National Surveys of Population Health: Big Data Analytics for Mobile Health Monitors

被引:18
作者
Schatz, Bruce R. [1 ]
机构
[1] Univ Illinois, Inst Genom Biol, Dept Med Informat, Urbana, IL 61801 USA
基金
美国国家科学基金会;
关键词
big data analytics; big data architecture; crowdsourcing; population health; predictive analytics; DISEASE; IMPACT; LIFE; CARE;
D O I
10.1089/big.2015.0021
中图分类号
TP39 [计算机的应用];
学科分类号
080201 [机械制造及其自动化];
摘要
At the core of the healthcare crisis is fundamental lack of actionable data. Such data could stratify individuals within populations to predict which persons have which outcomes. If baselines existed for all variations of all conditions, then managing health could be improved by matching the measuring of individuals to their cohort in the population. The scale required for complete baselines involves effective National Surveys of Population Health (NSPH). Traditionally, these have been focused upon acute medicine, measuring people to contain the spread of epidemics. In recent decades, the focus has moved to chronic conditions as well, which require smaller measures over longer times. NSPH have long utilized quality of life questionnaires. Mobile Health Monitors, where computing technologies eliminate manual administration, provide richer data sets for health measurement. Older technologies of telephone interviews will be replaced by newer technologies of smartphone sensors to provide deeper individual measures at more frequent timings across larger-sized populations. Such continuous data can provide personal health records, supporting treatment guidelines specialized for population cohorts. Evidence-based medicine will become feasible by leveraging hundreds of millions of persons carrying mobile devices interacting with Internet-scale services for Big Data Analytics.
引用
收藏
页码:219 / 229
页数:11
相关论文
共 51 条
[1]
[Anonymous], 2003, The Future of the Public's Health in the 21st Century, DOI [DOI 10.4103/JPBS.JPBS_168_18, 10.17226/10548, DOI 10.17226/10548]
[2]
[Anonymous], 2010, P 8 ACM C EMB NETW S, DOI DOI 10.1145/1869983.1869992
[3]
[Anonymous], 2008, EV BAS MED CHANG NAT, DOI DOI 10.17226/12041
[4]
[Anonymous], Behavioral Risk Factor Surveillance System
[5]
Using Big Data to Understand the Human Condition: The Kavli Human Project [J].
Azmak, Okan ;
Bayer, Hannah ;
Caplin, Andrew ;
Chun, Miyoung ;
Glimcher, Paul ;
Koonin, Steven ;
Patrinos, Aristides .
BIG DATA, 2015, 3 (03) :173-188
[6]
Berlin Jr RB, 1999, MedGenMed, pE9
[7]
Berlin R B Jr, 2001, Congest Heart Fail, V7, P13, DOI 10.1111/j.1527-5299.2001.00863.x
[8]
The association between self-rated health and mortality in a well-characterized sample of coronary artery disease patients [J].
Bosworth, HB ;
Siegler, IC ;
Brummett, BH ;
Barefoot, JC ;
Williams, RB ;
Clapp-Channing, NE ;
Mark, DB .
MEDICAL CARE, 1999, 37 (12) :1226-1236
[9]
Boyd E., 2011, BRAINS BOTS DEEP INS
[10]
Chee Brant W, 2011, AMIA Annu Symp Proc, V2011, P217