Yosemite part-4 webinar-final

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In our series on The Yosemite Project, we explore RDF as a data standard for health data. In this presentation, we will discuss with Claude Nanjo, a Software Architect at Cognitive Medical Systems, ways to expose clinical knowledge as OWL and RDF resources on the Web in order to promote greater convergence in the representation of health knowledge in the longer term. We will also explore how one might rally and coordinate the community to seed the Web with a core set of high-value resources and technologies that could greatly enhance health interoperability.

Gesundheitswesen

Towards a Web of Clinical Knowledge
Experience gained from DoDTAPS Research Project
Claude Nanjo
December 2014 SemanticWeb.com

Overview
•The healthcare challenge
•Exposing clinical knowledge on the Web as RDF
•Transforming clinical content –An example from the DoDTAPS project
•Getting closer to this vision

The Challenge
The US Healthcare System is highly heterogeneous.
How do we converge?

The Rapid Growth of Health Knowledge
Information will need to be sharable and machine-processableif it is to scale

… That is computable, Reusable, and Actionable
CaptureExposeTransformProcess & AnalyzeCHCS$ ICD9$to$SNOMED$ DX$transla4on$ NLP$parse$of$$ Med$SIG$into$ $discrete$data$ Name$parse$ into$discrete$data$ Address$parse$ into$discrete$data$ graph$ alignment$ Cluster$ Node$ Cluster$ Node$ Cluster$ Node$ Graph$ transform$&$ distribu4on$ Hadoop$ $ csv$ Clients$ ActImprove Outcomes

This Ecosystem Already Exists!
Computing

EXPERIENCE GAINED FROM DODAND VA RESEARCH PROJECTS

Our Focus Today
CaptureExposeTransformProcess & AnalyzeCHCS$ ICD9$to$SNOMED$ DX$transla4on$ NLP$parse$of$$ Med$SIG$into$ $discrete$data$ Name$parse$ into$discrete$data$ Address$parse$ into$discrete$data$ graph$ alignment$ Cluster$ Node$ Cluster$ Node$ Cluster$ Node$ Graph$ transform$&$ distribu4on$ Hadoop$ $ csv$ Clients$ ActImprove Outcomes

TAPS RDF Pipeline
CHCS
Model Transformation
ICD9 to SNOMED
DX translation
NLP parse of
Med SIG into
discrete data
Name parse
into discrete data
Address parse
into discrete data
graph
alignment
Cluster
Node
Cluster
Node
Cluster
Node
Graph Federation
Graph
transform &
distribution
Hadoop
csv
Clients
Graph Clients

RDF Pipeline Definition
For more information on the RDF Pipeline Framework, please visit:
http://rdfpipeline.org/

Deterministic Model Transformation: CHCS Address to FHIR Address

Non-Deterministic Model Transformation: From CHCS SIG to FHIR

Leveraging SPARQL for Terminology Alignment

Deterministic, Declarative, Reusable Transformation

Capture Knowledge Declaratively
CaptureExposeTransformProcess & AnalyzeCHCS$ ICD9$to$SNOMED$ DX$transla4on$ NLP$parse$of$$ Med$SIG$into$ $discrete$data$ Name$parse$ into$discrete$data$ Address$parse$ into$discrete$data$ graph$ alignment$ Cluster$ Node$ Cluster$ Node$ Cluster$ Node$ Graph$ transform$&$ distribu4on$ Hadoop$ $ csv$ Clients$ ActImprove Outcomes

Why we need standards to guide clinical capture
•RDF makes the processing of clinical information staight-forward.
–Lots of tooling
–It is a known semantic model with a number of supported representations (RDF/XML, Turtle, …)
•RDF cannot solve all problems
–Transformations are expensive even with RDF
–Hard to maintain
–Transformations are lossyand not always deterministic
•Need standards to converge on
–Standards that allows convergence towards a common clinical baseline.
–Great strides being made in the standards world
–Customization and localization increase TCO

A few encouraging developments
•The adoption of FHIR (Fast Healthcare Interoperability Resources) –an implementer-friendly resource-oriented, REST-based clinical resource framework
–http://www.hl7.org/implement/standards/fhir/summary.html
•The RDF Semantic Interoperability SWG at HL7
–Goal: To help expose HL7 standards using OWL/RDF
–Discussion on an OWL ontology for FHIR and a FHIR RDF representation
•Convergence of two existing communities
–HL7
–W3C Semantic Web for Health Care and Life Sciences Group
–http://wiki.hl7.org/index.php?title=RDF_for_Semantic_Interoperability

