Axes of a revolution: challenges and promises of big data in healthcare

被引:235
作者
Shilo, Smadar [1 ,2 ,3 ]
Rossman, Hagai [1 ,2 ]
Segal, Eran [1 ,2 ]
机构
[1] Weizmann Inst Sci, Dept Comp Sci & Appl Math, Rehovot, Israel
[2] Weizmann Inst Sci, Dept Mol Cell Biol, Rehovot, Israel
[3] Rambam Healthcare Campus, Pediat Diabet Unit, Ruth Rappaport Childrens Hosp, Haifa, Israel
基金
欧盟地平线“2020”;
关键词
MULTI-OMIC DATA; COHORT PROFILE; UK BIOBANK; CARDIOVASCULAR-DISEASE; PREDICTION; MEDICINE; FUTURE; EPIDEMIOLOGY; ASSOCIATIONS; FRAMINGHAM;
D O I
10.1038/s41591-019-0727-5
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Health data are increasingly being generated at a massive scale, at various levels of phenotyping and from different types of resources. Concurrent with recent technological advances in both data-generation infrastructure and data-analysis methodologies, there have been many claims that these events will revolutionize healthcare, but such claims are still a matter of debate. Addressing the potential and challenges of big data in healthcare requires an understanding of the characteristics of the data. Here we characterize various properties of medical data, which we refer to as 'axes' of data, describe the considerations and tradeoffs taken when such data are generated, and the types of analyses that may achieve the tasks at hand. We then broadly describe the potential and challenges of using big data in healthcare resources, aiming to contribute to the ongoing discussion of the potential of big data resources to advance the understanding of health and disease. Health data are being generated and collected at an unprecedented scale, but whether big data will truly revolutionize healthcare is still a matter of much debate.
引用
收藏
页码:29 / 38
页数:10
相关论文
共 127 条
[1]   Biases in electronic health record data due to processes within the healthcare system: retrospective observational study [J].
Agniel, Denis ;
Kohane, Isaac S. ;
Weber, Griffin M. .
BMJ-BRITISH MEDICAL JOURNAL, 2018, 361
[2]   Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables [J].
Ahlqvist, Emma ;
Storm, Petter ;
Karajamaki, Annemari ;
Martinell, Mats ;
Dorkhan, Mozhgan ;
Carlsson, Annelie ;
Vikman, Petter ;
Prasad, Rashmi B. ;
Aly, Dina Mansour ;
Almgren, Peter ;
Wessman, Ylva ;
Shaat, Nael ;
Spegel, Peter ;
Mulder, Hindrik ;
Lindholm, Eero ;
Melander, Olle ;
Hansson, Ola ;
Malmqvist, Ulf ;
Lernmark, Ake ;
Lahti, Kaj ;
Forsen, Tom ;
Tuomi, Tiinamaija ;
Rosengren, Anders H. ;
Groop, Leif .
LANCET DIABETES & ENDOCRINOLOGY, 2018, 6 (05) :361-369
[3]   The Qatar Biobank: background and methods [J].
Al Kuwari, Hanan ;
Al Thani, Asma ;
Al Marri, Ajayeb ;
Al Kaabi, Abdulla ;
Abderrahim, Hadi ;
Afifi, Nahla ;
Qafoud, Fatima ;
Chan, Queenie ;
Tzoulaki, Ioanna ;
Downey, Paul ;
Ward, Heather ;
Murphy, Neil ;
Riboli, Elio ;
Elliott, Paul .
BMC PUBLIC HEALTH, 2015, 15
[4]   The "All of Us" Research Program [J].
Denny J.C. ;
Rutter J.L. ;
Goldstein D.B. ;
Philippakis A. ;
Smoller J.W. ;
Jenkins G. ;
Dishman E. .
NEW ENGLAND JOURNAL OF MEDICINE, 2019, 381 (07) :668-676
[5]  
[Anonymous], GLOSS STAT TERMS BIO
[6]  
[Anonymous], 2010, DIABETES CARE, DOI DOI 10.2337/dc10-s062
[7]  
[Anonymous], 2016, P 33 INT C MACH LEAR
[8]  
[Anonymous], ARXIV190708322
[9]  
[Anonymous], SCI REP
[10]  
[Anonymous], STAT MED 1220