The path from big data to precision medicine

Huang, Bevan Emma; Mulyasasmita, Widya; Rajagopal, G.

doi:10.1080/23808993.2016.1157686

Cited by 38 publications

(18 citation statements)

References 128 publications

(139 reference statements)

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“…Data collection might introduce various forms of biases. Examples are batch effects, which might occur in the merging of data from different institutions; batch effects can be addressed by a careful statistical analysis [56], [57].…”

Section: G Comments On the Value Of Big Data Studiesmentioning

confidence: 99%

“…the aforementioned Genomics England 100K project or the NIH precision medicine initiative) that will collect genomic and other biomedical data from individuals for the next 5-10 years. A comprehensive and recent overview of these cohort studies from publicly or private funded entities can be found in [56]. The experiences gained from these initiatives will reveal interesting insights and lessons learned about data management of genomic and other "omics" data (e.g.…”

Section: F Digitization Challenges In Precision Medicinementioning

confidence: 99%

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Going Digital: A Survey on Digitalization and Large-Scale Data Analytics in Healthcare

et al. 2016

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We provide an overview of the recent trends towards digitalization and large scale data analytics in healthcare. It is expected that these trends are instrumental in the dramatic changes in the way healthcare will be organized in the future. We discuss the recent political initiatives designed to shift care delivery processes from paper to electronic, with the goals of more effective treatments with better outcomes; cost pressure is a major driver of innovation. We describe newly developed networks of healthcare providers, research organizations and commercial vendors to jointly analyze data for the development of decision support systems. We address the trend towards continuous healthcare where health is monitored by wearable and stationary devices; a related development is that patients increasingly assume responsibility for their own health data. Finally we discuss recent initiatives towards a personalized medicine, based on advances in molecular medicine, data management, and data analytics.

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Section: G Comments On the Value Of Big Data Studiesmentioning

confidence: 99%

Section: F Digitization Challenges In Precision Medicinementioning

confidence: 99%

Going Digital: A Survey on Digitalization and Large-Scale Data Analytics in Healthcare

et al. 2016

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“…Another important research field has been introduced thanks to the adoption of IoT in medical environment the "precision medicine". The "precision medicine" [117] paradigm has become very popular over recent years, powered by scientific as well as political perspectives.…”

Section: N Personalized Medicine and Precision Medicinementioning

confidence: 99%

E-health-IoT Universe: A Review

Scarpato¹,

Pieroni²,

Nunzio³

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

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The Internet of Things (IoT) devices are able to collect and share data directly with other devices through the cloud environment, providing a huge amount of information to be gathered, stored and analyzed for data-analytics processes. The scenarios in which the IoT devices may be useful are amazing varying, from automotive, to industrial automation or remote monitoring of domestic environment. Furthermore, has been proved that healthcare applications represent an important field of interest for IoT devices, due to the capability of improving the access to care, reducing the cost of healthcare and most importantly increasing the quality of life of the patients. In this paper, we analyze the state-of-art of IoT in medical environment, illustrating an extended range of IoT-driven healthcare applications that, however, still need innovative and high technology-based solutions to be considered ready to market. In particular, problems regarding characteristics of response-time and precision will be examined. Furthermore, wearable and energy saving properties will be investigated in this paper and also the IT architectures able to ensure security and privacy during the all data-transmission process. Finally, considerations about data mining applications, such as risks prediction, classification and clustering will be provided, that are considered fundamental issues to ensure the accuracy of the care processes.

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“…Precision medicine seeks to identify which treatment will be effective for which patients through long-term study of genetics, environmental and lifestyle data. In recent years significant resources have been invested in a range of public and private precision medicine initiatives [ 4 ]. The National Institute of Health’s All of Us Research Program [ 5 ] seeks to enroll one million Americans with an emphasis on reaching diverse and underserved populations; Project Baseline [ 6 ] is a collaboration of Google’s Verily and academic centers at Duke and Stanford Universities that will study 10,000 people of different ages, backgrounds, and medical histories over 4 years; Arivale [ 7 ], a wellness company with its roots in the One Hundred Person Wellness Project at the Institute for Systems Biology offers a concierge-style approach to genomic testing with individualized coaching; Human Longevity [ 8 ], offers high-end direct to consumer genomic testing services with plans to combine machine learning with a comprehensive database of human genotypes and phenotypes subjected to develop new ways to fight diseases associated with aging; and Foundation Medicine in partnership with Flatiron Health [ 9 ], while not focused directly on reaching patients and consumers, are developing an oncology clinic-genomic database resource for use by researchers to accelerate the development of targeted and immunotherapies to treat cancer.…”

Section: Introductionmentioning

confidence: 99%

DigitalMe: a journey towards personalized health and thriving

Okun

Wicks

2018

BioMed Eng OnLine

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The use of information and communication technologies for health (eHealth) delivered via mobile-based or digitally enhanced solutions (mHealth) have rapidly evolved. When used together across various mobile applications and devices eHealth and mHealth technologies have the ability to passively monitor behavior as an indicator of socialization and mood; accumulate a range of biomedical data such as weight and heart rate; and track metrics associated with activities including steps taken and hours slept. Yet, these technologies are insufficient for measuring the full array of data about an individual and the impact of that data on a person’s current and future health. Digital health converges eHealth and mHealth with patient data about their health, healthcare, living, and environment with genomics. An innovative opportunity to unravel the complexities of disease and aging is increasingly possible with an integrative multi-omics approach informed by multidisciplinary sciences including medicine, design, biomedical informatics and engineering. The digitization of individual level data from all available sources makes possible the development of DigitalMe™, a personalized virtual avatar of a real person. The combination of longitudinally collected person generated data and molecular data derived from biospecimens offers researchers unique opportunities to better understand the mechanisms of disease while advancing person-centric hypotheses generation related to treatments, diagnostics, and prognostics.

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The path from big data to precision medicine

Cited by 38 publications

References 128 publications

Going Digital: A Survey on Digitalization and Large-Scale Data Analytics in Healthcare

Going Digital: A Survey on Digitalization and Large-Scale Data Analytics in Healthcare

E-health-IoT Universe: A Review

DigitalMe: a journey towards personalized health and thriving

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