronic health recordsJie Xu, Fei Wang, Chengxi Zang, et al.
Nature Medicine|December 1, 2022
Data-driven identification of post-acute SARS-CoV-2 infection subphenotypesHao Zhang, Chengxi Zang, Zhenxing Xu, et al.
Medrxiv : the Preprint Server for Health Sciences|June 6, 2022
Machine Learning for Identifying Data-Driven Subphenotypes of Incident Post-Acute SARS-CoV-2 Infection Conditions with Large Scale Electronic Health Records: Findings from the RECOVER InitiativeHao Zhang, Chengxi Zang, Zhenxing Xu, et al.
Communications Medicine|July 11, 2024
Identification of risk factors of Long COVID and predictive modeling in the RECOVER EHR cohortsChengxi Zang, Yu Hou, Edward J Schenck, et al.
Plos One|June 6, 2024
Excess burden of respiratory and abdominal conditions following COVID-19 infections during the ancestral and Delta variant periods in the United States: An EHR-based cohort study from the RECOVER programJay K Varma, Chengxi Zang, Thomas W Carton, et al.
Medrxiv : the Preprint Server for Health Sciences|March 3, 2023
Excess burden of respiratory and abdominal conditions following COVID-19 infections during the ancestral and Delta variant periods in the United States: An EHR-based cohort study from the RECOVER ProgramJay K Varma, Chengxi Zang, Thomas W Carton, et al.
Pageof 4

Showing results (21-30 of 34) with videos related to

Sort By:
Pageof 4
Nature Communications|April 7, 2023
Data-driven analysis to understand long COVID using electronic health records from the RECOVER initiativeChengxi Zang, Yongkang Zhang, Jie Xu, et al.
Patterns (New York, N.Y.)|November 1, 2021
Contrastive learning improves critical event prediction in COVID-19 patientsTingyi Wanyan, Hossein Honarvar, Suraj K Jaladanki, et al.
Environmental Advances|February 14, 2023
Identifying environmental risk factors for post-acute sequelae of SARS-CoV-2 infection: An EHR-based cohort study from the recover programYongkang Zhang, Hui Hu, Vasilios Fokaidis, et al.
Research Square|March 22, 2023
Risk Factors and Predictive Modeling for Post-Acute Sequelae of SARS-CoV-2 Infection: Findings from EHR Cohorts of the RECOVER InitiativeChengxi Zang, Yu Hou, Edward Schenck, et al.
Scientific Reports|May 19, 2023
Comparing the effects of four common drug classes on the progression of mild cognitive impairment to dementia using electronic health recordsJie Xu, Fei Wang, Chengxi Zang, et al.
Nature Medicine|December 1, 2022
Data-driven identification of post-acute SARS-CoV-2 infection subphenotypesHao Zhang, Chengxi Zang, Zhenxing Xu, et al.
Medrxiv : the Preprint Server for Health Sciences|June 6, 2022
Machine Learning for Identifying Data-Driven Subphenotypes of Incident Post-Acute SARS-CoV-2 Infection Conditions with Large Scale Electronic Health Records: Findings from the RECOVER InitiativeHao Zhang, Chengxi Zang, Zhenxing Xu, et al.
Communications Medicine|July 11, 2024
Identification of risk factors of Long COVID and predictive modeling in the RECOVER EHR cohortsChengxi Zang, Yu Hou, Edward J Schenck, et al.
Plos One|June 6, 2024
Excess burden of respiratory and abdominal conditions following COVID-19 infections during the ancestral and Delta variant periods in the United States: An EHR-based cohort study from the RECOVER programJay K Varma, Chengxi Zang, Thomas W Carton, et al.
Medrxiv : the Preprint Server for Health Sciences|March 3, 2023
Excess burden of respiratory and abdominal conditions following COVID-19 infections during the ancestral and Delta variant periods in the United States: An EHR-based cohort study from the RECOVER ProgramJay K Varma, Chengxi Zang, Thomas W Carton, et al.
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