The document provides information about an upcoming Laboratory LOINC Workshop in Chicago, Illinois. It includes an agenda for the workshop covering topics such as the origins of LOINC, using the RELMA mapping tool, searching and mapping local terms to LOINC codes, and hands-on practice mapping terms. The workshop will be led by Daniel Vreeman from Indiana University and Clem McDonald from the National Library of Medicine.
The document discusses a presentation on LOINC (Logical Observation Identifiers Names and Codes) given at the 2011 Public Health Informatics conference in Atlanta, GA. The presentation provides an introduction to LOINC and covers topics such as the origins of LOINC, common elements in LOINC terms, LOINC collections like forms and surveys, and domain-specific approaches to mapping standards and terminologies in areas like microbiology. It also discusses LOINC tools and resources for mapping terms and codes.
A proposal for interoperable health information exchange with two Esperantos: ICF and LOINC. Presented at the 2010 NAAC ICF Conference: Enhancing our Understanding of the ICF.
2012 02 16 - Clinical LOINC Tutorial - Collections - Panels Forms and Assessm...dvreeman
This document summarizes a presentation on using LOINC (Logical Observation Identifiers Names and Codes) to standardize patient assessments. It discusses how LOINC provides a uniform model for representing standardized questions, answers, and panels/forms. The presentation covers the iterative development of LOINC's assessment model over 10 years, current assessment content in LOINC, and lessons learned regarding variation, data modeling, and intellectual property issues.
Presentation by Daniel J. Vreeman, PT, DPT, MSc for the AMIA KRS Working Group. Title: LOINC - An Introduction to the Universal Catalog of Laboratory and Clinical Observations.
This document provides an introduction and overview of LOINC (Logical Observation Identifiers Names and Codes). It discusses the origins of LOINC as a universal code system to facilitate exchange of clinical observation data. It describes how LOINC provides codes for questions, while other vocabularies provide codes for answers. The document outlines the growth of LOINC over time, its adoption internationally and in the US, and new areas of content modeling like standardized assessments. It emphasizes that LOINC development is an open, collaborative community effort to standardize clinical observations and questions.
This document discusses various topics related to drug discovery through bioinformatics. It begins by describing how genome-wide RNAi screening in the nematode C. elegans can be used to identify genes involved in biological pathways related to diseases like type-2 diabetes. It then discusses topics like structural genomics, target identification and validation, high-throughput screening approaches and facilities, sources for screening libraries, criteria for hit and lead compounds, and computational methods used in hit identification and optimization like pharmacophore modeling and evaluating compounds against the "rule of five". Descriptors that can be used for characterizing compounds are also listed.
2009 12 07 - LOINC Introduction and Overviewdvreeman
This document provides an overview and introduction to LOINC (Logical Observation Identifiers Names and Codes). It discusses the origins and growth of LOINC as a universal standard for identifying laboratory and clinical observations. Key points include: LOINC was created in 1994 by Regenstrief Institute to facilitate information exchange; it has over 80,000 codes covering many clinical domains and is used internationally; and adoption has increased steadily with over 800 downloads per month and participation from many organizations globally and within the US.
The document discusses a presentation on LOINC (Logical Observation Identifiers Names and Codes) given at the 2011 Public Health Informatics conference in Atlanta, GA. The presentation provides an introduction to LOINC and covers topics such as the origins of LOINC, common elements in LOINC terms, LOINC collections like forms and surveys, and domain-specific approaches to mapping standards and terminologies in areas like microbiology. It also discusses LOINC tools and resources for mapping terms and codes.
A proposal for interoperable health information exchange with two Esperantos: ICF and LOINC. Presented at the 2010 NAAC ICF Conference: Enhancing our Understanding of the ICF.
2012 02 16 - Clinical LOINC Tutorial - Collections - Panels Forms and Assessm...dvreeman
This document summarizes a presentation on using LOINC (Logical Observation Identifiers Names and Codes) to standardize patient assessments. It discusses how LOINC provides a uniform model for representing standardized questions, answers, and panels/forms. The presentation covers the iterative development of LOINC's assessment model over 10 years, current assessment content in LOINC, and lessons learned regarding variation, data modeling, and intellectual property issues.
Presentation by Daniel J. Vreeman, PT, DPT, MSc for the AMIA KRS Working Group. Title: LOINC - An Introduction to the Universal Catalog of Laboratory and Clinical Observations.
