Search Results for author:
Found 8 papers, 5 papers with code
MedKP: Medical Dialogue with Knowledge Enhancement and Clinical Pathway Encoding
With appropriate data selection and training techniques, Large Language Models (LLMs) have demonstrated exceptional success in various medical examinations and multiple-choice questions.
Guiding Clinical Reasoning with Large Language Models via Knowledge Seeds
Clinical reasoning refers to the cognitive process that physicians employ in evaluating and managing patients.
Streamlining Social Media Information Retrieval for COVID-19 Research with Deep Learning
Conclusions: This study advances public health research by implementing a novel, systematic pipeline for curating symptom lexicons from social media data.
Large Language Models Leverage External Knowledge to Extend Clinical Insight Beyond Language Boundaries
We systematically evaluate LLMs in the Chinese medical context and develop a novel in-context learning framework to enhance their performance.
Exploring Social Media for Early Detection of Depression in COVID-19 Patients
Secondly, We conducted an extensive analysis of this dataset to investigate the characteristic of COVID-19 patients with a higher risk of depression.
GreenPLM: Cross-Lingual Transfer of Monolingual Pre-Trained Language Models at Almost No Cost
Large pre-trained models have revolutionized natural language processing (NLP) research and applications, but high training costs and limited data resources have prevented their benefits from being shared equally amongst speakers of all the world's languages.
YATO: Yet Another deep learning based Text analysis Open toolkit
Designed in a hierarchical structure, YATO supports free combinations of three types of widely used features including 1) traditional neural networks (CNN, RNN, etc.
METS-CoV: A Dataset of Medical Entity and Targeted Sentiment on COVID-19 Related Tweets
To further investigate tweet users' attitudes toward specific entities, 4 types of entities (Person, Organization, Drug, and Vaccine) are selected and annotated with user sentiments, resulting in a targeted sentiment dataset with 9, 101 entities (in 5, 278 tweets).
