4 citations · 7 across the 5 of their papers we have counts for
5 papers
Fine-Tune, Don't Prompt, Your Language Model to Identify Biased Language in Clinical Notes
Isotta Landi, Eugenia Alleva, Nicole Bussola +5
Clinical documentation can contain emotionally charged language with stigmatizing or privileging valences. We present a framework for detecting and classifying such language as sti…
Predict Patient Self-reported Race from Skin Histological Images
Shengjia Chen, Ruchika Verma, Kevin Clare +6
Artificial Intelligence (AI) has demonstrated success in computational pathology (CPath) for disease detection, biomarker classification, and prognosis prediction. However, its pot…
Multi-Task Learning for Extracting Menstrual Characteristics from Clinical Notes
Anna Shopova, Cristoph Lippert, Leslee J. Shaw +1
Menstrual health is a critical yet often overlooked aspect of women's healthcare. Despite its clinical relevance, detailed data on menstrual characteristics is rarely available in…
Clinical Text Deduplication Practices for Efficient Pretraining and Improved Clinical Tasks
Isotta Landi, Eugenia Alleva, Alissa A. Valentine +2
Despite being a unique source of information on patients' status and disease progression, clinical notes are characterized by high levels of duplication and information redundancy.…
Keyword-optimized Template Insertion for Clinical Information Extraction via Prompt-based Learning
Eugenia Alleva, Isotta Landi, Leslee J Shaw +3
Clinical note classification is a common clinical NLP task. However, annotated data-sets are scarse. Prompt-based learning has recently emerged as an effective method to adapt pre-…