This document provides an introduction and overview of LOINC (Logical Observation Identifiers Names and Codes). It discusses the origins of LOINC as a universal code system to facilitate exchange of clinical observation data. It describes how LOINC provides codes for questions, while other vocabularies provide codes for answers. The document outlines the growth of LOINC over time, its adoption internationally and in the US, and new areas of content modeling like standardized assessments. It emphasizes that LOINC development is an open, collaborative community effort to standardize clinical observations and questions.
This document discusses various topics related to drug discovery through bioinformatics. It begins by describing how genome-wide RNAi screening in the nematode C. elegans can be used to identify genes involved in biological pathways related to diseases like type-2 diabetes. It then discusses topics like structural genomics, target identification and validation, high-throughput screening approaches and facilities, sources for screening libraries, criteria for hit and lead compounds, and computational methods used in hit identification and optimization like pharmacophore modeling and evaluating compounds against the "rule of five". Descriptors that can be used for characterizing compounds are also listed.
2009 12 07 - LOINC Introduction and Overviewdvreeman
This document provides an overview and introduction to LOINC (Logical Observation Identifiers Names and Codes). It discusses the origins and growth of LOINC as a universal standard for identifying laboratory and clinical observations. Key points include: LOINC was created in 1994 by Regenstrief Institute to facilitate information exchange; it has over 80,000 codes covering many clinical domains and is used internationally; and adoption has increased steadily with over 800 downloads per month and participation from many organizations globally and within the US.
The document summarizes a presentation on bioinformatics and Ruby. It discusses how bioinformatics deals with large amounts of biological data in the age of big data and data science. It outlines some of the key programming languages used in bioinformatics like C/C++, Perl, Java, R, and Python. It presents examples of how Ruby is being used for bioinformatics projects in Taiwan, including building an Ensembl virtual machine, developing analysis pipelines like DR.RAW, using Neo4j for data integration, and developing API and web applications. Finally, it discusses opportunities for learning bioinformatics and potential markets and applications for bioinformatics technologies.
Public Laboratory LOINC Workshop and Committee Meeting documents the origins and growth of LOINC as a universal standard for clinical observations and laboratory results. It discusses how LOINC provides a common language for information exchange and how its open model has led to widespread international adoption and translations. Large healthcare organizations around the world have implemented LOINC to facilitate interoperability across hundreds of systems.
The document outlines the schedule and content for a bioinformatics course. It includes 10 lessons covering topics like biological databases, sequence alignments, database searching, phylogenetics, and protein structure. It also mentions that the final exam will include randomly generated images from a set of 713 images.
The document discusses an introduction and tutorial about LOINC® and RELMA® given to the CDC Vocabulary Team Meeting. It provides an overview of the origins and growth of LOINC, which was created in 1994 to serve as a universal standard for identifying clinical observations. It aims to facilitate information exchange. The presentation describes LOINC's role in coding questions like lab test names rather than answers like numeric results. It also reviews the international adoption of LOINC across organizations in many countries.
This document discusses biomarkers for assessing immune function throughout the drug development process. It describes how various techniques can be used to identify, validate, and qualify biomarkers. These include flow cytometry to analyze cell populations and activation markers, Luminex to measure cytokine levels, and gene expression profiling using NanoString. Whole blood stimulation assays are discussed as a way to assess target engagement and immune responses ex vivo. The importance of assay validation and understanding sources of variation are also covered. Biomarkers can provide insights into mechanisms of action, safety, and efficacy to support clinical development.
Specialized oncology reference laboratory providing the latest testing technologies, global/tech-only options, and interactive education to the pathology community
Offer the complete spectrum of diagnostic services through nationwide network of laboratories
Dedicated to providing superior service, faster turn-around times, and complete attention to the needs of our clients and their patients
The document is a catalog for the Understanding Disease series of animated medical videos from the Focus Apps Store. It lists various diseases and medical conditions that are covered by the animated videos in the series, organized by body system. Each disease video explains causes, symptoms, diagnoses, and treatments of the condition. The document promotes the Focus Apps Store website for more information on purchasing access to the video series.
