Mapping of Terminology Standards, a Way for Interoperability (Position Paper)Sven Van Laere
Standards in medicine are essential to enable communication between healthcare providers. These standards can be used either for exchanging information, or for coding and documenting the health status of a patient. In this position paper we focus on the latter, namely terminology standards. However, the multidisciplinary field of medicine makes use of many different standards. We propose to invest in an interoperable electronic health record (EHR) that can be understood by all different levels of health care providers independent of the kind of terminology standard they use. To make this record interoperable, we suggest mapping standards in order to make uniform communication possible. We suggest using mappings between a reference
terminology (RT) and other terminology standards. By using this approach we limit the number of mappings that have to be provided. The Systematized Nomenclature of Medicine, Clinical Terms (SNOMED CT) can be used as a RT, because of its extensive character and the preserved semantics towards other terminology standards. Moreover, a lot of mappings from SNOMED CT to other standards are already defined previously.
ICIC 2014 What Can We Learn from Our Past, that Equips Us for the Future? Dr. Haxel Consult
This talk describes lessons learned from a 30-year career in Chemical Information Science, key influences and motivations, and signposts to the future for what we may expect. Work has changed from implicit knowledge of where to find information in books, through data stores, to the internet, and beyond to unimagined futures. The talk references the rise and fall of the UK pharmaceutical industry as a place to work, in line with changes to the provision of information to scientists. The nature of work is described in relation to the 2nd law of thermodynamics, and hopefully provides hope for the future of chemical information for the next generation.
The Open PHACTS project delivers an online platform integrating a wide variety of data from across chemistry and the life sciences and an ecosystem of tools and services to query this data in support of pharmacological research, turning the semantic web from a research project into something that can be used by practising medicinal chemists in both academia and industry. In the summer of 2015 it was the first winner of the European Linked Data Award. At the Royal Society of Chemistry we have provided the chemical underpinnings to this system and in this talk we review its development over the past five years. We cover both our early work on semantic modelling of chemistry data for the Open PHACTS triplestore and more recent work building an all-purpose data platform, for which the Open PHACTS data has been an important test case, what has worked well, what's missing and where this is is likely to go in future.
Making Data FAIR (Findable, Accessible, Interoperable, Reusable)Tom Plasterer
What to do About FAIR…
In the experience of most pharma professionals, FAIR remains fairly abstract, bordering on inconclusive. This session will outline specific case studies – real problems with real data, and address opportunities and real concerns.
·
Why making data Findable, Actionable, Interoperable and Reusable is important.
Talk presented at the Data Driven Drug Development (D4) conference on March 20th, 2019.
Mapping of Terminology Standards, a Way for Interoperability (Position Paper)Sven Van Laere
Standards in medicine are essential to enable communication between healthcare providers. These standards can be used either for exchanging information, or for coding and documenting the health status of a patient. In this position paper we focus on the latter, namely terminology standards. However, the multidisciplinary field of medicine makes use of many different standards. We propose to invest in an interoperable electronic health record (EHR) that can be understood by all different levels of health care providers independent of the kind of terminology standard they use. To make this record interoperable, we suggest mapping standards in order to make uniform communication possible. We suggest using mappings between a reference
terminology (RT) and other terminology standards. By using this approach we limit the number of mappings that have to be provided. The Systematized Nomenclature of Medicine, Clinical Terms (SNOMED CT) can be used as a RT, because of its extensive character and the preserved semantics towards other terminology standards. Moreover, a lot of mappings from SNOMED CT to other standards are already defined previously.
ICIC 2014 What Can We Learn from Our Past, that Equips Us for the Future? Dr. Haxel Consult
This talk describes lessons learned from a 30-year career in Chemical Information Science, key influences and motivations, and signposts to the future for what we may expect. Work has changed from implicit knowledge of where to find information in books, through data stores, to the internet, and beyond to unimagined futures. The talk references the rise and fall of the UK pharmaceutical industry as a place to work, in line with changes to the provision of information to scientists. The nature of work is described in relation to the 2nd law of thermodynamics, and hopefully provides hope for the future of chemical information for the next generation.
The Open PHACTS project delivers an online platform integrating a wide variety of data from across chemistry and the life sciences and an ecosystem of tools and services to query this data in support of pharmacological research, turning the semantic web from a research project into something that can be used by practising medicinal chemists in both academia and industry. In the summer of 2015 it was the first winner of the European Linked Data Award. At the Royal Society of Chemistry we have provided the chemical underpinnings to this system and in this talk we review its development over the past five years. We cover both our early work on semantic modelling of chemistry data for the Open PHACTS triplestore and more recent work building an all-purpose data platform, for which the Open PHACTS data has been an important test case, what has worked well, what's missing and where this is is likely to go in future.
Making Data FAIR (Findable, Accessible, Interoperable, Reusable)Tom Plasterer
What to do About FAIR…
In the experience of most pharma professionals, FAIR remains fairly abstract, bordering on inconclusive. This session will outline specific case studies – real problems with real data, and address opportunities and real concerns.
·
Why making data Findable, Actionable, Interoperable and Reusable is important.
Talk presented at the Data Driven Drug Development (D4) conference on March 20th, 2019.
Started in 2004 (under ASTM Committee E13.15) the Analytical Information Markup Language (AnIML) is an XML based standard for capturing, sharing, viewing, and archiving analytical instrument data from any analytical technique.
This paper discusses the AnIML standard in terms of philosophy, structure, usage, and the resources available to work with the standard. Examples will be given for different techniques as well as strategies for migration of legacy data. Finally, the current status of the standard and time frame for promulgation through ASTM will be reported.
