85 citations · 319 across the 11 of their papers we have counts for
11 papers
Improving Generalizability of Extracting Social Determinants of Health Using Large Language Models through Prompt-tuning
Cheng Peng, Zehao Yu, Kaleb E Smith +3
The progress in natural language processing (NLP) using large language models (LLMs) has greatly improved patient information extraction from clinical narratives. However, most met…
Generative Large Language Models Are All-purpose Text Analytics Engines: Text-to-text Learning Is All Your Need
Cheng Peng, Xi Yang, Aokun Chen +6
Objective To solve major clinical natural language processing (NLP) tasks using a unified text-to-text learning architecture based on a generative large language model (LLM) via pr…
Model Tuning or Prompt Tuning? A Study of Large Language Models for Clinical Concept and Relation Extraction
Cheng Peng, Xi Yang, Kaleb E Smith +4
Objective To develop soft prompt-based learning algorithms for large language models (LLMs), examine the shape of prompts, prompt-tuning using frozen/unfrozen LLMs, transfer learni…
Extracting Thyroid Nodules Characteristics from Ultrasound Reports Using Transformer-based Natural Language Processing Methods
Aman Pathak, Zehao Yu, Daniel Paredes +5
The ultrasound characteristics of thyroid nodules guide the evaluation of thyroid cancer in patients with thyroid nodules. However, the characteristics of thyroid nodules are often…
Identifying Symptoms of Delirium from Clinical Narratives Using Natural Language Processing
Aokun Chen, Daniel Paredes, Zehao Yu +13
Delirium is an acute decline or fluctuation in attention, awareness, or other cognitive function that can lead to serious adverse outcomes. Despite the severe outcomes, delirium is…
Clinical Concept and Relation Extraction Using Prompt-based Machine Reading Comprehension
Cheng Peng, Xi Yang, Zehao Yu +3
Objective: To develop a natural language processing system that solves both clinical concept extraction and relation extraction in a unified prompt-based machine reading comprehens…
Contextualized Medication Information Extraction Using Transformer-based Deep Learning Architectures
Aokun Chen, Zehao Yu, Xi Yang +3
Objective: To develop a natural language processing (NLP) system to extract medications and contextual information that help understand drug changes. This project is part of the 20…
SODA: A Natural Language Processing Package to Extract Social Determinants of Health for Cancer Studies
Zehao Yu, Xi Yang, Chong Dang +12
Objective: We aim to develop an open-source natural language processing (NLP) package, SODA (i.e., SOcial DeterminAnts), with pre-trained transformer models to extract social deter…
Clinical Relation Extraction Using Transformer-based Models
Xi Yang, Zehao Yu, Yi Guo +2
The newly emerged transformer technology has a tremendous impact on NLP research. In the general English domain, transformer-based models have achieved state-of-the-art performance…
A Study of Social and Behavioral Determinants of Health in Lung Cancer Patients Using Transformers-based Natural Language Processing Models
Zehao Yu, Xi Yang, Chong Dang +8
Social and behavioral determinants of health (SBDoH) have important roles in shaping people's health. In clinical research studies, especially comparative effectiveness studies, fa…
AS-MLP: An Axial Shifted MLP Architecture for Vision
Dongze Lian, Zehao Yu, Xing Sun +1
An Axial Shifted MLP architecture (AS-MLP) is proposed in this paper. Different from MLP-Mixer, where the global spatial feature is encoded for information flow through matrix tran…