Open innovation contributions from RSC resulting from the Open Phacts projectKen Karapetyan
The Royal Society of Chemistry was pleased to contribute to the Open PHACTS project, a 3 year project funded by the Innovative Medicines Initiative fund from the European Union. For three years we developed our existing platforms, created new and innovative widgets and data platforms to handle chemistry data, extended existing chemistry ontologies and embraced the semantic web open standards. As a result RSC served as the centralized chemistry data hub for the project. With the conclusion of the Open PHACTS project we will report on our experiences resulting from our participation in the project and provide an overview of what tools, capabilities and data have been released into the community as a result of our participation and how this may influence future projects. This will include the Open PHACTS open chemistry data dump including the chemistry related data in chemistry and semantic web consumable formats as well as some of the resulting chemistry software released to the community. The Open PHACTS project resulted in significant contributions to the chemistry community as well as the supporting pharmaceutical companies and biomedical community.
The Royal Society of Chemistry was pleased to contribute to the Open PHACTS project, a 3 year project funded by the Innovative Medicines Initiative fund from the European Union. For three years we developed our existing platforms, created new and innovative widgets and data platforms to handle chemistry data, extended existing chemistry ontologies and embraced the semantic web open standards. As a result RSC served as the centralized chemistry data hub for the project. With the conclusion of the Open PHACTS project we will report on our experiences resulting from our participation in the project and provide an overview of what tools, capabilities and data have been released into the community as a result of our participation and how this may influence future projects. This will include the Open PHACTS open chemistry data dump including the chemistry related data in chemistry and semantic web consumable formats as well as some of the resulting chemistry software released to the community. The Open PHACTS project resulted in significant contributions to the chemistry community as well as the supporting pharmaceutical companies and biomedical community.
The document discusses a workshop on Systems Biology Graph Notation (SBGN) comprehensive disease maps held at the Luxembourg Centre for Systems Biomedicine (LCSB) in Luxembourg on June 14th, 2012. The workshop was supported by international speakers and aimed to discuss SBGN disease maps. The LCSB is an interdisciplinary research center within the University of Luxembourg focused on experimental and computational biology as well as technical platforms and clinical research related to systems biomedicine. Specific research areas of focus at the LCSB include network models of diseases, computational biology, and Parkinson's disease.
Ontologies and Semantic Web technologies play an important role in the life sciences to help make data more interoperable and reusable. There are now many publicly available ontologies that enable biologists to describe everything from gene function through to animal physiology and disease.
Various efforts such as the Open Biomedical Ontologies (OBO) foundry provide central registries for biomedical ontologies and ensure they remain interoperable through a set of common shared development principles.
At EMBL-EBI we contribute to the development of biomedical ontologies and make extensive use of them in the annotation of public datasets. Biological data typically comes with rich and often complex metadata, so the ontologies provide a standard way to capture “what the data is about” and gives us hooks to connect to more data about similar things.
These ontology annotations have been put to good use in a number of large-scale data integration efforts and there’s an increasing recognition of the need for ontologies in making data FAIR (Findable, Accessible, Interoperable and Reusable).
EMBL-EBI build a number of integrative data platforms where ontologies are at the core of our domain models. One example is the Open Targets platform, where data about disease from 18 different databases can be aggregated and grouped based on therapeutic areas in the ontology and used to identify potential drug targets.
The ontologies team at EMBL-EBI provide a suite of services that are aimed at making ontologies more accessible for both humans and machines. We work with scientific data curators and software developers to integrate ontologies and semantics into both the data generation and data presentation workflows. We provide:
– An ontology lookup service (OLS) that provides search and visualisation services to over 200+ ontologies
– Services for automating the annotation of metadata and learning from previous annotations (Zooma)
– An ontology mapping and alignment service (OXO)
– Tools for working with metadata and ontologies in spreadsheets (Webulous)
– Software for enriching documents in search engines to support “semantic” query expansion
I’ll present how we are using these services at EMBL-EBI to scale up the semantic annotation of metadata. I’ll talk about our open source technology stack and describe how we utilise a polyglot persistence approach (graph databases, triples stores, document stores etc) to optimize how we deliver ontologies and semantics to our users.
The document discusses Zhiyong Lu's work at the National Center for Biotechnology Information (NCBI) developing text mining and natural language processing tools to help biocuration and biomedical research. Some key points:
- Lu leads teams that develop machine learning algorithms and tools for biomedical literature mining, clinical text analysis, and medical image analysis.
- Popular tools developed include PubTator, tmVar, DNorm, and deep learning models for chest X-ray and retinal image analysis.
