Search Results for author:
Found 12 papers, 7 papers with code
BRIDGE: Benchmarking Large Language Models for Understanding Real-world Clinical Practice Text
Large language models (LLMs) hold great promise for medical applications and are evolving rapidly, with new models being released at an accelerated pace.
A New Adaptive Balanced Augmented Lagrangian Method with Application to ISAC Beamforming Design
In this paper, we consider a class of convex programming problems with linear equality constraints, which finds broad applications in machine learning and signal processing.
Revealing COVID-19's Social Dynamics: Diachronic Semantic Analysis of Vaccine and Symptom Discourse on Twitter
Social media is recognized as an important source for deriving insights into public opinion dynamics and social impacts due to the vast textual data generated daily and the 'unconstrained' behavior of people interacting on these platforms.
FinTruthQA: A Benchmark Dataset for Evaluating the Quality of Financial Information Disclosure
This paper builds a benchmark FinTruthQA, that can evaluate advanced natural language processing (NLP) techniques for the automatic quality assessment of information disclosure in financial Q&A data.
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).
