<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>It is imperative to embark on a significant model-driven technology route for the intelligent development of the medical industry. This paper constructs a medical big model through three aspects: medical drug recommendation, medical data sampling, and medical image segmentation. The link between symptoms and drugs is established through the PALAS algorithm, the unbalanced numerical dataset is solved by using the oversampling SMOTE method, and the source domain of medical images is labeled by the MCDIFL method to adapt to the unsupervised domain in medical image segmentation. Finally, the development trend of medical macromodeling is discussed, and the data of diabetic patients admitted to Hospital X is used as a study case to specifically explore the effect of medical macromodeling in healthcare. The results show that the data of diabetic patient A was inputted into the medical extensive model analysis to obtain that the average values of patient A’s blood glucose value in the first three years were 7.13, 9.34, and 7.06 mmol/L, respectively, which experienced the evolution from mild to high and then to soft. The results can help medical personnel to make a scientific treatment plan for the patient. This study promotes the innovative application and development of artificial intelligence technology in medical services.</p>
</abstract>

It is imperative to embark on a significant model-driven technology route for the intelligent development of the medical industry. This paper constructs a medical big model through three aspects: medical drug recommendation, medical data sampling, and medical image segmentation. The link between symptoms and drugs is established through the PALAS algorithm, the unbalanced numerical dataset is solved by using the oversampling SMOTE method, and the source domain of medical images is labeled by the MCDIFL method to adapt to the unsupervised domain in medical image segmentation. Finally, the development trend of medical macromodeling is discussed, and the data of diabetic patients admitted to Hospital X is used as a study case to specifically explore the effect of medical macromodeling in healthcare. The results show that the data of diabetic patient A was inputted into the medical extensive model analysis to obtain that the average values of patient A’s blood glucose value in the first three years were 7.13, 9.34, and 7.06 mmol/L, respectively, which experienced the evolution from mild to high and then to soft. The results can help medical personnel to make a scientific treatment plan for the patient. This study promotes the innovative application and development of artificial intelligence technology in medical services.

Development Status and Strategy Analysis of Medical Big Models

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Development Status and Strategy Analysis of Medical Big Models"}

It is imperative to embark on a significant model-driven technology route for the intelligent development of the medical industry. This paper constructs a...

Development Status and Strategy Analysis of Medical Big Models

Published Online: Feb 26, 2024

Page range: -

Received: Jan 02, 2024

Accepted: Jan 07, 2024

DOI: https://doi.org/10.2478/amns-2024-0325

Keywords
Data sampling, Image segmentation, Medical macromodeling, PALAS algorithm, Oversampling SMOTE

© 2024 Guozheng Zhang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Development Status and Strategy Analysis of Medical Big Models

Published Online: Feb 26, 2024

Page range: -

Received: Jan 02, 2024

Accepted: Jan 07, 2024

DOI: https://doi.org/10.2478/amns-2024-0325

KeywordsData sampling, Image segmentation, Medical macromodeling, PALAS algorithm, Oversampling SMOTE

© 2024 Guozheng Zhang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
Data sampling, Image segmentation, Medical macromodeling, PALAS algorithm, Oversampling SMOTE