OpenTox - an open community and framework supporting predictive toxicology an...Barry Hardy
Presented at ACS Boston 2015 at a Session on the growing impact of Open Science chaired by Andy Lang and Tony Williams dedicated to the work, memory and legacy of JC Bradley and the work we carry forward!
One important goal of OpenTox is to support the development of an Open Standards-based predictive toxicology framework that provides a unified access to toxicological data and models. OpenTox supports the development of tools for the integration of data, for the generation and validation of in silico models for toxic effects, libraries for the development and integration of modelling algorithms, and scientifically sound validation and reporting routines.
The OpenTox Application Programming Interface (API) is an important open standards development for software development purposes. It provides a specification against which development of global interoperable toxicology resources by the broader community can be carried out. The use of OpenTox API-compliant web services to communicate instructions between linked resources with URI addresses supports the use of a wide variety of commands to carry out operations such as data integration, algorithm use, model building and validation. The OpenTox Framework currently includes, with its APIs, services for compounds, datasets, features, algorithms, models, ontologies, tasks, validation, reporting, investigations, studies, assays, and authentication and authorisation, which may be combined into multiple applications satisfying a variety of different user needs. As OpenTox creates a semantic web for toxicology, it should be an ideal framework for incorporating toxicology data, ontology and modelling developments, thus supporting both a mechanistic framework for toxicology and best practices in statistical analysis and computational modelling.
In this presentation I will review the recent OpenTox-based development of applications including the ToxBank data infrastructure supporting integrated analysis across biochemical, functional and omics datasets supporting the safety assessment goals of the SEURAT-1 program which aims to develop alternatives to animal testing.
Finally, I will provide an overview of the working group activities of the newly formed OpenTox Association which aim to progress the development of open source, data, standards and tools in this area.
The European Open Science Cloud: just what is it?Carole Goble
Presented at Jisc and CNI leaders conference 2018, 2 July 2018, Oxford, UK (https://www.jisc.ac.uk/events/jisc-and-cni-leaders-conference-02-jul-2018). The European Open Science Cloud. What exactly is it? In principle it is conceived as a virtual environment with open and seamless services for storage, management, analysis and re-use of research data, across borders and scientific disciplines. How? By federating existing scientific data infrastructures, currently dispersed across disciplines and Member States. In practice, what it is depends on the stakeholder. To European Research Infrastructures it’s a coordinated mission to organise and exchange their data, metadata, software and services to be FAIR – Findable, Accessible, Interoperable, Reusable – and to use e-Infrastructures, either EU or commercial. To EU e-Infrastructures offering data storage and cloud services, it’s a funding mission to integrate their services, policies and organisational structures, and to be used by the Research Infrastructures. To agencies it’s a means to promote Open Science, standardisation, cross-disciplinary research and coordinated investment with a dream of a “one stop shop” for researchers. And for Libraries?
FAIR data and model management for systems biology.FAIRDOM
Written and presented by Carole Goble (University of Manchester) as part of Intelligent Systems for Molecular Biology (ISMB), Dublin. July 10th - 14th 2015.
Reproducible and citable data and models: an introduction.FAIRDOM
Prepared and presented by Carole Goble (University of Manchester), Wolfgang Mueller (HITS), Dagmar Waltermath (University of Rostock), at the Reproducible and Citable Data and Models Workshop, Warnemünde, Germany. September 14th - 16th 2015.
How are we Faring with FAIR? (and what FAIR is not)Carole Goble
Keynote presented at the workshop FAIRe Data Infrastructures, 15 October 2020
https://www.gmds.de/aktivitaeten/medizinische-informatik/projektgruppenseiten/faire-dateninfrastrukturen-fuer-die-biomedizinische-informatik/workshop-2020/
Remarkably it was only in 2016 that the ‘FAIR Guiding Principles for scientific data management and stewardship’ appeared in Scientific Data. The paper was intended to launch a dialogue within the research and policy communities: to start a journey to wider accessibility and reusability of data and prepare for automation-readiness by supporting findability, accessibility, interoperability and reusability for machines. Many of the authors (including myself) came from biomedical and associated communities. The paper succeeded in its aim, at least at the policy, enterprise and professional data infrastructure level. Whether FAIR has impacted the researcher at the bench or bedside is open to doubt. It certainly inspired a great deal of activity, many projects, a lot of positioning of interests and raised awareness. COVID has injected impetus and urgency to the FAIR cause (good) and also highlighted its politicisation (not so good).
In this talk I’ll make some personal reflections on how we are faring with FAIR: as one of the original principles authors; as a participant in many current FAIR initiatives (particularly in the biomedical sector and for research objects other than data) and as a veteran of FAIR before we had the principles.
Short talk on Research Object and their use for reproducibility and publishing in the Systems Biology Commons Platform FAIRDOMHub, and the underlying software SEEK.
FAIRy stories: tales from building the FAIR Research CommonsCarole Goble
Plenary Lecture Presented at INCF Neuroinformatics 2019 https://www.neuroinformatics2019.org
Title: FAIRy stories: tales from building the FAIR Research Commons
Findable Accessable Interoperable Reusable. The “FAIR Principles” for research data, software, computational workflows, scripts, or any kind of Research Object is a mantra; a method; a meme; a myth; a mystery. For the past 15 years I have been working on FAIR in a range of projects and initiatives in the Life Sciences as we try to build the FAIR Research Commons. Some are top-down like the European Research Infrastructures ELIXIR, ISBE and IBISBA, and the NIH Data Commons. Some are bottom-up, supporting FAIR for investigator-led projects (FAIRDOM), biodiversity analytics (BioVel), and FAIR drug discovery (Open PHACTS, FAIRplus). Some have become movements, like Bioschemas, the Common Workflow Language and Research Objects. Others focus on cross-cutting approaches in reproducibility, computational workflows, metadata representation and scholarly sharing & publication. In this talk I will relate a series of FAIRy tales. Some of them are Grimm. There are villains and heroes. Some have happy endings; all have morals.
