Details

Autor(en) / Beteiligte

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

Titel

Omic and Electronic Health Record Big Data Analytics for Precision Medicine

Ist Teil von

• IEEE transactions on biomedical engineering, 2017-02, Vol.64 (2), p.263-273

Ort / Verlag

United States: IEEE

Erscheinungsjahr

2017

Quelle

MEDLINE

Beschreibungen/Notizen

• 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.