Expose Resources and Reusable Logic
CaptureExposeTransformProcess & AnalyzeCHCS$ ICD9$to$SNOMED$ DX$transla4on$ NLP$parse$of$$ Med$SIG$into$ $discrete$data$ Name$parse$ into$discrete$data$ Address$parse$ into$discrete$data$ graph$ alignment$ Cluster$ Node$ Cluster$ Node$ Cluster$ Node$ Graph$ transform$&$ distribu4on$ Hadoop$ $ csv$ Clients$ ActImprove Outcomes

Encouraging developments
•ONC’s Clinical Quality Framework Initiative
–The harmonization of CDS and Clinical Quality models through FHIR
–CDS ontologies?
•CDS Collaborative
–Collaboration between the University of Utah (OpenCDS) and Cognitive Medical Systems (Socratic Grid)
–Declarative rules to be exposed on the Web
–Enable access to clinical decision support services that leverage the web and RDF

Knowledge Discovery
CaptureExposeTransformProcess & AnalyzeCHCS$ ICD9$to$SNOMED$ DX$transla4on$ NLP$parse$of$$ Med$SIG$into$ $discrete$data$ Name$parse$ into$discrete$data$ Address$parse$ into$discrete$data$ graph$ alignment$ Cluster$ Node$ Cluster$ Node$ Cluster$ Node$ Graph$ transform$&$ distribu4on$ Hadoop$ $ csv$ Clients$ ActImprove Outcomes

Other prototypes from the TAPS project
•Record linkage through FHIR normalization using RDF
–CHCS and VISTA
•Patient Cohort Classification System
•PTSD Capacity Planner

Provide Support At the Point of Care
CaptureExposeTransformProcess & AnalyzeCHCS$ ICD9$to$SNOMED$ DX$transla4on$ NLP$parse$of$$ Med$SIG$into$ $discrete$data$ Name$parse$ into$discrete$data$ Address$parse$ into$discrete$data$ graph$ alignment$ Cluster$ Node$ Cluster$ Node$ Cluster$ Node$ Graph$ transform$&$ distribu4on$ Hadoop$ $ csv$ Clients$ ActImprove Outcomes

The Ask
•Help us get there, get involved in an initiative
•Engage the community
•Contribute to Open Linked Knowledge

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Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning. They need a comprehensive platform designed to address multi-faceted needs by offering multi-function data management and analytics to solve the enterprise’s most pressing data and analytic challenges in a streamlined fashion. In this research-based session, I’ll discuss what the components are in multiple modern enterprise analytics stacks (i.e., dedicated compute, storage, data integration, streaming, etc.) and focus on total cost of ownership. A complete machine learning infrastructure cost for the first modern use case at a midsize to large enterprise will be anywhere from $3 million to $22 million. Get this data point as you take the next steps on your journey into the highest spend and return item for most companies in the next several years.

Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...

DATAVERSITY

Do you ever wonder how data-driven organizations fuel analytics, improve customer experience, and accelerate business productivity? They are successful by governing and mastering data effectively so they can get trusted data to those who need it faster. Efficient data discovery, mastering and democratization is critical for swiftly linking accurate data with business consumers. When business teams can quickly and easily locate, interpret, trust, and apply data assets to support sound business judgment, it takes less time to see value. Join data mastering and data governance experts from Informatica—plus a real-world organization empowering trusted data for analytics—for a lively panel discussion. You’ll hear more about how a single cloud-native approach can help global businesses in any economy create more value—faster, more reliably, and with more confidence—by making data management and governance easier to implement.

Data at the Speed of Business with Data Mastering and Governance

DATAVERSITY

What is data literacy? Which organizations, and which workers in those organizations, need to be data-literate? There are seemingly hundreds of definitions of data literacy, along with almost as many opinions about how to achieve it. In a broader perspective, companies must consider whether data literacy is an isolated goal or one component of a broader learning strategy to address skill deficits. How does data literacy compare to other types of skills or “literacy” such as business acumen? This session will position data literacy in the context of other worker skills as a framework for understanding how and where it fits and how to advocate for its importance.

