Use of Large Language Models to Assess the Likelihood of Epidemics From the Content of Tweets: Infodemiology Study.

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第一作者： Michael S,Deiner

第一单位： Department of Ophthalmology, University of California, San Francisco, San Francisco, CA, United States.

作者： Michael S,Deiner ^[1] ; Natalie A,Deiner ^[2] ; Vagelis,Hristidis ^[3] ; Stephen D,McLeod ^[4] ; Thuy,Doan ^[1] ; Thomas M,Lietman ^[2] ; Travis C,Porco ^[5]

作者单位： Department of Ophthalmology, University of California, San Francisco, San Francisco, CA, United States. ^[1] Francis I. Proctor Foundation for Research in Ophthalmology, University of California, San Francisco, San Francisco, CA, United States. ^[2] College of Letters and Science, University of California, Santa Barbara, Santa Barbara, CA, United States. ^[3] Department of Computer Science and Engineering, University of California, Riverside, Riverside, CA, United States. ^[4] American Academy of Ophthalmology, San Francisco, CA, United States. ^[5] Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, United States. ^[6]

关键词 GPT-3.5 GPT-4 Generative Pre-trained Transformers Twitter X formerly known as Twitter conjunctivitis epidemic detection generative large language model infectious eye disease microblog social media

主题词人类(Humans);美国(United States);疾病流行(Epidemics);疾病暴发流行(Disease Outbreaks);结膜炎(Conjunctivitis);语言(Language)

DOI 10.2196/49139

PMID 38427404

发布时间 2024-03-18

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