title: An adapted large language model facilitates multiple medical tasks in
    diabetes care
summary: Diabetes is a chronic disease that poses a significant global health burden,
  and optimizing diabetes management requires multi-stakeholder collaboration.
  Large language models (LLMs) have shown promise in various healthcare
  scenarios, but their effectiveness across a diverse range of diabetes tasks
  remains unproven. In this study, we introduced a framework to train and
  validate diabetes-specific LLMs. We first developed a comprehensive data
  processing pipeline that includes data collection, filtering, augmentation and
  refinement. This approach contributes to creating a high-quality,
  diabetes-specific dataset, and several evaluation benchmarks entirely from
  scratch. Utilizing the collected training dataset, we fine-tuned a
  diabetes-specific LLM family that demonstrated state-of-the-art proficiency in
  understanding and processing various diabetes tasks compared to other LLMs.
  Furthermore, clinical studies showed the potential applications of our models
  in diabetes care, including providing personalized healthcare, assisting
  medical education, and streamlining clinical tasks. In conclusion, our study
  introduced a framework to develop and evaluate a diabetes-specific LLM family,
  and highlighted its potential to enhance clinical practice and provide
  personalized, data-driven support for diabetes support when facing different
  end users. The code is provided via GitHub at
  https://github.com/waltonfuture/Diabetica.
paper_authors._id: 66f0cebc7972f6224f1ee568
paper_authors.name: Lai Wei
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-24T07:53:16.513Z
paper_authors._id: 64a16b1aeacb4b50ba1c889d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64a16b1aeacb4b50ba1c889d/EhWoBGL6LlFGIVTUsp2F4.jpeg
paper_authors.fullname: Lai Wei
paper_authors.type: user
paper_authors.user: WaltonFuture
paper_authors._id: 66f0cebc7972f6224f1ee569
paper_authors.name: Zhen Ying
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-10-18T15:55:12.842Z
paper_authors._id: 66f226fec92de46ebbc83a8b
paper_authors.avatarUrl: /avatars/3b813c4d30b6825534b847cb27782558.svg
paper_authors.fullname: Zhen Ying
paper_authors.type: user
paper_authors.user: zying22
paper_authors._id: 66f0cebc7972f6224f1ee56a
paper_authors.name: Muyang He
paper_authors._id: 66f0cebc7972f6224f1ee56b
paper_authors.name: Yutong Chen
paper_authors._id: 66f0cebc7972f6224f1ee56c
paper_authors.name: Qian Yang
paper_authors._id: 66f0cebc7972f6224f1ee56d
paper_authors.name: Yanzhe Hong
paper_authors._id: 66f0cebc7972f6224f1ee56e
paper_authors.name: Jiaping Lu
paper_authors._id: 66f0cebc7972f6224f1ee56f
paper_authors.name: Xiaoying Li
paper_authors._id: 66f0cebc7972f6224f1ee570
paper_authors.name: Weiran Huang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-24T07:53:14.244Z
paper_authors._id: 65e095da35ad8b2fe8c80e71
paper_authors.avatarUrl: /avatars/225d4be6411dcec2460047ea1a88a5c3.svg
paper_authors.fullname: Weiran Huang
paper_authors.type: user
paper_authors.user: weiranhuang
paper_authors._id: 66f0cebc7972f6224f1ee571
paper_authors.name: Ying Chen
paper_title: An adapted large language model facilitates multiple medical tasks in
    diabetes care
paper_summary: Diabetes is a chronic disease that poses a significant global health burden,
  and optimizing diabetes management requires multi-stakeholder collaboration.
  Large language models (LLMs) have shown promise in various healthcare
  scenarios, but their effectiveness across a diverse range of diabetes tasks
  remains unproven. In this study, we introduced a framework to train and
  validate diabetes-specific LLMs. We first developed a comprehensive data
  processing pipeline that includes data collection, filtering, augmentation and
  refinement. This approach contributes to creating a high-quality,
  diabetes-specific dataset, and several evaluation benchmarks entirely from
  scratch. Utilizing the collected training dataset, we fine-tuned a
  diabetes-specific LLM family that demonstrated state-of-the-art proficiency in
  understanding and processing various diabetes tasks compared to other LLMs.
  Furthermore, clinical studies showed the potential applications of our models
  in diabetes care, including providing personalized healthcare, assisting
  medical education, and streamlining clinical tasks. In conclusion, our study
  introduced a framework to develop and evaluate a diabetes-specific LLM family,
  and highlighted its potential to enhance clinical practice and provide
  personalized, data-driven support for diabetes support when facing different
  end users. The code is provided via GitHub at
  https://github.com/waltonfuture/Diabetica.
paper_ai_summary: A framework for developing and evaluating diabetes-specific language models demonstrates superior performance in various diabetes tasks and potential applications in personalized healthcare and medical education.
paper_ai_keywords: large language models LLMs data processing pipeline data augmentation refinement diabetes-specific dataset evaluation benchmarks fine-tuning personalized healthcare medical education clinical tasks data-driven support