Expanding Consumer Health Vocabulary Across Languages Using Word Embeddings from User-Generated Content
This research proposes a cross-lingual automatic term recognition framework to extend the English consumer health vocabulary (CHV) into other languages by leveraging word embeddings learned from comparable user-generated health content.
Leveraging Large Language Models for Improved Health-Related Text Classification on Social Media Data
Employing data augmentation using large language models (GPT-4) with human-annotated data to train lightweight supervised classification models achieves superior results compared to training with human-annotated data alone or using large language models as zero-shot classifiers.
Large Language Models in Biomedical and Health Informatics: A Comprehensive Review
Large Language Models (LLMs) are transforming Biomedical and Health Informatics (BHI) by enhancing data analysis, patient care, and research through advanced Natural Language Processing (NLP) applications.
Large Language Model Applications in Mental Health: A Systematic Review
LLMs show potential in mental health applications but face challenges like interpretability and ethical concerns.
Detection of Opioid Users from Reddit Posts via an Attention-based Bidirectional Recurrent Neural Network
Machine learning models, specifically the Att-BLSTM, can accurately identify opioid users from Reddit posts, providing valuable insights into opioid addiction.
Automated Extraction and Maturity Analysis of Open Source Clinical Informatics Repositories from Scientific Literature
Automated methodology extracts and analyzes open-source clinical informatics repositories to enhance accessibility and utilization.
Understanding Surgeons’ Visual Needs During Intra-operative Coordination and Instruction
Shared gaze visualizations can enhance intra-operative coordination and instruction for surgeons, addressing varying visual needs of trainees.
Improving Generalizability of Extracting Social Determinants of Health Using Large Language Models through Prompt-tuning
Generative LLMs with P-tuning enhance SDoH extraction across domains.
Streamlining Social Media Information Retrieval for COVID-19 Research with Deep Learning
Developing a systematic pipeline for curating symptom lexicons from social media data using deep learning advances public health research by providing reliable medical insights.
Advancements in Glioma Diagnosis with Multiple Instance Learning using Hematoxylin and Eosin Whole Slide Images
The author presents a novel approach utilizing multiple instance learning to enhance glioma diagnosis, achieving state-of-the-art results in subtype classification and biomarker detection through rigorous experimentation.