COMBINE 2019, EU-STANDS4PM, Heidelberg, Germany 18 July 2019
FAIR: Findable Accessable Interoperable Reusable. The “FAIR Principles” for research data, software, computational workflows, scripts, or any other kind of Research Object one can think of, is now a mantra; a method; a meme; a myth; a mystery. FAIR is about supporting and tracking the flow and availability of data across research organisations and the portability and sustainability of processing methods to enable transparent and reproducible results. All this is within the context of a bottom up society of collaborating (or burdened?) scientists, a top down collective of compliance-focused funders and policy makers and an in-the-middle posse of e-infrastructure providers.
Making the FAIR principles a reality is tricky. They are aspirations not standards. They are multi-dimensional and dependent on context such as the sensitivity and availability of the data and methods. We already see a jungle of projects, initiatives and programmes wrestling with the challenges. FAIR efforts have particularly focused on the “last mile” – “FAIRifying” destination community archive repositories and measuring their “compliance” to FAIR metrics (or less controversially “indicators”). But what about FAIR at the first mile, at source and how do we help Alice and Bob with their (secure) data management? If we tackle the FAIR first and last mile, what about the FAIR middle? What about FAIR beyond just data – like exchanging and reusing pipelines for precision medicine?
Since 2008 the FAIRDOM collaboration [1] has worked on FAIR asset management and the development of a FAIR asset Commons for multi-partner researcher projects [2], initially in the Systems Biology field. Since 2016 we have been working with the BioCompute Object Partnership [3] on standardising computational records of HTS precision medicine pipelines.
So, using our FAIRDOM and BioCompute Object binoculars let’s go on a FAIR safari! Let’s peruse the ecosystem, observe the different herds and reflect what where we are for FAIR personalised medicine.
References
[1] http://www.fair-dom.org
[2] http://www.fairdomhub.org
[3] http://www.biocomputeobject.org
Presentation on the Chemical Analysis Metadata Platform (ChAMP) as a new project to characterize and organize metadata about chemical analysis methods. The project will develop an ontology, controlled vocabularies, and design rules
Open Science: how to serve the needs of the researcher? Carole Goble
Open science Jisc CNI roundtable 2018
Lightning talk
What should the future look like?
What are the essential characteristics we desire in a relatively near future system to support scholarly communication across the full research life cycle?
What are the key areas requiring attention, action, or investment today to reach the future that we want to reach?
What are the best opportunities to build upon existing practices, investments and infrastructure, both
open and commercially provided?
Where must alternatives be developed?
What areas are already on good trajectories and can be left to evolve without additional intervention
Stay up-to-date on the latest news, research, and resources. This month's edition covers 2024 predictions across the HPC and AI industry, NSF's National Artificial Intelligence Research Resource (NAIRR) pilot, the role of compilers in scientific computing, on-demand and upcoming webinars, and more!
During the last two decades Clinical Decision Support (CDS) standards and technologies have progressed significantly to develop them as more robust and scalable systems. However, the current context of medicine sets high demands in aspects such as interoperability to enable the use of EHR data in CDS systems, the need to establish communication challenges to include the patient as an active component in decision making, collaborative learning and sharing CDS systems across institutional borders, to name a few.
In this thesis I tackle some of these challenges. In particular, I evolve previous conceptual computerized decision support frameworks and I postulate a CDS systems environment where different models interact to enable:
• Secondary use of data for CDS systems: The dissertation presents a model to leverage different developments in data access and standardization of medical information. The result is an openEHR-based Data Warehouse architecture that enables access, standardization and abstraction of clinical data for CDS systems. The architecture allows: a) to access heterogeneous data sources; b) to standardize data into openEHR to grant interoperability of data; and c) to exploit an openEHR repository as a Data Warehouse that allows querying data in a technology-independent format (the Archetype Query Language).
• CDS systems semantic specification: The semantic model proposed exploits the paradigm of Linked Services to unambiguously describe CDS systems in a machine- understandable fashion. This grants ontological descriptions of functional, non- functional and data semantics. These descriptions facilitate to overcome some of the barriers in CDS functionality sharing. In particular, the semantic model proposed allows using expressive queries to discover CDS services in health
III
networks, and analyzing CDS systems interfaces to understand how to interoperate with
them.
• Effective patient-CDS systems interaction: the dissertation proposes a method to
evaluate the communication process between patients and consumer-oriented CDS systems. The method aims for detecting if important human-computer interaction barriers that could lead to negative outcomes are present in CDS systems user interfaces.
Started in 2004 (under ASTM Committee E13.15) the Analytical Information Markup Language (AnIML) is an XML based standard for capturing, sharing, viewing, and archiving analytical instrument data from any analytical technique.
This paper discusses the AnIML standard in terms of philosophy, structure, usage, and the resources available to work with the standard. Examples will be given for different techniques as well as strategies for migration of legacy data. Finally, the current status of the standard and time frame for promulgation through ASTM will be reported.
OpenTox - an open community and framework supporting predictive toxicology an...Barry Hardy
Presented at ACS Boston 2015 at a Session on the growing impact of Open Science chaired by Andy Lang and Tony Williams dedicated to the work, memory and legacy of JC Bradley and the work we carry forward!
One important goal of OpenTox is to support the development of an Open Standards-based predictive toxicology framework that provides a unified access to toxicological data and models. OpenTox supports the development of tools for the integration of data, for the generation and validation of in silico models for toxic effects, libraries for the development and integration of modelling algorithms, and scientifically sound validation and reporting routines.
