Leverage Big Data Analytics to Enhance Clinical Trials from Planning to Execu...Saama
Nikhil Gopinath, Senior Solutions Engineer for the Life Sciences at Saama, spoke at EyeforPharma's Clinical Trial Innovation Summit event in February 2017. These slides are from his "Leverage Big Data Analytics to Enhance Clinical Trials from Planning to Execution" presentation.
How BrackenData Leverages Data on Over 250,000 Clinical TrialsBracken
Learn about our why we've created our clinical trial intelligence solutions, how they provide big value to teams in the life sciences industry, and how you can start leveraging data immediately.
Discussion Forum data, sourced from sites like Reddit and other social media platforms, as well other sources of textual information, provides tremendous opportunity for insight and innovation. This presentation focuses on how an analysis of unstructured data can be used to innovate in Life/Health Science organizations
Identifying Drug Interaction Candidates in Real-World DataNeo4j
Speakers: Kathleen Mandziuk, Vice President, Patient Strategy and Digital Health, PRA HealthSciences
Nathan Smith, Senior Principal Data Scientist, PRA HealthSciences
Kerry Deem, Associate Director, Programming, PRA HealthSciences
Leverage Big Data Analytics to Enhance Clinical Trials from Planning to Execu...Saama
Nikhil Gopinath, Senior Solutions Engineer for the Life Sciences at Saama, spoke at EyeforPharma's Clinical Trial Innovation Summit event in February 2017. These slides are from his "Leverage Big Data Analytics to Enhance Clinical Trials from Planning to Execution" presentation.
How BrackenData Leverages Data on Over 250,000 Clinical TrialsBracken
Learn about our why we've created our clinical trial intelligence solutions, how they provide big value to teams in the life sciences industry, and how you can start leveraging data immediately.
Discussion Forum data, sourced from sites like Reddit and other social media platforms, as well other sources of textual information, provides tremendous opportunity for insight and innovation. This presentation focuses on how an analysis of unstructured data can be used to innovate in Life/Health Science organizations
Identifying Drug Interaction Candidates in Real-World DataNeo4j
Speakers: Kathleen Mandziuk, Vice President, Patient Strategy and Digital Health, PRA HealthSciences
Nathan Smith, Senior Principal Data Scientist, PRA HealthSciences
Kerry Deem, Associate Director, Programming, PRA HealthSciences
Using Feedback from Data Consumers to Capture Quality Information on Environm...Anusuriya Devaraju
Data quality information is essential to facilitate reuse of Earth science data. Recorded quality information must be sufficient for other researchers to select suitable data sets for their analysis and confirm the results and conclusions. In the research data ecosystem, several entities are responsible for data quality. Data producers (researchers and agencies) play a major role in this aspect as they often include validation checks or data cleaning as part of their work. It is possible that the quality information is not supplied with published data sets; if it is available, the descriptions might be incomplete, ambiguous or address specific quality aspects. Data repositories have built infrastructures to share data, but not all of them assess data quality. They normally provide guidelines of documenting quality information. Some suggests that scholarly and data journals should take a role in ensuring data quality by involving reviewers to assess data sets used in articles, and incorporating data quality criteria in the author guidelines. However, this mechanism primarily addresses data sets submitted to journals. We believe that data consumers will complement existing entities to assess and document the quality of published data sets. This has been adopted in crowd-source platforms such as Zooniverse, OpenStreetMap, Wikipedia, Mechanical Turk and Tomnod. This paper presents a framework designed based on open source tools to capture and share data users’ feedback on the application and assessment of research data. The framework comprises a browser plug-in, a web service and a data model such that feedback can be easily reported, retrieved and searched. The feedback records are also made available as Linked Data to promote integration with other sources on the Web. Vocabularies from Dublin Core and PROV-O are used to clarify the source and attribution of feedback. The application of the framework is illustrated with the CSIRO’s Data Access Portal.
