5 papers
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…
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…
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…
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…
Large Language Models for Patient Comments Multi-Label Classification
Hajar Sakai, Sarah S. Lam, Mohammadsadegh Mikaeili +2
Patient experience and care quality are crucial for a hospital's sustainability and reputation. The analysis of patient feedback offers valuable insight into patient satisfaction a…