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Review
. 2017 Feb;64(2):263-273.
doi: 10.1109/TBME.2016.2573285. Epub 2016 Oct 10.

-Omic and Electronic Health Record Big Data Analytics for Precision Medicine

Po-Yen Wu, Chih-Wen Cheng, Chanchala D Kaddi, Janani Venugopalan, Ryan Hoffman, May D Wang
Review

-Omic and Electronic Health Record Big Data Analytics for Precision Medicine

Po-Yen Wu et al. IEEE Trans Biomed Eng. 2017 Feb.

Abstract

Objective: Rapid advances of high-throughput technologies and wide adoption of electronic health records (EHRs) have led to fast accumulation of -omic and EHR data. These voluminous complex data contain abundant information for precision medicine, and big data analytics can extract such knowledge to improve the quality of healthcare.

Methods: In this paper, we present -omic and EHR data characteristics, associated challenges, and data analytics including data preprocessing, mining, and modeling.

Results: To demonstrate how big data analytics enables precision medicine, we provide two case studies, including identifying disease biomarkers from multi-omic data and incorporating -omic information into EHR.

Conclusion: Big data analytics is able to address -omic and EHR data challenges for paradigm shift toward precision medicine.

Significance: Big data analytics makes sense of -omic and EHR data to improve healthcare outcome. It has long lasting societal impact.

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Figures

Fig. 1
Fig. 1
The key types of biomedical big data for precision medicine.
Fig. 2
Fig. 2
Integrative analysis of multi-omic data leads to the improved understanding of cancer mechanisms, which in turn enables more precise classification of cancer subtypes.
Fig. 3
Fig. 3
Integrating derived -omic knowledge into the existing EHR system is an approach to utilizing molecular information for clinical decision support, and it also help deliver precision medicine.
See this image and copyright information in PMC

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