Big data for personalized diabetes prevention

被引:5
|
作者
Jarasch, A. [1 ]
Glaser, A. [1 ]
Haering, H. [1 ,2 ]
Roden, M. [1 ,3 ]
Schuermann, A. [1 ,4 ]
Solimena, M. [1 ,5 ]
Theiss, F. [1 ,6 ]
Tschoep, M. [1 ,7 ]
Wess, G. [1 ,8 ]
de Angelis, M. Hrabe [1 ,9 ]
机构
[1] Helmholtz Zentrum Munchen, DZD, Ingolstadter Landstr 1, D-85764 Neuherberg, Germany
[2] Eberhard Karls Univ Tubingen, Inst Diabetesforsch & Metabol Erkrankungen Hel, Tubingen, Germany
[3] Deutsch Diabet Zentrum, Dusseldorf, Germany
[4] Deutsch Inst Ernahrungsforsch Potsdam Rehbruck, Nuthetal, Germany
[5] Tech Univ Dresden, Paul Langerhans Inst Helmholtz Zentrums Munchen, Univ Klinikum Carl Gustav Carus, Dresden, Germany
[6] Helmholtz Zentrum Munchen Deutsch Forschungszentr, Inst Comp Biol, Neuherberg, Germany
[7] Helmholtz Zentrum Munchen Deutsch Forschungszentr, Inst Diabet & Obes, Neuherberg, Germany
[8] Helmholtz Zentrum Munchen Deutsch Forschungszentr, Neuherberg, Germany
[9] Helmholtz Zentrum Munchen Deutsch Forschungszentr, Inst Expt Genet, Neuherberg, Germany
来源
DIABETOLOGE | 2018年 / 14卷 / 07期
关键词
Prediabetic state; Subtypes; Preventive medicine; Medical informatics; Artificial intelligence;
D O I
10.1007/s11428-018-0384-1
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Since 1980, the number of people with diabetes has quadrupled worldwide. In Germany alone, almost 7million people suffer from this metabolic disease and every year, there are up to 500,000 new diagnoses. These numbers show the urgent need for new effective prevention measures and innovative forms of treatment. Digitalization makes it possible to explore the widespread disease of diabetes in anew dimension in order to identify subtypes of diabetes very early on and offer suitable personalized preventive measures. With the establishment of aDigital Diabetes Prevention Center, health and research data from awide variety of sources could be brought together, analysed and evaluated using innovative information technology (IT) capabilities to identify different diabetes subtypes and offer specific prevention and therapy measures that can be used directly through close cooperation with the population.
引用
收藏
页码:486 / 492
页数:7
相关论文
共 50 条
  • [31] Diabetes care: is big data the future?
    Begg, Alan
    PRACTICAL DIABETES, 2022, 39 (03) : 7 - 9
  • [32] Diabetes Prevention Program Enhanced with Online Personalized Diet Plan
    Turnipseed, Hunter
    Martinez, Erica
    Whigham, Leah
    Salsa, Ghadir Helal
    Mitchell-Bennett, Lisa
    Reininger, Belinda
    Dhurandhar, Rohan
    Dhurandhar, Emily
    Dhurandhar, Nikhil
    OBESITY, 2023, 31 : 172 - 173
  • [33] Big data versus small data analysis towards personalized medicine practice
    Mira Marcus-Kalish
    EPMA Journal, 2014, 5 (Suppl 1):
  • [34] Will Big Data and personalized medicine do the gender dimension justice?
    Antonio Carnevale
    Emanuela A. Tangari
    Andrea Iannone
    Elena Sartini
    AI & SOCIETY, 2023, 38 : 829 - 841
  • [35] iASiS: Towards Heterogeneous Big Data Analysis for Personalized Medicine
    Krithara, Anastasia
    Aisopos, Fotis
    Rentoumi, Vassiliki
    Nentidis, Anastasios
    Bougatiotis, Konstantinos
    Vidal, Maria-Esther
    Menasalvas, Ernestina
    Rodriguez-Gonzalez, Alejandro
    Samaras, Eleftherios G.
    Garrard, Peter
    Torrente, Maria
    Provencio Pulla, Mariano
    Dimakopoulos, Nikos
    Mauricio, Rui
    Rambla De Argila, Jordi
    Gaetano Tartaglia, Gian
    Paliouras, George
    2019 IEEE 32ND INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS), 2019, : 106 - 111
  • [36] Construction of Personalized English Teaching Model Driven by Big Data
    Zhang Xiaohui
    2019 INTERNATIONAL CONFERENCE ON ARTS, MANAGEMENT, EDUCATION AND INNOVATION (ICAMEI 2019), 2019, : 349 - 353
  • [37] Research on Personalized Exercises and Teaching Feedback Based on Big Data
    Xie, Xiaolan
    Li, Xinrong
    ICIIP'18: PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON INTELLIGENT INFORMATION PROCESSING, 2018, : 166 - 171
  • [38] Big data for neuroscience in the context of predictive, preventive, and personalized medicine
    Bajinka, Ousman
    Ouedraogo, Serge Yannick
    Li, Na
    Zhan, Xianquan
    EPMA JOURNAL, 2025, 16 (01): : 17 - 35
  • [39] Big Data Analysis for Personalized Medicine in Lung Cancer Patients
    Torrente, M.
    Nunez-Garcia, B.
    Franco, F.
    Calvo De Juan, V.
    Menasalvas, E.
    Rodriguez-Gonzalez, A.
    Parejo, C.
    Provencio, M.
    JOURNAL OF THORACIC ONCOLOGY, 2019, 14 (10) : S313 - S314
  • [40] Big data analytics of social network marketing and personalized recommendations
    Shu-Hsien Liao
    Ching-An Yang
    Social Network Analysis and Mining, 2021, 11