activity
20222026
most citedA Study of Generative Large Language Model for Medical Research and Healthcare

400 citations · 645 across the 20 of their papers we have counts for

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Showing 2023Show all

7 papers · 1 filter

cs.CL2023

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…

cs.CL2023★ 1 cited

On the Impact of Cross-Domain Data on German Language Models

Amin Dada, Aokun Chen, Cheng Peng +12

Traditionally, large language models have been either trained on general web crawls or domain-specific data. However, recent successes of generative large language models, have she…

cs.CL2023★ 71 cited

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…

cs.CL2023★ 400 cited

A Study of Generative Large Language Model for Medical Research and Healthcare

Cheng Peng, Xi Yang, Aokun Chen +16

There is enormous enthusiasm and concerns in using large language models (LLMs) in healthcare, yet current assumptions are all based on general-purpose LLMs such as ChatGPT. This s…

cs.CL2023★ 1 cited

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…

cs.CL2023★ 30 cited

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…

cs.CL2023★ 40 cited

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…