There are a growing number of examples demonstrating compelling and creative uses of data provided by U.S. Department of Health and Human Services (HHS) agencies.
HHS provides a wealth of open data sources and APIs. Industry, researchers and media have been able to put these data assets to good use, creating significant economic value, informing the public and improving public health.
Building an Intelligent Biobank to Power Research Decision-MakingDenodo
This presentation belongs to the workshop: "Building an Intelligent Biobank to Power Research Decision-Making", from ISBER 2015 Annual Meeting by Lori A. Ball (Chief Operating Officer, President of Integrated Client Solutions at BioStorage Technologies, Inc), Brian Brunner (Senior Manager, Clinical Practice at LabAnswer) and Suresh Chandrasekaran (Senior Vice President at Denodo).
The workshop cover three different topic areas:
- Research sample intelligence: the growing need for Global Data Integration (Biobank Sample and Data Stakeholders).
- Building a research data integration plan and cloud sourcing strategy (data integration).
- How data virtualization works and the value it delivers (a data virtualization introduction, solution portfolio and current customers in Life Sciences industry).
The biomedical R&D environment is increasingly dependent on data meta-analysis and bioinformatics to support research advancements. The integration of biorepository sample inventory data with biomarker and clinical research information has become a priority to R&D organizations. Therefore, a flexible IT system for managing sample collections, integrating sample data with clinical data and providing a data virtualization platform will enable the advancement of research studies. This workshop provides an overview of how sample data integration, virtualization and analytics can lead to more streamlined and unified sample intelligence to support global biobanking for future research.
American College of Radiology, Data Science Institute, AI-Lab
The ACR Data Science Institute has developed the ACR AI-LAB™, a data science toolkit designed to democratize AI by empowering radiologists to develop algorithms at their own institutions, using their own patient data, to meet their own clinical needs.
Access Lab 2020: Context aware unified institutional knowledge services: an open architecture for digital libraries to offer a seamless user journey to content
Alvet Miranda, senior manager or South/West Asia, Oceania and Africa, EBSCO
Carl Kesselman and I (along with our colleagues Stephan Erberich, Jonathan Silverstein, and Steve Tuecke) participated in an interesting workshop at the Institute of Medicine on July 14, 2009. Along with Patrick Soon-Shiong, we presented our views on how grid technologies can help address the challenges inherent in healthcare data integration.
Overview of Library & Systematic Review (LASYR) Infrastructure for Blockchain and Emerging Technologies project at IEEE Healthcare: Blockchain & AI event - 07 April 2021
How much is that data in the window : Healthcare data valuationSean Manion PhD
Presentation on healthcare data valuation, data confidence fabrics, layers of trust in healthcare, and health data marketplaces as part of the Health Data Valuation event, Session 10 of the IEEE Healthcare: Blockchain & AI Virtual Series on 25 August 2021
This document explores the concepts behind how DDOD (Demand-Driven Open Data) can be used in conjunction with FOIA (Freedom of Information Act) requests. It describes how DDOD and FOIA can leverage each other's strengths to help overcome their inherent challenges.
DDOD is an initiative by the U.S. Department of Health and Human Services (HHS) started in November 2014 as part of its IDEA Lab program. The goal is to leverage the vast data assets throughout HHS’s agencies (CMS, FDA, NIH, CDC, NCHS, AHRQ and others) to create additional economic and public health value.
DDOD provides a systematic, ongoing and transparent mechanism for anybody to tell HHS and its agencies what data would be valuable to them. It's the Lean Startup approach to open data. With this initiative HHS can move from measuring Open Data in terms of number of datasets released to value in terms of use cases enabled.
DDOD website: http://ddod.us
Why is the NIH investing $100M at the intersection of data science and health research? The NIH seeks to invest in ways to help researchers easily find, access, analyze, and curate research data. Researchers want visual analytics, and to build the database into a “social network” – being able to “friend” or “like” the data.
