Difference between revisions of "Emerging Technologies"
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Revision as of 07:20, 3 January 2018Research, development, and adoption of innovative, but not widely adopted, tools and technologies for leveraging and enhancing the value of healthcare and clinical data. This can include use of existing or immature technologies in innovative ways.
Working Group Overview
New challenges in regulatory science and drug, biologic, and device development provide new opportunities for recognizing and leveraging new or emerging technologies and computational tools or underutilized existing technologies. Initiated at the 2013 PhUSE Annual CSS, the Emerging Technologies working group provides a forum for determining interest in specific computational science topics, tools, technologies, and approaches.
This Emerging Technologies working group will be an open, transparent forum for sharing pre-competitive means of applying new technologies and is being challenged with creation of well-defined collaborative projects that will describe, prioritize, assess, and assist advancement of these opportunities. Possible topics include (but are not limited to) semantic web applications, analysis metadata, modeling, simulation, and “The Cloud”. Projects incorporating these topics might include prioritization, development, and piloting for feasibility and value.
|Geoff Low||ET Co-Lead||Medidata Solutions||geoff.low (at) phuse.eu|
|Ian Fleming||ET Co-Lead||Medidata Solutions||Ian.Fleming (at) phuse.eu|
|Lilliam Rosario||Steering Committee Liaison||FDA||Lilliam.Rosario (at) fda.hhs.gov|
|Crystal Allard||Group Coordinator||FDA||Crystal.Allard (at) fda.hhs.gov|
|Steve Wilson||FDA Co-Lead||FDA||Stephen.Wilson (at) fda.hhs.gov|
PhUSE CSS 2017
The following projects are meeting at 2017 CSS
- Evaluation Of Alternative Transport Formats - Work on data representations
- Cloud Adoption in the Life Sciences Industry
- Data Visualisations for Clinical Data
- Investigating the use of FHIR in Clinical Research
- Educating for the Future
The Agenda is as follows:
- Metadata Definition Project: File:ET-project-template-metadata-definitions.doc
- Metadata Management Project: File:ET-project-Template - User Guide for E2E meta data management.doc
After the Emerging Technology Round Table Sessions the following projects were identified as prospective working groups for the 2015 CSS and beyond. Some of these need passionate people to lead!
Framework for mHealth
As personal fitness trackers become more ubiquitous, industry is starting to assess how these devices can be used to supplement the clinical record. Very much in its infancy, use of these personal devices is a grey area in terms of adoption status, information quality (for example, the use of proprietary data gathering algorithms), patient privacy and informed consent. This group will develop a framework so companies looking to adopt these devices in their clinical trials can make an informed decision as they assess the potential of integrating mobile health with clinical research.
- Sponsor: Tony Hewer
- Needs a Lead?: Maybe
Machine Learning for Clinical Research
With the continually raising profile of Data Science in the Pharmaceutical Industry, techniques which have traditionally not been widely adopted in the industry are starting to become part of a clinical analyst's toolbox. Text Analytics, already employed in other industries, enables analysts to uncover clinically important information in previously opaque free-text. Techniques to be discussed include sentiment analysis, concept/entity extraction, clustering and other approaches. This group will gather people interested in text mining techniques to develop frameworks and approaches for extracting structured information from unstructured clinical trial text data.
- Sponsor: CDER
- Needs a Lead?: Yes
Big Data approaches in Clinical Research
Big Data is a term we are all familiar with - it is promoted as the way to comprehensively answer multiple questions across multiple domains. But what is Big Data? Is it relevant to clinical researcher? Is it simply large volumes of data? Is it these large datasets plus the techniques needed to extract value from this data? What approaches work and when? This group will begin by answering the most basic questions associated with Big Data and clinical research -- is it relevant, and where do we go from here? This topic has been previously posed and we have a Project Request: File:ET-project-Template-BigData.doc
- Needs a Lead?: Yes