- The tools have been applied to tasks like curation of PubMed, UniProt, and other databases and have received positive user feedback.
- Lu has also led several BioCreative challenges
The document describes the development of an in silico and in vitro platform for predicting and diagnosing hepatotoxicity. Key aspects include:
1) Using Ontomine to predict toxicity endpoints from chemical structures and identify related drug targets and biomarkers, which are then validated experimentally.
2) Developing a drug-toxicity matrix of 50 compounds categorized by hepatotoxicity mechanisms to collect data.
3) Analyzing gene expression data from rats exposed to hepatotoxic and non-hepatotoxic compounds to identify differentially expressed genes, some of which are predicted to be novel blood-based biomarkers.
4) Validating 11 predicted biomarkers using Ontomine on an independent dataset, with 10 biomarkers correlating
This document discusses biological variation in clinical measurements. It aims to identify the nature of biological variation, appreciate its significance, and understand how to determine and apply indices of biological variation. Biological variation refers to components of variance in biochemical measurements determined by a subject's physiology. The sources, quantification, and practical applications of biological variation data are explored. Understanding biological variation is fundamental to developing reference data and interpreting clinical measurements over time.
Specialty pathology endpoints for all clients, including complex immunohistochemistry, in situ hybridization, image analysis, artificial intelligence, stereology, tissue cross-reactivity, and electron microscopy.
The first biochip was invented by an American company namely Affymetrix, and the product of this company is GeneChip (DNA microarrays). These products comprise the number of individual DNA sensors used for sensing defects. Biochip plays an essential role in the field of biology research like systems biology as well as disease biology while the number of clinical applications is rising. It is a set of microarrays which are placed on a strong surface of a substrate to allow thousands of reactions to be performed in less time. The development of biochip mainly includes the combination of molecular biology, biochemistry, and genetics. Biochips are used for analyzing organic molecules connected with a live organism. This power-point presentation discusses what is Biochip, types, biochips and their uses, disadvantages, and its applications.
The document discusses various considerations for identifying central nervous system (CNS) drugs, including bioavailability. It defines bioavailability as the amount of drug available in the body to act at the target. Three key points are made: 1) Drugs must reach the CNS target area in sufficient amounts during the appropriate time window to have efficacy, otherwise bioavailability limits efficacy; 2) Molecular properties influence absorption, distribution, metabolism and excretion of drugs, impacting bioavailability; 3) Case studies show how changes in CNS bioavailability and metabolism can impact drug safety and efficacy.
This document provides an overview of the RELMA tool for mapping local laboratory terms to standardized LOINC codes. It discusses the goals of health information standards including interoperability and comparability. It then reviews key aspects of the RELMA tool including installing RELMA, loading and preparing local terms for mapping, searching LOINC hierarchies, mapping terms, and exporting mapped terms. The presentation emphasizes best practices for accurate mapping including understanding what is being measured versus reported and ensuring all aspects of the LOINC code are appropriately represented.
This document discusses the origins and development of the LOINC Clinical Document Ontology (CDO), which provides a standardized terminology for clinical document names. It describes how the CDO was created based on empirical analysis of over 2000 local document names. The CDO uses a multi-axial model with domains like subject matter, role, setting, type of service, and kind of document. Iterative evaluations found the expanded CDO better mapped local names than the original. Ongoing work involves adding new content and harmonizing with other clinical terminologies.
2012 02 10 - Vreeman - Possibilities and Implications of ICF-powered Health I...dvreeman
The document discusses the possibilities and implications of using the International Classification of Functioning (ICF) to power health information technology. It describes how incorporating standardized vocabularies like ICF and LOINC into electronic health records could allow for data reuse across settings, clinical decision support, and a more seamless exchange of health information. This would help realize the vision of a healthcare system with coordinated, consumer-centered care enabled by digital tools.
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The document summarizes a presentation on bioinformatics and Ruby. It discusses how bioinformatics deals with large amounts of biological data in the age of big data and data science. It outlines some of the key programming languages used in bioinformatics like C/C++, Perl, Java, R, and Python. It presents examples of how Ruby is being used for bioinformatics projects in Taiwan, including building an Ensembl virtual machine, developing analysis pipelines like DR.RAW, using Neo4j for data integration, and developing API and web applications. Finally, it discusses opportunities for learning bioinformatics and potential markets and applications for bioinformatics technologies.