The OpenTox Application Programming Interface (API) is an important open standards development for software development purposes. It provides a specification against which development of global interoperable toxicology resources by the broader community can be carried out. The use of OpenTox API-compliant web services to communicate instructions between linked resources with URI addresses supports the use of a wide variety of commands to carry out operations such as data integration, algorithm use, model building and validation. The OpenTox Framework currently includes, with its APIs, services for compounds, datasets, features, algorithms, models, ontologies, tasks, validation, reporting, investigations, studies, assays, and authentication and authorisation, which may be combined into multiple applications satisfying a variety of different user needs. As OpenTox creates a semantic web for toxicology, it should be an ideal framework for incorporating toxicology data, ontology and modelling developments, thus supporting both a mechanistic framework for toxicology and best practices in statistical analysis and computational modelling.
In this presentation I will review the recent OpenTox-based development of applications including the ToxBank data infrastructure supporting integrated analysis across biochemical, functional and omics datasets supporting the safety assessment goals of the SEURAT-1 program which aims to develop alternatives to animal testing.
Finally, I will provide an overview of the working group activities of the newly formed OpenTox Association which aim to progress the development of open source, data, standards and tools in this area.
The European Open Science Cloud: just what is it?Carole Goble
Presented at Jisc and CNI leaders conference 2018, 2 July 2018, Oxford, UK (https://www.jisc.ac.uk/events/jisc-and-cni-leaders-conference-02-jul-2018). The European Open Science Cloud. What exactly is it? In principle it is conceived as a virtual environment with open and seamless services for storage, management, analysis and re-use of research data, across borders and scientific disciplines. How? By federating existing scientific data infrastructures, currently dispersed across disciplines and Member States. In practice, what it is depends on the stakeholder. To European Research Infrastructures it’s a coordinated mission to organise and exchange their data, metadata, software and services to be FAIR – Findable, Accessible, Interoperable, Reusable – and to use e-Infrastructures, either EU or commercial. To EU e-Infrastructures offering data storage and cloud services, it’s a funding mission to integrate their services, policies and organisational structures, and to be used by the Research Infrastructures. To agencies it’s a means to promote Open Science, standardisation, cross-disciplinary research and coordinated investment with a dream of a “one stop shop” for researchers. And for Libraries?
FAIR data and model management for systems biology.FAIRDOM
Written and presented by Carole Goble (University of Manchester) as part of Intelligent Systems for Molecular Biology (ISMB), Dublin. July 10th - 14th 2015.
Reproducible and citable data and models: an introduction.FAIRDOM
Prepared and presented by Carole Goble (University of Manchester), Wolfgang Mueller (HITS), Dagmar Waltermath (University of Rostock), at the Reproducible and Citable Data and Models Workshop, Warnemünde, Germany. September 14th - 16th 2015.
How are we Faring with FAIR? (and what FAIR is not)Carole Goble
Keynote presented at the workshop FAIRe Data Infrastructures, 15 October 2020
https://www.gmds.de/aktivitaeten/medizinische-informatik/projektgruppenseiten/faire-dateninfrastrukturen-fuer-die-biomedizinische-informatik/workshop-2020/
Remarkably it was only in 2016 that the ‘FAIR Guiding Principles for scientific data management and stewardship’ appeared in Scientific Data. The paper was intended to launch a dialogue within the research and policy communities: to start a journey to wider accessibility and reusability of data and prepare for automation-readiness by supporting findability, accessibility, interoperability and reusability for machines. Many of the authors (including myself) came from biomedical and associated communities. The paper succeeded in its aim, at least at the policy, enterprise and professional data infrastructure level. Whether FAIR has impacted the researcher at the bench or bedside is open to doubt. It certainly inspired a great deal of activity, many projects, a lot of positioning of interests and raised awareness. COVID has injected impetus and urgency to the FAIR cause (good) and also highlighted its politicisation (not so good).
In this talk I’ll make some personal reflections on how we are faring with FAIR: as one of the original principles authors; as a participant in many current FAIR initiatives (particularly in the biomedical sector and for research objects other than data) and as a veteran of FAIR before we had the principles.
Short talk on Research Object and their use for reproducibility and publishing in the Systems Biology Commons Platform FAIRDOMHub, and the underlying software SEEK.
FAIRy stories: tales from building the FAIR Research CommonsCarole Goble
Plenary Lecture Presented at INCF Neuroinformatics 2019 https://www.neuroinformatics2019.org
Title: FAIRy stories: tales from building the FAIR Research Commons
Findable Accessable Interoperable Reusable. The “FAIR Principles” for research data, software, computational workflows, scripts, or any kind of Research Object is a mantra; a method; a meme; a myth; a mystery. For the past 15 years I have been working on FAIR in a range of projects and initiatives in the Life Sciences as we try to build the FAIR Research Commons. Some are top-down like the European Research Infrastructures ELIXIR, ISBE and IBISBA, and the NIH Data Commons. Some are bottom-up, supporting FAIR for investigator-led projects (FAIRDOM), biodiversity analytics (BioVel), and FAIR drug discovery (Open PHACTS, FAIRplus). Some have become movements, like Bioschemas, the Common Workflow Language and Research Objects. Others focus on cross-cutting approaches in reproducibility, computational workflows, metadata representation and scholarly sharing & publication. In this talk I will relate a series of FAIRy tales. Some of them are Grimm. There are villains and heroes. Some have happy endings; all have morals.
COMBINE 2019, EU-STANDS4PM, Heidelberg, Germany 18 July 2019
FAIR: Findable Accessable Interoperable Reusable. The “FAIR Principles” for research data, software, computational workflows, scripts, or any other kind of Research Object one can think of, is now a mantra; a method; a meme; a myth; a mystery. FAIR is about supporting and tracking the flow and availability of data across research organisations and the portability and sustainability of processing methods to enable transparent and reproducible results. All this is within the context of a bottom up society of collaborating (or burdened?) scientists, a top down collective of compliance-focused funders and policy makers and an in-the-middle posse of e-infrastructure providers.
