An adapted large language model facilitates multiple medical tasks in
  diabetes care
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.
66f0cebc7972f6224f1ee568
Lai Wei
claimed_verified
2024-09-24T07:53:16.513Z
64a16b1aeacb4b50ba1c889d
https://cdn-avatars.huggingface.co/v1/production/uploads/64a16b1aeacb4b50ba1c889d/EhWoBGL6LlFGIVTUsp2F4.jpeg
Lai Wei
user
WaltonFuture
66f0cebc7972f6224f1ee569
Zhen Ying
claimed_verified
2024-10-18T15:55:12.842Z
66f226fec92de46ebbc83a8b
/avatars/3b813c4d30b6825534b847cb27782558.svg
Zhen Ying
user
zying22
66f0cebc7972f6224f1ee56a
Muyang He
66f0cebc7972f6224f1ee56b
Yutong Chen
66f0cebc7972f6224f1ee56c
Qian Yang
66f0cebc7972f6224f1ee56d
Yanzhe Hong
66f0cebc7972f6224f1ee56e
Jiaping Lu
66f0cebc7972f6224f1ee56f
Xiaoying Li
66f0cebc7972f6224f1ee570
Weiran Huang
claimed_verified
2024-09-24T07:53:14.244Z
65e095da35ad8b2fe8c80e71
/avatars/225d4be6411dcec2460047ea1a88a5c3.svg
Weiran Huang
user
weiranhuang
66f0cebc7972f6224f1ee571
Ying Chen
An adapted large language model facilitates multiple medical tasks in
  diabetes care
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.
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.
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