Exploring Levels of Data Literacy

DATAVERSITY

Developing a Data Strategy for your organization can seem like a daunting task – but it’s worth the effort. Getting your Data Strategy right can provide significant value, as data drives many of the key initiatives in today’s marketplace – from digital transformation, to marketing, to customer centricity, to population health, and more. This webinar will help demystify Data Strategy and its relationship to Data Architecture and will provide concrete, practical ways to get started.

Building a Data Strategy – Practical Steps for Aligning with Business Goals

DATAVERSITY

Uncover how your business can save money and find new revenue streams. Driving profitability is a top priority for companies globally, especially in uncertain economic times. It's imperative that companies reimagine growth strategies and improve process efficiencies to help cut costs and drive revenue – but how? By leveraging data-driven strategies layered with artificial intelligence, companies can achieve untapped potential and help their businesses save money and drive profitability. In this webinar, you'll learn: - How your company can leverage data and AI to reduce spending and costs - Ways you can monetize data and AI and uncover new growth strategies - How different companies have implemented these strategies to achieve cost optimization benefits

Make Data Work for You

DATAVERSITY

Organizations with governed metadata made available through their data catalog can answer questions their people have about the organization’s data. These organizations get more value from their data, protect their data better, gain improved ROI from data-centric projects and programs, and have more confidence in their most strategic data. Join Bob Seiner for this lively webinar where he will talk about the value of a data catalog and how to build the use of the catalog into your stewards’ daily routines. Bob will share how the tool must be positioned for success and viewed as a must-have resource that is a steppingstone and catalyst to governed data across the organization.

Data Catalogs Are the Answer – What is the Question?

DATAVERSITY

Data Catalogs Are the Answer – What Is the Question?

DATAVERSITY

Because every organization produces and propagates data as part of their day-to-day operations, data trends are becoming more and more important in the mainstream business world’s consciousness. For many organizations in various industries, though, comprehension of this development begins and ends with buzzwords: “Big Data,” “NoSQL,” “Data Scientist,” and so on. Few realize that all solutions to their business problems, regardless of platform or relevant technology, rely to a critical extent on the data model supporting them. As such, data modeling is not an optional task for an organization’s data effort, but rather a vital activity that facilitates the solutions driving your business. Since quality engineering/architecture work products do not happen accidentally, the more your organization depends on automation, the more important the data models driving the engineering and architecture activities of your organization. This webinar illustrates data modeling as a key activity upon which so much technology and business investment depends. Specific learning objectives include: - Understanding what types of challenges require data modeling to be part of the solution - How automation requires standardization on derivable via data modeling techniques - Why only a working partnership between data and the business can produce useful outcomes

Data Modeling Fundamentals

DATAVERSITY

Analytics play a critical role in supporting strategic business initiatives. Despite the obvious value to analytic professionals of providing the analytics for these initiatives, many executives question the economic return of analytics as well as data lakes, machine learning, master data management, and the like. Technology professionals need to calculate and present business value in terms business executives can understand. Unfortunately, most IT professionals lack the knowledge required to develop comprehensive cost-benefit analyses and return on investment (ROI) measurements. This session provides a framework to help technology professionals research, measure, and present the economic value of a proposed or existing analytics initiative, no matter the form that the business benefit arises. The session will provide practical advice about how to calculate ROI and the formulas, and how to collect the necessary information.

Showing ROI for Your Analytic Project

DATAVERSITY

Data Mesh is a trending approach to building a decentralized data architecture by leveraging a domain-oriented, self-service design. However, the pure definition of Data Mesh lacks a center of excellence or central data team and doesn’t address the need for a common approach for sharing data products across teams. The semantic layer is emerging as a key component to supporting a Hub and Spoke style of organizing data teams by introducing data model sharing, collaboration, and distributed ownership controls. This session will explain how data teams can define common models and definitions with a semantic layer to decentralize analytics product creation using a Hub and Spoke architecture. Attend this session to learn about: - The role of a Data Mesh in the modern cloud architecture. - How a semantic layer can serve as the binding agent to support decentralization. - How to drive self service with consistency and control.

