DocumentCode :
3658386
Title :
Biomedical Big Data Analytics for Patient-Centric and Outcome-Driven Precision Health
Author :
May D. Wang
Author_Institution :
Georgia Inst. of Technol., Atlanta, GA, USA
Volume :
3
fYear :
2015
fDate :
7/1/2015 12:00:00 AM
Firstpage :
1
Lastpage :
2
Abstract :
Rapid advancements in biotechnologies such as -omic (genomics, proteomics, metabolomics, lipidomics etc.), next generation sequencing, bio-nanotechnologies, molecular imaging, and mobile sensors etc. accelerate the data explosion in biomedicine and health wellness. Multiple nations around the world have been seeking novel effective ways to make sense of "big data" for evidence-based, outcome-driven, and affordable 5P (Patient-centric, Predictive, Preventive, Personalized, and Precise) healthcare. My main research focus is on multi-modal and multi-scale (i.e. molecular, cellular, whole body, individual, and population) biomedical data analytics for discovery, development, and delivery, including translational bioinformatics in biomarker discovery for personalized care; imaging informatics in histopathology for clinical diagnosis decision support; bionanoinformatics for minimally-invasive image-guided surgery; critical care informatics in ICU for real-time evidence-based decision making; and chronic care informatics for patient-centric health. In this talk, first, I will highlight major challenges in biomedical and health informatics pipeline consisting of data quality control, information feature extraction, advanced knowledge modeling, decision making, and proper action taking through feedback. Second, I will present informatics methodological research in (i) data integrity and integration; (ii) case-based reasoning for individualized care; and (iii) streaming data analytics for real-time decision support using a few mobile health case studies (e.g. Sickle Cell Disease, asthma, pain management, rehabilitation, diabetes etc.). Last, there is big shortage of data scientists and engineers who are capable of handling Big Data. In addition, there is an urgent need to educate healthcare stakeholders (i.e. patients, physicians, payers, and hospitals) on how to tackle these grant challenges. I will discuss efforts such as patient-centric educational intervention, community-based crowd sourcing, and Biomedical Data Analytics MOOC development. Our research has been supported by NIH, NSF, Georgia Research Alliance, Georgia Cancer Coalition, Emory-Georgia Tech Cancer Nanotechnology Center, Children´s Health Care of Atlanta, Atlanta Clinical and Translational Science Institute, and industrial partners such as Microsoft Research and HP.
Keywords :
"Biomedical imaging","Informatics","Bioinformatics","Big data","Cancer","Decision making"
Publisher :
ieee
Conference_Titel :
Computer Software and Applications Conference (COMPSAC), 2015 IEEE 39th Annual
Electronic_ISBN :
0730-3157
Type :
conf
DOI :
10.1109/COMPSAC.2015.343
Filename :
7273312
Link To Document :
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