Impact of DDOD on Data Quality - White House 2016David Portnoy
"The Impact of Demand-Driven Open Data (DDOD) on Data Quality" was presented on April 27, 2016 at Open Data Roundtable held at the White House Office of Science and Technology Policy.
It discusses the data quality problems prevalent in open data and their impact, the origins of the DDOD concept, how it works, progress towards its goals, several use case examples, and how to implement it at other organizations.
More information:
* DDOD http://ddod.healthdata.gov
* Open Data Roundtables https://www.data.gov/meta/open-data-roundtables/
* White House Office of Science and Technology Policy: https://www.whitehouse.gov/blog/2016/02/05/open-data-empowering-americans-make-data-driven-decisions
Bridging Health Care and Clinical Trial Data through TechnologySaama
Karim Damji, SVP of Product and Marketing, presented at the Bridging Clinical Research and Clinical Health Care conference held at the Gaylord in National Harbor on April 4-5, 2018.
Using Feedback from Data Consumers to Capture Quality Information on Environm...Anusuriya Devaraju
Data quality information is essential to facilitate reuse of Earth science data. Recorded quality information must be sufficient for other researchers to select suitable data sets for their analysis and confirm the results and conclusions. In the research data ecosystem, several entities are responsible for data quality. Data producers (researchers and agencies) play a major role in this aspect as they often include validation checks or data cleaning as part of their work. It is possible that the quality information is not supplied with published data sets; if it is available, the descriptions might be incomplete, ambiguous or address specific quality aspects. Data repositories have built infrastructures to share data, but not all of them assess data quality. They normally provide guidelines of documenting quality information. Some suggests that scholarly and data journals should take a role in ensuring data quality by involving reviewers to assess data sets used in articles, and incorporating data quality criteria in the author guidelines. However, this mechanism primarily addresses data sets submitted to journals. We believe that data consumers will complement existing entities to assess and document the quality of published data sets. This has been adopted in crowd-source platforms such as Zooniverse, OpenStreetMap, Wikipedia, Mechanical Turk and Tomnod. This paper presents a framework designed based on open source tools to capture and share data users’ feedback on the application and assessment of research data. The framework comprises a browser plug-in, a web service and a data model such that feedback can be easily reported, retrieved and searched. The feedback records are also made available as Linked Data to promote integration with other sources on the Web. Vocabularies from Dublin Core and PROV-O are used to clarify the source and attribution of feedback. The application of the framework is illustrated with the CSIRO’s Data Access Portal.
There are a growing number of examples demonstrating compelling and creative uses of data provided by U.S. Department of Health and Human Services (HHS) agencies.
HHS provides a wealth of open data sources and APIs. Industry, researchers and media have been able to put these data assets to good use, creating significant economic value, informing the public and improving public health.
Building an Intelligent Biobank to Power Research Decision-MakingDenodo
This presentation belongs to the workshop: "Building an Intelligent Biobank to Power Research Decision-Making", from ISBER 2015 Annual Meeting by Lori A. Ball (Chief Operating Officer, President of Integrated Client Solutions at BioStorage Technologies, Inc), Brian Brunner (Senior Manager, Clinical Practice at LabAnswer) and Suresh Chandrasekaran (Senior Vice President at Denodo).
The workshop cover three different topic areas:
- Research sample intelligence: the growing need for Global Data Integration (Biobank Sample and Data Stakeholders).
- Building a research data integration plan and cloud sourcing strategy (data integration).
- How data virtualization works and the value it delivers (a data virtualization introduction, solution portfolio and current customers in Life Sciences industry).
The biomedical R&D environment is increasingly dependent on data meta-analysis and bioinformatics to support research advancements. The integration of biorepository sample inventory data with biomarker and clinical research information has become a priority to R&D organizations. Therefore, a flexible IT system for managing sample collections, integrating sample data with clinical data and providing a data virtualization platform will enable the advancement of research studies. This workshop provides an overview of how sample data integration, virtualization and analytics can lead to more streamlined and unified sample intelligence to support global biobanking for future research.
