Biomedical Big Data Analytics for Outcome-Driven Precision Health
May Dongmei Wang, Ph.D.
Distinguished Lecturer of IEEE-EMBS
Associate Professor, The Wallace H. Coulter Joint Department of Biomedical Engineering Georgia Institute of Technology and Emory University
DEIB – Building 21, Conference Room (IV floor)
December 17th, 2015
3.00 pm
Contacts:
Sergio Cerutti
Research Line:
Biomimetics and micro-nano-technologies
Physiological modeling, diagnostics, health systems and e-health
Distinguished Lecturer of IEEE-EMBS
Associate Professor, The Wallace H. Coulter Joint Department of Biomedical Engineering Georgia Institute of Technology and Emory University
DEIB – Building 21, Conference Room (IV floor)
December 17th, 2015
3.00 pm
Contacts:
Sergio Cerutti
Research Line:
Biomimetics and micro-nano-technologies
Physiological modeling, diagnostics, health systems and e-health
Sommario
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 team has been conducting multi-modal and multi-scale (i.e. molecular, cellular, whole body, individual, and population) biomedical data analytics research for discovery, development, and delivery.
First, I will highlight major challenges in biomedical 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 utilities of health analytics for translational medicine such as histopathological imaging informatics for improving clinical decision support; RNA-seq data analytics such as algorithms for users to achieve improved biological utility, reproducibility, and effectiveness in decision making; and Electronic Health Record data quality control and mining. Third, I will discuss emerging research directions such as integrating genomics in EHR. Last, there is big shortage of data scientists and engineers who are capable of handling Big Data to meet the need of healthcare stakeholders (i.e. patients, physicians, payers, and hospitals). 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, USA Centers for Disease Control and Prevention (CDC), and industrial partners such as Microsoft Research and HP.
First, I will highlight major challenges in biomedical 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 utilities of health analytics for translational medicine such as histopathological imaging informatics for improving clinical decision support; RNA-seq data analytics such as algorithms for users to achieve improved biological utility, reproducibility, and effectiveness in decision making; and Electronic Health Record data quality control and mining. Third, I will discuss emerging research directions such as integrating genomics in EHR. Last, there is big shortage of data scientists and engineers who are capable of handling Big Data to meet the need of healthcare stakeholders (i.e. patients, physicians, payers, and hospitals). 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, USA Centers for Disease Control and Prevention (CDC), and industrial partners such as Microsoft Research and HP.
Biografia
May D. Wang, Ph.D. is an Associate Professor in the Joint Department of Biomedical Engineering of Georgia Tech and Emory, School of Electrical and Computer Engineering of Georgia Tech, a Kavli Fellow, a Georgia Research Alliance Distinguished Cancer Scholar, and a Fellow of The American Institute for Biological and Medical Engineering (AIMBE). Professor Wang serves as Co-Director of Biomedical Informatics Program of Georgia Tech in Atlanta Clinical and Translational Science Institute (ACTSI), Co-Director of Georgia-Tech Center of Bio-Imaging Mass Spectrometry, and Biocomputing and Bioinformatics Core Director in Emory-Georgia-Tech Cancer Nanotechnology Center. She is also with Emory Winship Institute, Georgia Tech IBB and and IPaT.
Dr. Wang’s research interest is Biomedical Big Data Analytics, with a focus in Biomedical and Health Informatics (BHI) for Personalized and Predictive Health. Specifically, she works on high throughput NGS and -omic data mining to identify clinical biomarkers, bionanoinformatics, pathological imaging informatics to assist clinical diagnosis, critical and chronic care health informatics for evidence-based decision making, and predictive systems modeling to improve health outcome.She has led RNA-data analysis investigation within FDA-led Sequencing Consortium (SEQC) of MAQC-III.
Currently, Prof. Wang serves as the Senior Editor for IEEE Journal of Biomedical and Health Informatics (J-BHI), an Associate Editor for IEEE Transactions on Biomedical Engineering (TBME), and an Emerging Area Editor for Proceedings of National Academy of Science (PNAS). She also serves as IEEE EMBS Biomedical and Health Informatics Technical Committee Chair, and 2014-2015 EMBS Distinguished Lecturer. In addition, Dr. Wang has devoted to the training of young generation of data scientists and engineers, and is a recipient of Georgia-Tech’s Outstanding Faculty Mentor for Undergraduate Research.
Dr. Wang’s research interest is Biomedical Big Data Analytics, with a focus in Biomedical and Health Informatics (BHI) for Personalized and Predictive Health. Specifically, she works on high throughput NGS and -omic data mining to identify clinical biomarkers, bionanoinformatics, pathological imaging informatics to assist clinical diagnosis, critical and chronic care health informatics for evidence-based decision making, and predictive systems modeling to improve health outcome.She has led RNA-data analysis investigation within FDA-led Sequencing Consortium (SEQC) of MAQC-III.
Currently, Prof. Wang serves as the Senior Editor for IEEE Journal of Biomedical and Health Informatics (J-BHI), an Associate Editor for IEEE Transactions on Biomedical Engineering (TBME), and an Emerging Area Editor for Proceedings of National Academy of Science (PNAS). She also serves as IEEE EMBS Biomedical and Health Informatics Technical Committee Chair, and 2014-2015 EMBS Distinguished Lecturer. In addition, Dr. Wang has devoted to the training of young generation of data scientists and engineers, and is a recipient of Georgia-Tech’s Outstanding Faculty Mentor for Undergraduate Research.