Making the FAIR principles a reality is tricky. They are aspirations not standards. They are multi-dimensional and dependent on context such as the sensitivity and availability of the data and methods. We already see a jungle of projects, initiatives and programmes wrestling with the challenges. FAIR efforts have particularly focused on the “last mile” – “FAIRifying” destination community archive repositories and measuring their “compliance” to FAIR metrics (or less controversially “indicators”). But what about FAIR at the first mile, at source and how do we help Alice and Bob with their (secure) data management? If we tackle the FAIR first and last mile, what about the FAIR middle? What about FAIR beyond just data – like exchanging and reusing pipelines for precision medicine?
Since 2008 the FAIRDOM collaboration [1] has worked on FAIR asset management and the development of a FAIR asset Commons for multi-partner researcher projects [2], initially in the Systems Biology field. Since 2016 we have been working with the BioCompute Object Partnership [3] on standardising computational records of HTS precision medicine pipelines.
So, using our FAIRDOM and BioCompute Object binoculars let’s go on a FAIR safari! Let’s peruse the ecosystem, observe the different herds and reflect what where we are for FAIR personalised medicine.
References
[1] http://www.fair-dom.org
[2] http://www.fairdomhub.org
[3] http://www.biocomputeobject.org
Presentation on the Chemical Analysis Metadata Platform (ChAMP) as a new project to characterize and organize metadata about chemical analysis methods. The project will develop an ontology, controlled vocabularies, and design rules
Open Science: how to serve the needs of the researcher? Carole Goble
Open science Jisc CNI roundtable 2018
Lightning talk
What should the future look like?
What are the essential characteristics we desire in a relatively near future system to support scholarly communication across the full research life cycle?
What are the key areas requiring attention, action, or investment today to reach the future that we want to reach?
What are the best opportunities to build upon existing practices, investments and infrastructure, both
open and commercially provided?
Where must alternatives be developed?
What areas are already on good trajectories and can be left to evolve without additional intervention
Stay up-to-date on the latest news, research, and resources. This month's edition covers 2024 predictions across the HPC and AI industry, NSF's National Artificial Intelligence Research Resource (NAIRR) pilot, the role of compilers in scientific computing, on-demand and upcoming webinars, and more!
During the last two decades Clinical Decision Support (CDS) standards and technologies have progressed significantly to develop them as more robust and scalable systems. However, the current context of medicine sets high demands in aspects such as interoperability to enable the use of EHR data in CDS systems, the need to establish communication challenges to include the patient as an active component in decision making, collaborative learning and sharing CDS systems across institutional borders, to name a few.
In this thesis I tackle some of these challenges. In particular, I evolve previous conceptual computerized decision support frameworks and I postulate a CDS systems environment where different models interact to enable:
• Secondary use of data for CDS systems: The dissertation presents a model to leverage different developments in data access and standardization of medical information. The result is an openEHR-based Data Warehouse architecture that enables access, standardization and abstraction of clinical data for CDS systems. The architecture allows: a) to access heterogeneous data sources; b) to standardize data into openEHR to grant interoperability of data; and c) to exploit an openEHR repository as a Data Warehouse that allows querying data in a technology-independent format (the Archetype Query Language).
• CDS systems semantic specification: The semantic model proposed exploits the paradigm of Linked Services to unambiguously describe CDS systems in a machine- understandable fashion. This grants ontological descriptions of functional, non- functional and data semantics. These descriptions facilitate to overcome some of the barriers in CDS functionality sharing. In particular, the semantic model proposed allows using expressive queries to discover CDS services in health
III
networks, and analyzing CDS systems interfaces to understand how to interoperate with
them.
• Effective patient-CDS systems interaction: the dissertation proposes a method to
evaluate the communication process between patients and consumer-oriented CDS systems. The method aims for detecting if important human-computer interaction barriers that could lead to negative outcomes are present in CDS systems user interfaces.
Clinical Decision Support Systems (CDSS) were explicitly introduced in the 90’s with the aim of providing knowledge to clinicians in order to influence its decisions and, therefore, improve patients’ health care. There are different architectural approaches for implementing CDSS. Some of these approaches are based on cloud computing, which provides on-demand computing resources over the internet. The goal of this paper is to determine and discuss key issues and approaches involving architectural designs in implementing a CDSS using cloud computing. To this end, we performed a standard Systematic Literature Review (SLR) of primary studies showing the intervention of cloud computing on CDSS implementations. Twenty-one primary studies were reviewed. We found that CDSS architectural components are similar in most of the studies. Cloud-based CDSS are most used in Home Healthcare and Emergency Medical Systems. Alerts/Reminders and Knowledge Service are the most common implementations. Major challenges are around security, performance, and compatibility. We concluded on the benefits of implementing a cloud-based CDSS since it allows cost-efficient, ubiquitous and elastic computing resources. We highlight that some studies show weaknesses regarding the conceptualization of a cloud-based computing approach and lack of a formal methodology in the architectural design process.
Architectural approaches for implementing Clinical Decision Support Systems i...Luis Felipe Tabares Pérez
Clinical Decision Support Systems (CDSS) were explicitly introduced with the aim of providing knowledge to clinicians in order to influence its decisions and, therefore, improve patients’ health care. Its architectural approaches are based on Cloud Computing, which provides on-demand computing resources over internet. The goal of this presentation is to determine and discuss key issues and approaches involving architectural designs of a CDSS using cloud computing.
