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20232026
most citedBackdoor Adjustment of Confounding by Provenance for Robust Text Classification of Multi-institutional Clinical Notes

1 citations · 1 across the 8 of their papers we have counts for

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cs.CL2025

Mitigating Confounding in Speech-Based Dementia Detection through Weight Masking

Zhecheng Sheng, Xiruo Ding, Brian Hur +3

Deep transformer models have been used to detect linguistic anomalies in patient transcripts for early Alzheimer's disease (AD) screening. While pre-trained neural language models…

cs.CL2025

"Is There Anything Else?'': Examining Administrator Influence on Linguistic Features from the Cookie Theft Picture Description Cognitive Test

Changye Li, Zhecheng Sheng, Trevor Cohen +1

Alzheimer's Disease (AD) dementia is a progressive neurodegenerative disease that negatively impacts patients' cognitive ability. Previous studies have demonstrated that changes in…

cs.CL2024

Reexamining Racial Disparities in Automatic Speech Recognition Performance: The Role of Confounding by Provenance

Changye Li, Trevor Cohen, Serguei Pakhomov

Automatic speech recognition (ASR) models trained on large amounts of audio data are now widely used to convert speech to written text in a variety of applications from video capti…

cs.CL2024

Too Big to Fail: Larger Language Models are Disproportionately Resilient to Induction of Dementia-Related Linguistic Anomalies

Changye Li, Zhecheng Sheng, Trevor Cohen +1

As artificial neural networks grow in complexity, understanding their inner workings becomes increasingly challenging, which is particularly important in healthcare applications. T…

cs.CL2024

Useful Blunders: Can Automated Speech Recognition Errors Improve Downstream Dementia Classification?

Changye Li, Weizhe Xu, Trevor Cohen +1

\textbf{Objectives}: We aimed to investigate how errors from automatic speech recognition (ASR) systems affect dementia classification accuracy, specifically in the ``Cookie Theft'…

cs.CL2023

Enhancing Robustness of Foundation Model Representations under Provenance-related Distribution Shifts

Xiruo Ding, Zhecheng Sheng, Brian Hur +3

Foundation models are a current focus of attention in both industry and academia. While they have shown their capabilities in a variety of tasks, in-depth research is required to d…