Public Laboratory LOINC Workshop and Committee Meeting documents the origins and growth of LOINC as a universal standard for clinical observations and laboratory results. It discusses how LOINC provides a common language for information exchange and how its open model has led to widespread international adoption and translations. Large healthcare organizations around the world have implemented LOINC to facilitate interoperability across hundreds of systems.
The document outlines the schedule and content for a bioinformatics course. It includes 10 lessons covering topics like biological databases, sequence alignments, database searching, phylogenetics, and protein structure. It also mentions that the final exam will include randomly generated images from a set of 713 images.
The document discusses an introduction and tutorial about LOINC® and RELMA® given to the CDC Vocabulary Team Meeting. It provides an overview of the origins and growth of LOINC, which was created in 1994 to serve as a universal standard for identifying clinical observations. It aims to facilitate information exchange. The presentation describes LOINC's role in coding questions like lab test names rather than answers like numeric results. It also reviews the international adoption of LOINC across organizations in many countries.
This document discusses biomarkers for assessing immune function throughout the drug development process. It describes how various techniques can be used to identify, validate, and qualify biomarkers. These include flow cytometry to analyze cell populations and activation markers, Luminex to measure cytokine levels, and gene expression profiling using NanoString. Whole blood stimulation assays are discussed as a way to assess target engagement and immune responses ex vivo. The importance of assay validation and understanding sources of variation are also covered. Biomarkers can provide insights into mechanisms of action, safety, and efficacy to support clinical development.
Specialized oncology reference laboratory providing the latest testing technologies, global/tech-only options, and interactive education to the pathology community
Offer the complete spectrum of diagnostic services through nationwide network of laboratories
Dedicated to providing superior service, faster turn-around times, and complete attention to the needs of our clients and their patients
The document is a catalog for the Understanding Disease series of animated medical videos from the Focus Apps Store. It lists various diseases and medical conditions that are covered by the animated videos in the series, organized by body system. Each disease video explains causes, symptoms, diagnoses, and treatments of the condition. The document promotes the Focus Apps Store website for more information on purchasing access to the video series.
Open innovation contributions from RSC resulting from the Open Phacts projectKen Karapetyan
The Royal Society of Chemistry was pleased to contribute to the Open PHACTS project, a 3 year project funded by the Innovative Medicines Initiative fund from the European Union. For three years we developed our existing platforms, created new and innovative widgets and data platforms to handle chemistry data, extended existing chemistry ontologies and embraced the semantic web open standards. As a result RSC served as the centralized chemistry data hub for the project. With the conclusion of the Open PHACTS project we will report on our experiences resulting from our participation in the project and provide an overview of what tools, capabilities and data have been released into the community as a result of our participation and how this may influence future projects. This will include the Open PHACTS open chemistry data dump including the chemistry related data in chemistry and semantic web consumable formats as well as some of the resulting chemistry software released to the community. The Open PHACTS project resulted in significant contributions to the chemistry community as well as the supporting pharmaceutical companies and biomedical community.
The Royal Society of Chemistry was pleased to contribute to the Open PHACTS project, a 3 year project funded by the Innovative Medicines Initiative fund from the European Union. For three years we developed our existing platforms, created new and innovative widgets and data platforms to handle chemistry data, extended existing chemistry ontologies and embraced the semantic web open standards. As a result RSC served as the centralized chemistry data hub for the project. With the conclusion of the Open PHACTS project we will report on our experiences resulting from our participation in the project and provide an overview of what tools, capabilities and data have been released into the community as a result of our participation and how this may influence future projects. This will include the Open PHACTS open chemistry data dump including the chemistry related data in chemistry and semantic web consumable formats as well as some of the resulting chemistry software released to the community. The Open PHACTS project resulted in significant contributions to the chemistry community as well as the supporting pharmaceutical companies and biomedical community.
The document discusses a workshop on Systems Biology Graph Notation (SBGN) comprehensive disease maps held at the Luxembourg Centre for Systems Biomedicine (LCSB) in Luxembourg on June 14th, 2012. The workshop was supported by international speakers and aimed to discuss SBGN disease maps. The LCSB is an interdisciplinary research center within the University of Luxembourg focused on experimental and computational biology as well as technical platforms and clinical research related to systems biomedicine. Specific research areas of focus at the LCSB include network models of diseases, computational biology, and Parkinson's disease.