How a Semantic Layer Makes Data Mesh Work at Scale

DATAVERSITY

Enterprise data literacy. A worthy objective? Certainly! A realistic goal? That remains to be seen. As companies consider investing in data literacy education, questions arise about its value and purpose. While the destination – having a data-fluent workforce – is attractive, we wonder how (and if) we can get there. Kicking off this webinar series, we begin with a panel discussion to explore the landscape of literacy, including expert positions and results from focus groups: - why it matters, - what it means, - what gets in the way, - who needs it (and how much they need), - what companies believe it will accomplish. In this engaging discussion about literacy, we will set the stage for future webinars to answer specific questions and feature successful literacy efforts.

Is Enterprise Data Literacy Possible?

DATAVERSITY

Change is hard, especially in response to negative stimuli or what is perceived as negative stimuli. So organizations need to reframe how they think about data privacy, security and governance, treating them as value centers to 1) ensure enterprise data can flow where it needs to, 2) prevent – not just react – to internal and external threats, and 3) comply with data privacy and security regulations. Working together, these roles can accelerate faster access to approved, relevant and higher quality data – and that means more successful use cases, faster speed to insights, and better business outcomes. However, both new information and tools are required to make the shift from defense to offense, reducing data drama while increasing its value. Join us for this panel discussion with experts in these fields as they discuss: - Recent research about where data privacy, security and governance stand - The most valuable enterprise data use cases - The common obstacles to data value creation - New approaches to data privacy, security and governance - Their advice on how to shift from a reactive to resilient mindset/culture/organization You’ll be educated, entertained and inspired by this panel and their expertise in using the data trifecta to innovate more often, operate more efficiently, and differentiate more strategically.

The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...

DATAVERSITY

Emerging Trends in Data Architecture – What’s the Next Big Thing?

DATAVERSITY

As DATAVERSITY’s RWDG series hurdles into our 12th year, this webinar takes a quick look behind us, evaluates the present, and predicts the future of Data Governance. Based on webinar numbers, hot Data Governance topics have evolved over the years from policies and best practices, roles and tools, data catalogs and frameworks, to supporting data mesh and fabric, artificial intelligence, virtualization, literacy, and metadata governance. Join Bob Seiner as he reflects on the past and what has and has not worked, while sharing examples of enterprise successes and struggles. In this webinar, Bob will challenge the audience to stay a step ahead by learning from the past and blazing a new trail into the future of Data Governance. In this webinar, Bob will focus on: - Data Governance’s past, present, and future - How trials and tribulations evolve to success - Leveraging lessons learned to improve productivity - The great Data Governance tool explosion - The future of Data Governance

Data Governance Trends - A Look Backwards and Forwards

DATAVERSITY

Would you share your bank account information on social media? How about shouting your social security number on the New York City subway? We didn’t think so either – that’s why data governance is consistently top of mind. In this webinar, we’ll discuss the common Cloud data governance best practices – and how to apply them today. Join us to uncover Google Cloud’s investment in data governance and learn practical and doable methods around key management and confidential computing. Hear real customer experiences and leave with insights that you can share with your team. Let’s get solving. Topics that you will hear addressed in this webinar: - Understanding the basics of Cloud Incident Response (IR) and anticipated data governance trends - Best practices for key management and apply data governance to your day-to-day - The next wave of Confidential Computing and how to get started, including a demo

Data Governance Trends and Best Practices To Implement Today

DATAVERSITY

It is a fascinating, explosive time for enterprise analytics. It is from the position of analytics leadership that the enterprise mission will be executed and company leadership will emerge. The data professional is absolutely sitting on the performance of the company in this information economy and has an obligation to demonstrate the possibilities and originate the architecture, data, and projects that will deliver analytics. After all, no matter what business you’re in, you’re in the business of analytics. The coming years will be full of big changes in enterprise analytics and data architecture. William will kick off the fifth year of the Advanced Analytics series with a discussion of the trends winning organizations should build into their plans, expectations, vision, and awareness now.