UDM (Unified Data Model) - Enabling Exchange of Comprehensive Reaction Inform...Frederik van den Broek
Slides from my talk at the ACS CINF Symposium on Chemical Nomenclature & Representation on 26 August 2019 in San Diego.
Abstract:
The first edition of the Beilstein Handbook of Organic Chemistry was published nearly 140 years ago. Electronic laboratory notebooks have been in use in chemistry for almost 20 years. And the life science industry still doesn't have a well-defined way of capturing and exchanging information about chemical reactions and relies on imprecise or vendor-specific data formats. Without a common language and structure to describe experiments, data integration is unnecessarily expensive and a significant part of published data has not been readily available for processing or analysis.
The Unified Data Model (UDM) project team aims to improve the situation. UDM is a collective effort of vendors and life science organizations to create an open, extendable and freely available reference model and data format for exchange of experimental information about compound synthesis and testing. Run under the umbrella of the Pistoia Alliance, the project team has published two releases of the UDM data format and it is expected that the model will continue to be improved as demand stipulates working with the Pistoia FAIR data implementation by industry community.
Enhancing Interoperability: The Implementation of OpenAIRE Guidelines and COA...4Science
ABSTRACT: The continuous work of the OpenAIRE community on guidelines for CRIS managers, literature repositories, and data archives, together with the publication of the “Behaviours and Technical Recommendations of the COAR Next Generation Repositories Working Group”, are raising important challenges for the CRIS and the repository communities, working together to make research information more an more interoperable, and, hopefully, open. The recommendations of the Open Science Policy Platform, published by the European Commission, identify FAIR (Findable-Accessible-Interoperable-Reusable) data among its priorities. In an interoperable world, all these indications lead toward a common direction, where implementers are encouraged to use open protocols, such as the OAI-PMH and ResourceSync, open standards such as CERIF, persistent identifiers such as DOIs and ORCiDs, to make this happen. The presentation will go through these challenges, illustrating how CRIS and repository managers should work together toward a successful information exchange, and exemplifying how a single free open platform, DSpace-CRIS, can implement both a CRIS and a repository and fulfill requirements for a FAIR environment for research information and research objects.
Ontologies For the Modern Age - McGuinness' Keynote at ISWC 2017Deborah McGuinness
Ontologies are seeing a resurgence of interest and usage as big data proliferates, machine learning advances, and integration of data becomes more paramount. The previous models of sometimes labor-intensive, centralized ontology construction and maintenance do not mesh well in today’s interdisciplinary world that is in the midst of a big data, information extraction, and machine learning explosion. In this talk, we will provide some historical perspective on ontologies and their usage, and discuss a model of building and maintaining large collaborative, interdisciplinary ontologies along with the data repositories and data services that they empower. We will give a few examples of heterogeneous semantic data resources made more interconnected and more powerful by ontology-supported infrastructures, discuss a vision for ontology-enabled future research and provide some examples in a large health empowerment joint effort between RPI and IBM Watson Health.
OpenAIRE provide dashboard #OpenAIREweek2020Pedro Príncipe
OpenAIRE provide session at the OpenAIRE week 2020 - A user journey in OpenAIRE provide - services and the interoperability guidelines, by Pedro Principe
OSFair2017 Workshop | The European Open Science Cloud Pilot Open Science Fair
Brian Matthews presents the European Open Science Cloud (EOSC) and the EOSCpilot | OSFair2017 Workshop
Workshop title: How FAIR friendly is your data catalogue?
Workshop overview:
This workshop will build upon the work planned by the EOSCpilot data interoperability task and the BlueBridge workshop held on April 3 at the RDA meeting. We will investigate common mechanisms for interoperation of data catalogues that preserve established community standards, norms and resources, while simplifying the process of being/becoming FAIR. Can we have a simple interoperability architecture based on a common set of metadata types? What are the minimum metadata requirements to expose FAIR data to EOSC services and EOSC users?
DAY 3 - PARALLEL SESSION 6 & 7
Slides to be presented at a webinar arranged by Metasolution as part of a Vinnova project http://metasolutions.se/2014/03/webbinarium-med-kerstin-forsberg-om-lankade-data-i-lakemedelsforskningen/
Overview of the CDISC2RDF ontologies and a first overview of the import/transformation for standards-as-is into machine processable OWL/RDF. See also http://cdisc2rdf.com/
Designing and launching the Clinical Reference LibraryKerstin Forsberg
Presentation for the European Clinical Data Forum conference, 24 May, 2011. Describing the business problems and drivers behind the design of a ISO11179 based metadata registry for clinical data. And also introducing the features of the CRL application.
"Linked Data, an opportunity to mitigate complexity pharmaceutical research and development" A poster accapted for first international workshop on linked web data management in Uppsala, 25 March, 2011
MIE2014: A Framework for Evaluating and Utilizing Medical Terminology Mappings
1. A Framework for Evaluating and Utilizing
Medical Terminology Mappings
EHR4CR – Open PHACTS, SALUS and W3C collaboration
Sajjad Hussain1, Hong Sun2, Ali Anil Sinaci3, Gokce Banu Laleci Erturkmen3, Charlie Mead4,
Alasdair Gray5, Deborah McGuinness6, Eric Prud’Hommeaux7, Christel Daniel1, Kerstin Forsberg8
MIE2014 2-Sept-2014
EHR4CR: 1INSERM UMRS 1142, Paris, France; 8 AstraZeneca, R&D Information, Mölndal Sweden
Open PHACTS: 5School of Mathematical and Computer Sciences, Heriot-Watt University
SALUS: 3Software Research, Development and Consultancy, Ankara, Turkey,
2Advanced Clinical Applications Research Group, Agfa HealthCare, Gent, Belgium
W3C: 4Health Care and Life Sciences IG, 7MIT, Cambridge, MA, USA,
6Department of Computer Science, Rensselaer Polytechnic Institute, Troy, US
1
2014 Medical Informatics Europe
Version 1.0 http://slideshare.net/kerfors/MIE2014
2. Objective
• Show the challenging nature of mapping
utilization among different terminologies.
