Hierarchical Neyman-Pearson Classification for Prioritizing Severe Disease Categories in COVID-19 Patient Data-Reference-Cited by-同舟云学术

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Hierarchical Neyman-Pearson Classification for Prioritizing Severe Disease Categories in COVID-19 Patient Data

Published:2023-10-17 Issue: Volume: Page:1-13
ISSN:0162-1459
Container-title:Journal of the American Statistical Association
language:en
Short-container-title:Journal of the American Statistical Association

Author:

Wang Lijia¹,Wang Y. X. Rachel²,Li Jingyi Jessica³^ORCID,Tong Xin⁴^ORCID

Affiliation:

1. School of Data Science, City University of Hong Kong, Kowloon, Hong Kong

2. School of Mathematics and Statistics, University of Sydney, Camperdown, Australia

3. Department of Statistics, University of California, Los Angeles, Los Angeles, CA

4. Department of Data Sciences and Operations, University of Southern California, Los Angeles, CA

Funder

NSF

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/01621459.2023.2270657

Reference52 articles.

1. “Sanity Checks for Saliency Maps,” in;Adebayo J.;NeurIPS,2018

2. Analyse multiple disease subtypes and build associated gene networks using genome-wide expression profiles

3. Machine learning approaches in COVID-19 diagnosis, mortality, and severity risk prediction: A review

4. Sensitive detection of rare disease-associated cell subsets via representation learning

5. Accounting for incomplete testing in the estimation of epidemic parameters

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