Ontologies and Semantic Web technologies play an important role in the life sciences to help make data more interoperable and reusable. There are now many publicly available ontologies that enable biologists to describe everything from gene function through to animal physiology and disease.
Various efforts such as the Open Biomedical Ontologies (OBO) foundry provide central registries for biomedical ontologies and ensure they remain interoperable through a set of common shared development principles.
At EMBL-EBI we contribute to the development of biomedical ontologies and make extensive use of them in the annotation of public datasets. Biological data typically comes with rich and often complex metadata, so the ontologies provide a standard way to capture “what the data is about” and gives us hooks to connect to more data about similar things.
These ontology annotations have been put to good use in a number of large-scale data integration efforts and there’s an increasing recognition of the need for ontologies in making data FAIR (Findable, Accessible, Interoperable and Reusable).
EMBL-EBI build a number of integrative data platforms where ontologies are at the core of our domain models. One example is the Open Targets platform, where data about disease from 18 different databases can be aggregated and grouped based on therapeutic areas in the ontology and used to identify potential drug targets.
The ontologies team at EMBL-EBI provide a suite of services that are aimed at making ontologies more accessible for both humans and machines. We work with scientific data curators and software developers to integrate ontologies and semantics into both the data generation and data presentation workflows. We provide:
– An ontology lookup service (OLS) that provides search and visualisation services to over 200+ ontologies
– Services for automating the annotation of metadata and learning from previous annotations (Zooma)
– An ontology mapping and alignment service (OXO)
– Tools for working with metadata and ontologies in spreadsheets (Webulous)
– Software for enriching documents in search engines to support “semantic” query expansion
I’ll present how we are using these services at EMBL-EBI to scale up the semantic annotation of metadata. I’ll talk about our open source technology stack and describe how we utilise a polyglot persistence approach (graph databases, triples stores, document stores etc) to optimize how we deliver ontologies and semantics to our users.
The document discusses Zhiyong Lu's work at the National Center for Biotechnology Information (NCBI) developing text mining and natural language processing tools to help biocuration and biomedical research. Some key points:
- Lu leads teams that develop machine learning algorithms and tools for biomedical literature mining, clinical text analysis, and medical image analysis.
- Popular tools developed include PubTator, tmVar, DNorm, and deep learning models for chest X-ray and retinal image analysis.
- The tools have been applied to tasks like curation of PubMed, UniProt, and other databases and have received positive user feedback.
- Lu has also led several BioCreative challenges
The document describes the development of an in silico and in vitro platform for predicting and diagnosing hepatotoxicity. Key aspects include:
1) Using Ontomine to predict toxicity endpoints from chemical structures and identify related drug targets and biomarkers, which are then validated experimentally.
2) Developing a drug-toxicity matrix of 50 compounds categorized by hepatotoxicity mechanisms to collect data.
3) Analyzing gene expression data from rats exposed to hepatotoxic and non-hepatotoxic compounds to identify differentially expressed genes, some of which are predicted to be novel blood-based biomarkers.
4) Validating 11 predicted biomarkers using Ontomine on an independent dataset, with 10 biomarkers correlating
This document discusses biological variation in clinical measurements. It aims to identify the nature of biological variation, appreciate its significance, and understand how to determine and apply indices of biological variation. Biological variation refers to components of variance in biochemical measurements determined by a subject's physiology. The sources, quantification, and practical applications of biological variation data are explored. Understanding biological variation is fundamental to developing reference data and interpreting clinical measurements over time.
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The first biochip was invented by an American company namely Affymetrix, and the product of this company is GeneChip (DNA microarrays). These products comprise the number of individual DNA sensors used for sensing defects. Biochip plays an essential role in the field of biology research like systems biology as well as disease biology while the number of clinical applications is rising. It is a set of microarrays which are placed on a strong surface of a substrate to allow thousands of reactions to be performed in less time. The development of biochip mainly includes the combination of molecular biology, biochemistry, and genetics. Biochips are used for analyzing organic molecules connected with a live organism. This power-point presentation discusses what is Biochip, types, biochips and their uses, disadvantages, and its applications.
The document discusses various considerations for identifying central nervous system (CNS) drugs, including bioavailability. It defines bioavailability as the amount of drug available in the body to act at the target. Three key points are made: 1) Drugs must reach the CNS target area in sufficient amounts during the appropriate time window to have efficacy, otherwise bioavailability limits efficacy; 2) Molecular properties influence absorption, distribution, metabolism and excretion of drugs, impacting bioavailability; 3) Case studies show how changes in CNS bioavailability and metabolism can impact drug safety and efficacy.
