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Improving Data-Literacy and Building AI-assistants for Pharmaceutical Professionals
Live Virtual Event
Added:05/03/2024 16:26

The primary goal of the University of Georgia’s (UGA) Precision One Health (POH) initiative is to provide individual patients with the right medical care at the right time through better treatment and disease-prevention. The POH rests upon three pillars:
Pillar 1: Pathogenesis and Diagnostics
Pillar 2: Therapeutic Intervention
Pillar 3: Health Promotion and Disease Prevention

Our approach fits the bench-to-bedside–to-community paradigm through an integration of scientific, clinical and community knowledge. For our data core, these knowledgebases are essentially matrices of genomic, environmental and lifestyle data that often overlap across disciplines (Fig. 1). Our methods combine classical approaches in physical modeling, epidemiology and applied statistics with emerging techniques in artificial intelligence and data-science.
The Systems Modeling and Data Analytics Core (SMDA) supports all three POH Pillars as each domain faces similar data-challenges to realize the promise of personalized healthcare (Fig 1). Fortunately, solutions to these challenges have the potential to revolutionize translational research by:
Increasing the productivity of individual investigators (training)
Improving communication between disciplines
For example, investigators within the UGA colleges: Arts and Sciences (FAS), Veterinary Medicine (CVM), Pharmacy (COP) and Public Health (CPH), work with datasets on: genomics, therapeutic response, diet and environmental exposure. Better coordination of these data-analytics efforts has the potential to reduce duplicative labor, improve workforce training and enable new research that is clinically relevant and scientifically and statistically rigorous (Fig. 1).
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About this item

The primary goal of the University of Georgia’s (UGA) Precision One Health (POH) initiative is to provide individual patients with the right medical care at the right time through better treatment and disease-prevention. The POH rests upon three pillars:

  • Pillar 1: Pathogenesis and Diagnostics
  • Pillar 2: Therapeutic Intervention
  • Pillar 3: Health Promotion and Disease Prevention

Our approach fits the bench-to-bedside–to-community paradigm through an integration of scientific, clinical and community knowledge. For our data core, these knowledgebases are essentially matrices of genomic, environmental and lifestyle data that often overlap across disciplines (Fig. 1). Our methods combine classical approaches in physical modeling, epidemiology and applied statistics with emerging techniques in artificial intelligence and data-science.

The Systems Modeling and Data Analytics Core (SMDA) supports all three POH Pillars as each domain faces similar data-challenges to realize the promise of personalized healthcare (Fig 1). Fortunately, solutions to these challenges have the potential to revolutionize translational research by:

  • Increasing the productivity of individual investigators (training)
  • Improving communication between disciplines

For example, investigators within the UGA colleges: Arts and Sciences (FAS), Veterinary Medicine (CVM), Pharmacy (COP) and Public Health (CPH), work with datasets on: genomics, therapeutic response, diet and environmental exposure. Better coordination of these data-analytics efforts has the potential to reduce duplicative labor, improve workforce training and enable new research that is clinically relevant and scientifically and statistically rigorous (Fig. 1).

Speaker Information

Jonathan Mochel, Ph.D.

 

Eugene Douglass, Ph.D.

Dr. Douglass has training in physical and organic chemistry, immunology and computational biology. In his undergraduate work (Worcester Polytechnic Institute) he worked with physicists and chemists and published first and second author publications in medical-diagnostics and photovoltaics, respectively. During his doctoral work (Yale University) he designed, synthesized and tested small-molecule immunotherapies and fluorescent sensors. In addition, he derived a closed form solution for a drug-kinetics problem that had remained unsolved for 70 years. As a postdoctoral researcher, he helped build drug-screening and RNA-sequencing infrastructure that supported several clinical trials at Columbia University.

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