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Found 5 papers, 0 papers with code
Identifying Distorted Thinking in Patient-Therapist Text Message Exchanges by Leveraging Dynamic Multi-Turn Context
In this work, we automatically identify distorted thinking in text-based patient-therapist exchanges, investigating the role of conversation history (context) in distortion prediction.
Improving Classification of Infrequent Cognitive Distortions: Domain-Specific Model vs. Data Augmentation
Performance improvements with an augmented model, MentalBERT, exceed those obtained with data augmentation.
Enhancing Robustness of Foundation Model Representations under Provenance-related Distribution Shifts
We focus on confounding by provenance, a form of distribution shift that emerges in the context of multi-institutional datasets when there are differences in source-specific language use and class distributions.
Backdoor Adjustment of Confounding by Provenance for Robust Text Classification of Multi-institutional Clinical Notes
Machine learning and deep learning approaches have been used to improve the performance of clinical NLP.
Retrofitting Vector Representations of Adverse Event Reporting Data to Structured Knowledge to Improve Pharmacovigilance Signal Detection
Adverse drug events (ADE) are prevalent and costly.
