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Predicting the Information Need for Domestic Violence Survivors Based on the Fine-Tuned Large Language Model.
Hui, Vivian; Guan, Shaowei; Zhang, Bohan; Lee, Young Ji; Constantino, Rose E.
Afiliação
  • Hui V; Centre for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Hong Kong SAR.
  • Guan S; Health and Community Systems, School of Nursing, University of Pittsburgh, PA, USA.
  • Zhang B; Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong SAR.
  • Lee YJ; Centre for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Hong Kong SAR.
  • Constantino RE; Health and Community Systems, School of Nursing, University of Pittsburgh, PA, USA.
Stud Health Technol Inform ; 315: 691-692, 2024 Jul 24.
Article em En | MEDLINE | ID: mdl-39049384
ABSTRACT
Women with domestic violence experiences often refuse to seek help face-to-face due to embarrassment. They begin to share their emotions and seek help from online health communities. Understanding and responding to these posts can be crucial in providing timely support to the victims. We proposed a fine-tuned large language model (LLM) capable of accurately predicting the informational need based on the content of postings. We fine-tuned the LAMMA2-7B-chat model based on the guidance of identifying the information need and a dataset comprising 273 posts from Reddit, which are manually annotated by domain experts. Furthermore, we evaluated the performance of our model using a random sample of 15 posts, and 66.6% were accurately predicted. The results demonstrate that our model can rapidly capture the information needs expressed in the posts, enabling healthcare providers to provide timely and useful support based on our predictions.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Sobreviventes / Violência Doméstica Limite: Female / Humans Idioma: En Revista: Stud Health Technol Inform Assunto da revista: INFORMATICA MEDICA / PESQUISA EM SERVICOS DE SAUDE Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Sobreviventes / Violência Doméstica Limite: Female / Humans Idioma: En Revista: Stud Health Technol Inform Assunto da revista: INFORMATICA MEDICA / PESQUISA EM SERVICOS DE SAUDE Ano de publicação: 2024 Tipo de documento: Article