• A framework built upon existing terminology
mappings to:
– Infer new mappings for different use cases.
– Present provenance of the mappings together
with the justification information.
– Perform mapping validation in order to show
that inferred mappings can be erroneous.
• Enable a more collaborative semantic landscape
with providers and consumers of terminology
mappings.
2
2014 Medical Informatics Europe
http://slideshare.net/kerfors/MIE2014
3. Semantic landscape 1(3)
3
For more information about these see the reference
slides in the end of this slide deck.
2014 Medical Informatics Europe
http://slideshare.net/kerfors/MIE2014
Consumers and, somewhat
reluctant, creators of mappings
4. Semantic landscape 2(3)
4
Providers of terminology mappings,
some examples
2014 Medical Informatics Europe
http://slideshare.net/kerfors/MIE2014
Consumers and, somewhat
reluctant, creators of mappings
5. Semantic landscape 3(3)
5
Providers of terminology mappings,
some examples
Providers of terminologies,
some examples
2014 Medical Informatics Europe
http://slideshare.net/kerfors/MIE2014
Consumers and, somewhat
reluctant, creators of mappings
6. Rationale
• Challenging nature of mapping utilization, or
“How hard can it be?”
– Appear to the uninitiated as a simple exercise like “this
term in this terminology is the same as that term in that
terminology”
6
2014 Medical Informatics Europe
http://slideshare.net/kerfors/MIE2014
7. Example Scenario
• Challenging nature of mapping utilization, or
“How hard can it be?”
– Appear to the uninitiated as a simple exercise like “this
term in this terminology is the same as that term in that
terminology”
7
10. Example Scenario 3(3)
10
matches
matches
matches
Defined
Mappings
Inferred
Mappings
matches
Problematic
Mappings
11. “It’s complicated”. So, we often become, somewhat
reluctant, creators of our own mappings
• Availability of up-to-date information to assess the suitability
of a given terminology for a particular use case.
• Difficulty of correctly using complex, rapidly evolving
terminologies.
• Differences in granularity between the source and target
terminologies.
• Lack of semantic mappings in order to completely and
unambiguously define computationally equivalent semantics.
• Lack of provenance information, i.e. how, when and for what
purposes the mappings were created.
• Time and effort required to complete and evaluate mappings.
11
2014 Medical Informatics Europe
http://slideshare.net/kerfors/MIE2014
12. Objective: A more collaborative
semantic landscape
12
Informed consumers of
terminology mappings
Value adding providers of
terminology mappings
Value adding providers of
terminologies
13. Framework
13
2014 Medical Informatics Europe
http://slideshare.net/kerfors/MIE2014
14. Mapping Strategies
• Lexical Mappings (LOOM) generated by performing lexical comparison between
preferred labels and alternative labels of terms. These mappings are represented via
skos:closeMatch property.
• Xref OBO Mappings Xref and Dbxref are properties used by ontology developers to
refer to an analogous term in another vocabulary. These mappings are represented
via skos:relatedMatch property.
• CUI Mappings from UMLS are extracted by utilizing the same Concept Unique
Identifier (CUI) annotation as join point of similar terms from different vocabularies.
These mappings are represented via skos:closeMatch property.
• URI-based Mappings are generated identity mappings between term concepts in
different ontologies that are represented by the same URI. These mappings are
represented via skos:exactMatch property.
14
2014 Medical Informatics Europe
http://slideshare.net/kerfors/MIE2014
16. Collaborative semantic landscape
16
Informed consumers of
terminology mappings
Value adding providers of
terminology mappings
Value adding providers of
terminologies
Enabled by applications of
the RDF standard
17. 17
Application of RDF for
representing mappings
Enabled by applications of
the RDF standard
18. 18
Application of RDF for
representing provenance
Enabled by applications of
the RDF standard
19. Applications of RDF for packaging assertions
19
(e.g. mappings) with provenance
Enabled by applications of
the RDF standard
20. 20
Applications of RDF for describing
datasets and linksets with justifications
Enabled by applications of
the RDF standard
21. Example Scenario
21
matches
matches
matches
Defined
Mappings
Inferred
Mappings
matches
Problematic
Mappings
26. 26
Applications of RDF for describing
datasets and linksets with justifications
2014 Medical Informatics Europe
http://slideshare.net/kerfors/MIE2014
Enabled by applications of
the RDF standard
30. CIM Workshop at ISWC2014 to discuss:
Justification Vocabulary terms for
Relating Terminology Concepts/Terms
30
31. Acknowledgments
• Session chair
• MIE2014 organizers
• SALUS team: Hong Sun, Ali Anil Sinaci, Gokce Banu Laleci Erturkmen
– Support from the European Community’s Seventh Framework Programme
(FP7/2007–2013) under Grant Agreement No. ICT-287800, SALUS Project
(Scalable, Standard based Interoperability Framework for Sustainable
Proactive Post Market Safety Studies).