American College of Radiology, Data Science Institute, AI-Lab
The ACR Data Science Institute has developed the ACR AI-LAB™, a data science toolkit designed to democratize AI by empowering radiologists to develop algorithms at their own institutions, using their own patient data, to meet their own clinical needs.
Access Lab 2020: Context aware unified institutional knowledge services: an open architecture for digital libraries to offer a seamless user journey to content
Alvet Miranda, senior manager or South/West Asia, Oceania and Africa, EBSCO
Carl Kesselman and I (along with our colleagues Stephan Erberich, Jonathan Silverstein, and Steve Tuecke) participated in an interesting workshop at the Institute of Medicine on July 14, 2009. Along with Patrick Soon-Shiong, we presented our views on how grid technologies can help address the challenges inherent in healthcare data integration.
Overview of Library & Systematic Review (LASYR) Infrastructure for Blockchain and Emerging Technologies project at IEEE Healthcare: Blockchain & AI event - 07 April 2021
How much is that data in the window : Healthcare data valuationSean Manion PhD
Presentation on healthcare data valuation, data confidence fabrics, layers of trust in healthcare, and health data marketplaces as part of the Health Data Valuation event, Session 10 of the IEEE Healthcare: Blockchain & AI Virtual Series on 25 August 2021
This document explores the concepts behind how DDOD (Demand-Driven Open Data) can be used in conjunction with FOIA (Freedom of Information Act) requests. It describes how DDOD and FOIA can leverage each other's strengths to help overcome their inherent challenges.
DDOD is an initiative by the U.S. Department of Health and Human Services (HHS) started in November 2014 as part of its IDEA Lab program. The goal is to leverage the vast data assets throughout HHS’s agencies (CMS, FDA, NIH, CDC, NCHS, AHRQ and others) to create additional economic and public health value.
DDOD provides a systematic, ongoing and transparent mechanism for anybody to tell HHS and its agencies what data would be valuable to them. It's the Lean Startup approach to open data. With this initiative HHS can move from measuring Open Data in terms of number of datasets released to value in terms of use cases enabled.
DDOD website: http://ddod.us
Why is the NIH investing $100M at the intersection of data science and health research? The NIH seeks to invest in ways to help researchers easily find, access, analyze, and curate research data. Researchers want visual analytics, and to build the database into a “social network” – being able to “friend” or “like” the data.
Impact of DDOD on Data Quality - White House 2016David Portnoy
"The Impact of Demand-Driven Open Data (DDOD) on Data Quality" was presented on April 27, 2016 at Open Data Roundtable held at the White House Office of Science and Technology Policy.
It discusses the data quality problems prevalent in open data and their impact, the origins of the DDOD concept, how it works, progress towards its goals, several use case examples, and how to implement it at other organizations.
More information:
* DDOD http://ddod.healthdata.gov
* Open Data Roundtables https://www.data.gov/meta/open-data-roundtables/
* White House Office of Science and Technology Policy: https://www.whitehouse.gov/blog/2016/02/05/open-data-empowering-americans-make-data-driven-decisions
Bridging Health Care and Clinical Trial Data through TechnologySaama
Karim Damji, SVP of Product and Marketing, presented at the Bridging Clinical Research and Clinical Health Care conference held at the Gaylord in National Harbor on April 4-5, 2018.
Enabling Discovery in High-Risk Plaque using Semantic Web ApproachesTom Plasterer
Enabling Discovery in High-Risk Plaque using Semantic Web Approaches
The HRP initiative (HRP) is a joint research and development effort to advance the understanding, recognition and management of high-risk plaque for the benefit of multiple stakeholders in the healthcare system. As the primary underlying cause of heart attacks, high-risk, or vulnerable plaque is the number one cause of death in the Western world. There are currently no methods of screening, diagnosis or treatment for high-risk plaque.
