A0162
Title: Hierarchical Neyman-Pearson classification for prioritizing severe disease categories in COVID-19 patient data
Authors: Lijia Wang - City University of Hong Kong (Hong Kong) [presenting]
Abstract: COVID-19 has a spectrum of disease severity, ranging from asymptomatic to requiring hospitalization. Understanding the mechanisms driving disease severity is crucial for developing effective treatments and reducing mortality rates. One way to gain such understanding is using a multi-class classification framework, in which patients' biological features are used to predict patients' severity classes. In this severity classification problem, it is beneficial to prioritize identifying more severe classes and control the under-classification errors, in which patients are misclassified into less severe categories. The Neyman-Pearson classification paradigm has been developed to prioritize the designated type of error. However, current NP procedures are either for binary classification or do not provide high probability controls on the prioritized errors in multi-class classification. A hierarchical NP framework and an umbrella algorithm that generally adapts to popular classification methods and controls the under-classification errors with high probability are proposed.
