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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...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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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. |
format | Online Article Text |
id | pubmed-9791221 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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