Publications by authors named "Neelay Velingker"
Article Synopsis
- Patients recovering from COVID-19 often experience lingering symptoms known as Long COVID, which can manifest weeks or months after their initial infection, but the prevalence of this condition is not well understood.
- To address this, a collaborative initiative called the Long COVID Computational Challenge (L3C) was launched to develop effective risk prediction tools for identifying individuals at risk of Long COVID using extensive healthcare data from over 75 institutions in the U.S.
- The challenge resulted in 74 teams creating 35 predictive models, with the top models achieving high accuracy scores, demonstrating the potential for machine learning to enhance the identification of patients at risk for Long COVID.
DISCRET: Synthesizing Faithful Explanations For Treatment Effect Estimation.
Proc Mach Learn Res
July 2024
Designing faithful yet accurate AI models is challenging, particularly in the field of individual treatment effect estimation (ITE). ITE prediction models deployed in critical settings such as healthcare should ideally be (i) accurate, and (ii) provide faithful explanations. However, current solutions are inadequate: state-of-the-art black-box models do not supply explanations, post-hoc explainers for black-box models lack faithfulness guarantees, and self-interpretable models greatly compromise accuracy.
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