The HRP initiative leverages recent advances in biology and information technology to design and optimize a care-cycle for high-risk plaque, promising to reduce morbidity, mortality and cost associated with cardiovascular disease. This Initiative is being led by the world’s foremost scientists in the fields of cardiology, pathology, and imaging, and is made possible through funding by leading pharmaceutical and medical technology entities.
HRP takes advantages of semantic web technologies for physician and researcher-lead data analysis and data interoperability. One of the key applications is a web tool linking patient demographics, clinical chemistries, physical measurements and cardiovascular imaging modalities. This empowers scientists to rapidly compare multiple clinical parameters to find patients of interest, assisting greatly in defining high-risk plaque.
Challenges in Clinical Research: Aridhia Disrupts Technology Approach to Rese...VMware Tanzu
Join Jeff Kelly, Pivotal’s Big Data Strategist and Chris Roche, Aridhia’s CEO, to learn how Big Data and data science are being applied to clinical research. Learn…
• Why research-oriented healthcare delivery organizations and academic medical centers need an ACRIS
• How improving collaboration and productivity accelerates the discovery of insights and increases competitiveness
• Why robust data security is critical to modernizing engagement between academia, industry and healthcare
• How to reduce research costs while improving commercialization opportunities
• Why enabling transparent analysis and reproducibility of research are key to scientific progress
• Best practices to get started on your digital transformation and Big Data journey
Challenges in Clinical Research: Aridhia's Disruptive Technology Approach to ...Aridhia Informatics Ltd
This webinar with our partner Pivotal aired in July 2016.
The increasing sophistication of modern medicine, a seemingly endless supply of data, and the ability to perform large-scale computation is transforming clinical research. However, utilising data to generate new treatments and therapies has continued to prove complicated. The silo-based information systems built over the last 30 years are simply unable to scale to support today’s use cases.
Aridhia, creators of AnalytiXagility, the ground-breaking research and healthcare data analysis platform, is now enabling its customers to rapidly analyse massive amounts of data in meaningful ways to change how diseases are understood, managed and treated. Powered by Pivotal Greenplum, AnalytiXagility is at the forefront of Advanced Clinical Research Information Systems (ACRIS), one of Gartner’s 10 “Transformational Digital Disruptors in Healthcare by 2025”.
Learn how big data and data science are being applied to clinical research and:
• Why research-oriented healthcare delivery organizations and academic medical centers need an ACRIS
• How improving collaboration and productivity accelerates the discovery of insights and increases competiveness
• Why robust data security is critical to modernizing engagement between academia, industry and healthcare
• How to reduce research costs while improving commercialization opportunities
• Why enabling transparent analysis and reproducibility of research are key to scientific progress
• Best practices to get started on your digital transformation and Big Data journey
The clinical development data deluge is reaching critical mass for pharmaceuticals. Use of varied data for targeted outcomes remains difficult, despite studies that generate evidence of the risk-benefit profile of investigational products. New technologies are federating the ability to leverage analytic-ready data for innovations in clinical operations and clinical science. With the application of clinical data-as-a-service and meta-data core, centralized clinical data lakes have the power to improve data quality, evidence generation, and time-to-insights.
Karim Damji and Benzi Mathews presented this deck at the Clinical Trial Innovation Summit held in Boston on April 24-26.
Enterprise Analytics: Serving Big Data Projects for HealthcareDATA360US
Andrew Rosenberg's Presentation on "Enterprise Analytics: Serving Big Data Projects for Healthcare" at DATA 360 Healthcare Informatics Conference - March 5th, 2015
Clinical Data Models - The Hyve - Bio IT World April 2019Kees van Bochove
Population genetics and genomics is an emerging topic for the application of machine learning methods in healthcare and biomedical sciences. Currently, several large genomics initiatives, such as Genomics England, UK Biobank, the All of Us Project, and Europe's 1 Million Genomes Initiative are all in the process of making both clinical and genomics data available from large numbers of patients to benefit biomedical research. However, a key challenge in these initiatives is the standardization of the clinical and outcomes data in such a way that machine learning methods can be effectively trained to discover useful medical and scientific insights. In this talk, we will look at what data is available at scale, and review some of examples of the application of common data and evidence models such as OMOP, FHIR, GA4GH etc. in order to achieve this, based on projects which The Hyve has executed with some of these initiatives to harmonize their clinical, genomics, imaging and wearables data and make it FAIR.
