activity
20242026
most citedLarge Language Models for Healthcare Text Classification: A Systematic Review

21 citations · 25 across the 9 of their papers we have counts for

collaborators
Showing cs.CLShow all

8 papers · 1 filter

cs.CL2026

Unsupervised Neural Network for Automated Classification of Surgical Urgency Levels in Medical Transcriptions

Sadaf Tabatabaee, Sarah S. Lam

Efficient classification of surgical procedures by urgency is paramount to optimize patient care and resource allocation within healthcare systems. This study introduces an unsuper…

cs.CL20251 cited

KDH-MLTC: Knowledge Distillation for Healthcare Multi-Label Text Classification

Hajar Sakai, Sarah S. Lam

The increasing volume of healthcare textual data requires computationally efficient, yet highly accurate classification approaches able to handle the nuanced and complex nature of…

cs.CL2025

HAMLET: Healthcare-focused Adaptive Multilingual Learning Embedding-based Topic Modeling

Hajar Sakai, Sarah S. Lam

Traditional topic models often struggle with contextual nuances and fail to adequately handle polysemy and rare words. This limitation typically results in topics that lack coheren…

cs.CL2025

Large Language Models for Healthcare Text Classification: A Systematic Review

Hajar Sakai, Sarah S. Lam

Large Language Models (LLMs) have fundamentally transformed approaches to Natural Language Processing (NLP) tasks across diverse domains. In healthcare, accurate and cost-efficient…

cs.CL2025

QUAD-LLM-MLTC: Large Language Models Ensemble Learning for Healthcare Text Multi-Label Classification

Hajar Sakai, Sarah S. Lam

The escalating volume of collected healthcare textual data presents a unique challenge for automated Multi-Label Text Classification (MLTC), which is primarily due to the scarcity…

cs.CL2025

BEYONDWORDS is All You Need: Agentic Generative AI based Social Media Themes Extractor

Mohammed-Khalil Ghali, Abdelrahman Farrag, Sarah Lam +1

Thematic analysis of social media posts provides a major understanding of public discourse, yet traditional methods often struggle to capture the complexity and nuance of unstructu…