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Disease-specific data processing: An intelligent digital platform for diabetes based on model prediction and data analysis utilizing big data technology

BACKGROUND: Artificial intelligence technology has become a mainstream trend in the development of medical informatization. Because of the complex structure and a large amount of medical data generated in the current medical informatization process, big data technology to assist doctors in scientifi...

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Autores principales: Kong, Xiangyong, Peng, Ruiyang, Dai, Huajie, Li, Yichi, Lu, Yanzhuan, Sun, Xiaohan, Zheng, Bozhong, Wang, Yuze, Zhao, Zhiyun, Liang, Shaolin, Xu, Min
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9791221/
https://www.ncbi.nlm.nih.gov/pubmed/36579056
http://dx.doi.org/10.3389/fpubh.2022.1053269
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author Kong, Xiangyong
Peng, Ruiyang
Dai, Huajie
Li, Yichi
Lu, Yanzhuan
Sun, Xiaohan
Zheng, Bozhong
Wang, Yuze
Zhao, Zhiyun
Liang, Shaolin
Xu, Min
author_facet Kong, Xiangyong
Peng, Ruiyang
Dai, Huajie
Li, Yichi
Lu, Yanzhuan
Sun, Xiaohan
Zheng, Bozhong
Wang, Yuze
Zhao, Zhiyun
Liang, Shaolin
Xu, Min
author_sort Kong, Xiangyong
collection PubMed
description BACKGROUND: Artificial intelligence technology has become a mainstream trend in the development of medical informatization. Because of the complex structure and a large amount of medical data generated in the current medical informatization process, big data technology to assist doctors in scientific research and analysis and obtain high-value information has become indispensable for medical and scientific research. METHODS: This study aims to discuss the architecture of diabetes intelligent digital platform by analyzing existing data mining methods and platform building experience in the medical field, using a large data platform building technology utilizing the Hadoop system, model prediction, and data processing analysis methods based on the principles of statistics and machine learning. We propose three major building mechanisms, namely the medical data integration and governance mechanism (DCM), data sharing and privacy protection mechanism (DPM), and medical application and medical research mechanism (MCM), to break down the barriers between traditional medical research and digital medical research. Additionally, we built an efficient and convenient intelligent diabetes model prediction and data analysis platform for clinical research. RESULTS: Research results from this platform are currently applied to medical research at Shanghai T Hospital. In terms of performance, the platform runs smoothly and is capable of handling massive amounts of medical data in real-time. In terms of functions, data acquisition, cleaning, and mining are all integrated into the system. Through a simple and intuitive interface operation, medical and scientific research data can be processed and analyzed conveniently and quickly. CONCLUSIONS: The platform can serve as an auxiliary tool for medical personnel and promote the development of medical informatization and scientific research. Also, the platform may provide the opportunity to deliver evidence-based digital therapeutics and support digital healthcare services for future medicine.
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spelling pubmed-97912212022-12-27 Disease-specific data processing: An intelligent digital platform for diabetes based on model prediction and data analysis utilizing big data technology Kong, Xiangyong Peng, Ruiyang Dai, Huajie Li, Yichi Lu, Yanzhuan Sun, Xiaohan Zheng, Bozhong Wang, Yuze Zhao, Zhiyun Liang, Shaolin Xu, Min Front Public Health Public Health BACKGROUND: Artificial intelligence technology has become a mainstream trend in the development of medical informatization. Because of the complex structure and a large amount of medical data generated in the current medical informatization process, big data technology to assist doctors in scientific research and analysis and obtain high-value information has become indispensable for medical and scientific research. METHODS: This study aims to discuss the architecture of diabetes intelligent digital platform by analyzing existing data mining methods and platform building experience in the medical field, using a large data platform building technology utilizing the Hadoop system, model prediction, and data processing analysis methods based on the principles of statistics and machine learning. We propose three major building mechanisms, namely the medical data integration and governance mechanism (DCM), data sharing and privacy protection mechanism (DPM), and medical application and medical research mechanism (MCM), to break down the barriers between traditional medical research and digital medical research. Additionally, we built an efficient and convenient intelligent diabetes model prediction and data analysis platform for clinical research. RESULTS: Research results from this platform are currently applied to medical research at Shanghai T Hospital. In terms of performance, the platform runs smoothly and is capable of handling massive amounts of medical data in real-time. In terms of functions, data acquisition, cleaning, and mining are all integrated into the system. Through a simple and intuitive interface operation, medical and scientific research data can be processed and analyzed conveniently and quickly. CONCLUSIONS: The platform can serve as an auxiliary tool for medical personnel and promote the development of medical informatization and scientific research. Also, the platform may provide the opportunity to deliver evidence-based digital therapeutics and support digital healthcare services for future medicine. Frontiers Media S.A. 2022-12-12 /pmc/articles/PMC9791221/ /pubmed/36579056 http://dx.doi.org/10.3389/fpubh.2022.1053269 Text en Copyright © 2022 Kong, Peng, Dai, Li, Lu, Sun, Zheng, Wang, Zhao, Liang and Xu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Kong, Xiangyong
Peng, Ruiyang
Dai, Huajie
Li, Yichi
Lu, Yanzhuan
Sun, Xiaohan
Zheng, Bozhong
Wang, Yuze
Zhao, Zhiyun
Liang, Shaolin
Xu, Min
Disease-specific data processing: An intelligent digital platform for diabetes based on model prediction and data analysis utilizing big data technology
title Disease-specific data processing: An intelligent digital platform for diabetes based on model prediction and data analysis utilizing big data technology
title_full Disease-specific data processing: An intelligent digital platform for diabetes based on model prediction and data analysis utilizing big data technology
title_fullStr Disease-specific data processing: An intelligent digital platform for diabetes based on model prediction and data analysis utilizing big data technology
title_full_unstemmed Disease-specific data processing: An intelligent digital platform for diabetes based on model prediction and data analysis utilizing big data technology
title_short Disease-specific data processing: An intelligent digital platform for diabetes based on model prediction and data analysis utilizing big data technology
title_sort disease-specific data processing: an intelligent digital platform for diabetes based on model prediction and data analysis utilizing big data technology
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9791221/
https://www.ncbi.nlm.nih.gov/pubmed/36579056
http://dx.doi.org/10.3389/fpubh.2022.1053269
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