2023 Trends in Enterprise Analytics

DATAVERSITY

Too often I hear the question “Can you help me with our data strategy?” Unfortunately, for most, this is the wrong request because it focuses on the least valuable component: the data strategy itself. A more useful request is: “Can you help me apply data strategically?” Yes, at early maturity phases the process of developing strategic thinking about data is more important than the actual product! Trying to write a good (must less perfect) data strategy on the first attempt is generally not productive –particularly given the widespread acceptance of Mike Tyson’s truism: “Everybody has a plan until they get punched in the face.” This program refocuses efforts on learning how to iteratively improve the way data is strategically applied. This will permit data-based strategy components to keep up with agile, evolving organizational strategies. It also contributes to three primary organizational data goals. Learn how to improve the following: - Your organization’s data - The way your people use data - The way your people use data to achieve your organizational strategy This will help in ways never imagined. Data are your sole non-depletable, non-degradable, durable strategic assets, and they are pervasively shared across every organizational area. Addressing existing challenges programmatically includes overcoming necessary but insufficient prerequisites and developing a disciplined, repeatable means of improving business objectives. This process (based on the theory of constraints) is where the strategic data work really occurs as organizations identify prioritized areas where better assets, literacy, and support (data strategy components) can help an organization better achieve specific strategic objectives. Then the process becomes lather, rinse, and repeat. Several complementary concepts are also covered, including: - A cohesive argument for why data strategy is necessary for effective data governance - An overview of prerequisites for effective strategic use of data strategy, as well as common pitfalls - A repeatable process for identifying and removing data constraints - The importance of balancing business operation and innovation

Data Strategy Best Practices

DATAVERSITY

The question is asked all the time: “What part of the organization should own your Data Governance program?” The typical answers are “the business” and “IT (information technology).” Another answer to that question is “Yes.” The program must be owned and reside somewhere in the organization. You may ask yourself if there is a correct answer to the question. Join this new RWDG webinar with Bob Seiner where Bob will answer the question that is the title of this webinar. Determining ownership of Data Governance is a vital first step. Figuring out the appropriate part of the organization to manage the program is an important second step. This webinar will help you address these questions and more. In this session Bob will share: - What is meant by “the business” when it comes to owning Data Governance - Why some people say that Data Governance in IT is destined to fail - Examples of IT positioned Data Governance success - Considerations for answering the question in your organization - The final answer to the question of who should own Data Governance

Who Should Own Data Governance – IT or Business?

DATAVERSITY

It is clear that Data Management best practices exist and so does a useful process for improving existing Data Management practices. The question arises: Since we understand the goal, how does one design a process for Data Management goal achievement? This program describes what must be done at the programmatic level to achieve better data use and a way to implement this as part of your data program. The approach combines DMBoK content and CMMI/DMM processes – permitting organizations with the opportunity to benefit from the best of both. It also permits organizations to understand: - Their current Data Management practices - Strengths that should be leveraged - Remediation opportunities

Data Management Best Practices

DATAVERSITY

MLOps is a practice for collaboration between Data Science and operations to manage the production machine learning (ML) lifecycles. As an amalgamation of “machine learning” and “operations,” MLOps applies DevOps principles to ML delivery, enabling the delivery of ML-based innovation at scale to result in: Faster time to market of ML-based solutions More rapid rate of experimentation, driving innovation Assurance of quality, trustworthiness, and ethical AI MLOps is essential for scaling ML. Without it, enterprises risk struggling with costly overhead and stalled progress. Several vendors have emerged with offerings to support MLOps: the major offerings are Microsoft Azure ML and Google Vertex AI. We looked at these offerings from the perspective of enterprise features and time-to-value.

MLOps – Applying DevOps to Competitive Advantage

DATAVERSITY

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Architecture, Products, and Total Cost of Ownership of the Leading Machine Le...

Data at the Speed of Business with Data Mastering and Governance

Exploring Levels of Data Literacy

Building a Data Strategy – Practical Steps for Aligning with Business Goals

Make Data Work for You

Data Catalogs Are the Answer – What is the Question?

Data Catalogs Are the Answer – What Is the Question?

Data Modeling Fundamentals

Showing ROI for Your Analytic Project

How a Semantic Layer Makes Data Mesh Work at Scale

Is Enterprise Data Literacy Possible?

The Data Trifecta – Privacy, Security & Governance Race from Reactivity to Re...

Emerging Trends in Data Architecture – What’s the Next Big Thing?

Data Governance Trends - A Look Backwards and Forwards

Data Governance Trends and Best Practices To Implement Today

2023 Trends in Enterprise Analytics

Data Strategy Best Practices

Who Should Own Data Governance – IT or Business?

Data Management Best Practices

MLOps – Applying DevOps to Competitive Advantage

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