• EHR4CR team: WP4, WPG2, WP7 members
– Support from the Innovative Medicines Initiative Joint Undertaking under
grant agreement n° [No 115189]. European Union's Seventh Framework
Programme (FP7/2007-2013) and EFPIA companies
• Open PHACTS team: Alasdair Gray
• W3C HCLS team: Eric Prud’Hommeaux, Charlie Mead
31
2014 Medical Informatics Europe
http://slideshare.net/kerfors/MIE2014
32. Reference material
• Projects/organisations of the authors of this paper
• Example
– Mapping Representation using SKOS
– Mapping Provenance Representation
2014 Joint Summits on Translational Science 32
33. EHR4CR
Electronic Healthcare Record For Clinical Research
http://www.ehr4cr.eu/
• IMI (Innovative Medicine Initiative)
– Public-Private Partnership between EU and EFPIA
• ICT platform: using EHR data for supporting
clinical research
• Protocol feasibility
• Patient recruitment
• Clinical trial execution: Clinical Research Forms (eCRF)/
Individual Case Safety Reports (ICSR) prepopulation
• 33 European academic and industrial partners
– 11 pilot sites from 5 countries – 4 millions patients
33
2014 Medical Informatics Europe
http://slideshare.net/kerfors/MIE2014
34. Open PHACTS
Open Pharmacology Space
http://www.openphacts.org/
• IMI (Innovative Medicine Initiative)
• 31 partners: 10 pharma – 21 academic / SME
• The Challenge - Open standards for
drug discovery data
– Develop robust standards for solid
integration between data sources via
semantic technologies
– Implement the standards in a semantic
integration hub (“Open Pharmacological Space”)
– Deliver services to support on-going drug
discovery programs in pharma and
public domain
34
2014 Medical Informatics Europe
http://slideshare.net/kerfors/MIE2014
35. SALUS
Sustainable Proactive Post Market Safety Studies
http://www.salusproject.eu/
• European Commission (STREP)
• ICT platform : using EHRs data to improve post-market
safety activities on a proactive basis
• Semi-automatic notification of suspected adverse events
• Reporting adverse events (Individual Case Safety Reports
(ICSR) prepopulation)
• Post Marketing safety studies
• 8 European academic and industrial partners
– 2 pilot sites
• Lombardia Region (Italy) and Eastern Saxony (Germany)
35
2014 Medical Informatics Europe
http://slideshare.net/kerfors/MIE2014
36. W3C
Semantic Web Health Care and Life Sciences Interest
Group (HCLS IG)
http://www.w3.org/2001/sw/
• ..
36
2014 Medical Informatics Europe
http://slideshare.net/kerfors/MIE2014
Presentation Title: A Framework for Evaluating and Utilizing Medical Terminology Mappings
Timeslot 19- Tuesday, Sep. 2nd. 10:30 - 12:00 in room 04-Haskoy as a Full paper.
Track: Natural Language Processing
Session: Natural Language Processing 2
In this paper we show the challenging nature of mapping utilization among different terminologies.
The introduced framework has been built upon existing terminology mappings to
infer new mappings for different computable semantic interoperability use cases,
present provenance of the mappings together with the context information—an important problem for term mapping utilization, and
perform mapping validation in order to show that inferred mappings can be erroneous.
The framework enables a more collaborative semantic landscape with providers and consumers of terminology mappings.
For example, considering SNOMED-CT as hub terminology, both ICD-9 and MedDRA codes are mapped to SNOMED-CT codes.
For example, considering SNOMED-CT as hub terminology, both ICD-9 and MedDRA codes are mapped to SNOMED-CT codes.
For example, considering SNOMED-CT as hub terminology, both ICD-9 and MedDRA codes are mapped to SNOMED-CT codes.
For example, considering SNOMED-CT as hub terminology, both ICD-9 and MedDRA codes are mapped to SNOMED-CT codes.
For example, considering SNOMED-CT as hub terminology, both ICD-9 and MedDRA codes are mapped to SNOMED-CT codes.
For example, considering SNOMED-CT as hub terminology, both ICD-9 and MedDRA codes are mapped to SNOMED-CT codes.
Enable a more collaborative semantic landscape with providers and consumers of terminology mappings.
Enable a more collaborative semantic landscape with providers and consumers of terminology mappings.
Enable a more collaborative semantic landscape with providers and consumers of terminology mappings.
Enable a more collaborative semantic landscape with providers and consumers of terminology mappings.
For example, considering SNOMED-CT as hub terminology, both ICD-9 and MedDRA codes are mapped to SNOMED-CT codes.
For example, considering SNOMED-CT as hub terminology, both ICD-9 and MedDRA codes are mapped to SNOMED-CT codes.
For example, considering SNOMED-CT as hub terminology, both ICD-9 and MedDRA codes are mapped to SNOMED-CT codes.
Using the nanopublication schema, the inferred mapping are represented as an RDF triple in the Assertion graph.
The provenance information related to the mapping assertions are recorded into two categories:
Attribution, where meta-data and context about the mapping can be represented;
Supporting, where the justification behind obtaining the recorded mapping assertion are represented. In this case, the Supporting graph includes a ‘meta-level’ justification trace generated from EYE reasoning engine.
Using the nanopublication schema, the inferred mapping are represented as an RDF triple in the Assertion graph.
The provenance information related to the mapping assertions are recorded into two categories:
Attribution, where meta-data and context about the mapping can be represented;
Supporting, where the justification behind obtaining the recorded mapping assertion are represented. In this case, the Supporting graph includes a ‘meta-level’ justification trace generated from EYE reasoning engine.
Enable a more collaborative semantic landscape with providers and consumers of terminology mappings.
Using the nanopublication schema, the inferred mapping are represented as an RDF triple in the Assertion graph.
The provenance information related to the mapping assertions are recorded into two categories:
Attribution, where meta-data and context about the mapping can be represented;
Supporting, where the justification behind obtaining the recorded mapping assertion are represented. In this case, the Supporting graph includes a ‘meta-level’ justification trace generated from EYE reasoning engine.
Largest public-private partnership to date with the goal to tie interoperability aspects
The IMI EHR4CR project will run over 4 years (2012-2015) and involve 33 European academic and industrial partners
Comprehensive business model for governance, acceptance, adoption and sustainability