In this presentation, you will learn how to transform a Big Data initiative into a realized, measurable ROI:
• Understand the complex mix of business expectation, hype, reality, and new information source opportunities in the Big Data space
• Use the Business Case process to help to you identify what you can achieve and what is not yet ready
• Build communities of interest around prototypes and plan for success for your company’s advantage
• Learn how to industrialize your Big Data innovations to achieve measurable, sustainable benefits
Data Harmonization for a Molecularly Driven Health SystemWarren Kibbe
Seminar for Dr. Min Zhang's Purdue Bioinformatics Seminar Series. Touched on learning health systems, the Gen3 Data Commons, the NCI Genomic Data Commons, Data Harmonization, FAIR, and open science.
Healthcare use of workflow engine technology with emphasis on data analysis...Vojtech Huser
Healthcare use of workflow engine technology with emphasis on data analysis and decision support
1. Describe the abstract notion of a workflow engine and workflow technology in general
2. Understand the relationship of flowcharts (common in medical guidelines) to executable models of processes used by workflow engines
3. Understand current use of workflow engines in healthcare in production environment and in research context (phenotype modeling, data analysis, clinical decision support, process mining and discovery)
Includes description of some of my research projects
4. List the evidence for benefits and challenges of using workflow engines in healthcare
Tutorial: AMIA NOW conference: Introduction to workflow technology: Represen...Vojtech Huser
Introduction to workflow technology
Representation of healthcare processes in a workflow editor and their execution in a workflow engine
Vojtech Huser, MD PhD
Marshfield Clinic
Completeness. Modeling all relevant performance factors to provide a holistic measurement of the concept. Concision. A calculation that is as simple and straightfoward as possible, making it understandable and logical to users. Measurability. Using direct performance data rather than relying too heavily on proxies or subjective measures. And from a practical perspective, if you can’t reliably gather valid data, the exercise is futile. Independence. The components of the measure need to be independent so that variation in one component doesn’t directly drive another.
Completeness. Modeling all relevant performance factors to provide a holistic measurement of the concept. Concision. A calculation that is as simple and straightfoward as possible, making it understandable and logical to users. Measurability. Using direct performance data rather than relying too heavily on proxies or subjective measures. And from a practical perspective, if you can’t reliably gather valid data, the exercise is futile. Independence. The components of the measure need to be independent so that variation in one component doesn’t directly drive another.
Completeness. Modeling all relevant performance factors to provide a holistic measurement of the concept. Concision. A calculation that is as simple and straightfoward as possible, making it understandable and logical to users. Measurability. Using direct performance data rather than relying too heavily on proxies or subjective measures. And from a practical perspective, if you can’t reliably gather valid data, the exercise is futile. Independence. The components of the measure need to be independent so that variation in one component doesn’t directly drive another.
Completeness. Modeling all relevant performance factors to provide a holistic measurement of the concept. Concision. A calculation that is as simple and straightfoward as possible, making it understandable and logical to users. Measurability. Using direct performance data rather than relying too heavily on proxies or subjective measures. And from a practical perspective, if you can’t reliably gather valid data, the exercise is futile. Independence. The components of the measure need to be independent so that variation in one component doesn’t directly drive another.
Completeness. Modeling all relevant performance factors to provide a holistic measurement of the concept. Concision. A calculation that is as simple and straightfoward as possible, making it understandable and logical to users. Measurability. Using direct performance data rather than relying too heavily on proxies or subjective measures. And from a practical perspective, if you can’t reliably gather valid data, the exercise is futile. Independence. The components of the measure need to be independent so that variation in one component doesn’t directly drive another.