1 citations · 3 across the 7 of their papers we have counts for
7 papers
DeconDTN-Toolkit: A Library for Evaluation and Enhancement of Robustness to Provenance Shift
Yongsen Tan, Zhecheng Sheng, Xiruo Ding +2
Despite the burgeoning body of work on distribution shifts, provenance shift-where the relationship between data source and label changes at deployment-remains poorly understood an…
When Prompts Interact: Assessing Prompt Arithmetic for Deconfounding under Distribution Shift
Zhecheng Sheng, Yongsen Tan, Xiruo Ding +2
In classification tasks, models may rely on confounding variables to achieve strong in-distribution performance, capturing spurious features that fail under distribution shift. Thi…
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…
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…
Backdoor Adjustment of Confounding by Provenance for Robust Text Classification of Multi-institutional Clinical Notes
Xiruo Ding, Zhecheng Sheng, Meliha Yetişgen +2
Natural Language Processing (NLP) methods have been broadly applied to clinical tasks. Machine learning and deep learning approaches have been used to improve the performance of cl…
Retrofitting Vector Representations of Adverse Event Reporting Data to Structured Knowledge to Improve Pharmacovigilance Signal Detection
Xiruo Ding, Trevor Cohen
Adverse drug events (ADE) are prevalent and costly. Clinical trials are constrained in their ability to identify potential ADEs, motivating the development of spontaneous reporting…
Electronic Health Record Phenotyping with Internally Assessable Performance (PhIAP) using Anchor-Positive and Unlabeled Patients
Lingjiao Zhang, Xiruo Ding, Yanyuan Ma +5
Building phenotype models using electronic health record (EHR) data conventionally requires manually labeled cases and controls. Assigning labels is labor intensive and, for some p…