This document provides an overview of the RELMA tool for mapping local laboratory terms to standardized LOINC codes. It discusses the goals of health information standards including interoperability and comparability. It then reviews key aspects of the RELMA tool including installing RELMA, loading and preparing local terms for mapping, searching LOINC hierarchies, mapping terms, and exporting mapped terms. The presentation emphasizes best practices for accurate mapping including understanding what is being measured versus reported and ensuring all aspects of the LOINC code are appropriately represented.
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This document discusses the origins and development of the LOINC Clinical Document Ontology (CDO), which provides a standardized terminology for clinical document names. It describes how the CDO was created based on empirical analysis of over 2000 local document names. The CDO uses a multi-axial model with domains like subject matter, role, setting, type of service, and kind of document. Iterative evaluations found the expanded CDO better mapped local names than the original. Ongoing work involves adding new content and harmonizing with other clinical terminologies.
2012 02 10 - Vreeman - Possibilities and Implications of ICF-powered Health I...dvreeman
The document discusses the possibilities and implications of using the International Classification of Functioning (ICF) to power health information technology. It describes how incorporating standardized vocabularies like ICF and LOINC into electronic health records could allow for data reuse across settings, clinical decision support, and a more seamless exchange of health information. This would help realize the vision of a healthcare system with coordinated, consumer-centered care enabled by digital tools.
2012 02 11 EHRs - healthcare system chicken soup or rotten eggdvreeman
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2012 02 11 - Informatics Competencies in PT Educationdvreeman
The document proposes a framework for informatics competencies in physical therapy education. It discusses how informatics is addressed in core PT education documents and competencies established in other healthcare professions. The framework proposes competencies in 6 roles: lifelong learner, clinical reasoning, evidence-based practice, electronic health record literacy, advancing the science of PT, and accountability, communication, and education. It emphasizes viewing informatics as a longitudinal theme across the curriculum.
This document provides an overview of LOINC (Logical Observation Identifiers Names and Codes) presented by Daniel Vreeman. In 3 sentences: LOINC is a universal standard for identifying health measurements and observations that allows for data exchange between systems. It has over 60,000 codes covering laboratory and clinical observations. The LOINC community is open-source and has over 14,000 members from 145 countries contributing to its ongoing development and adoption worldwide.
2011 11 16 - Vreeman - Corralling Creativity with Standardsdvreeman
The document summarizes Daniel J. Vreeman's presentation at the 9th Forum on Laboratory Informatics on challenges and successes with community-wide laboratory data exchange using standards. The presentation discusses Indiana Network for Patient Care, which connects over 200 healthcare organizations using Logical Observation Identifiers Names and Codes (LOINC) as the standard terminology to facilitate data sharing and interoperability. It highlights successes in public health surveillance and clinical research enabled by the network, and lessons learned around prioritizing trust and iterating systems based on user needs.
2011 09 10 - Maybe is Not a Wary Word - Vreeman - Exploring Future of PTdvreeman
1. The document discusses the future of physical therapy and the need for physical therapists to be part of collaborative, multidisciplinary healthcare teams with the patient as the focus.
2. It advocates for adopting interoperable electronic health records but acknowledges the complexity, and suggests physical therapy education programs incorporate informatics training.
3. The document envisions a future with complete, longitudinal patient information that follows the consumer across settings to facilitate coordinated, value-based care guided by consumer-centered information tools.
2011 08 15 - Clinical LOINC Tutorial - Collections - Panels Forms and Assessm...dvreeman
This document summarizes a presentation on using LOINC (Logical Observation Identifiers Names and Codes) to standardize clinical assessments and patient-reported outcomes. It describes how LOINC provides a model for organizing assessments into hierarchical panels and items with specific attributes. A growing number of standardized assessments are available in LOINC, including government forms, clinical screening tools, and patient-reported outcomes. Lessons learned include the need to minimize variation between similar assessments and start from a uniform data model to avoid discrepancies. IP issues also present challenges for widespread adoption.
This document summarizes a presentation on the clinical document ontology (CDO) developed by LOINC. It describes the origins and development of having a standardized vocabulary for clinical document names, including empirical analysis of local document names. The presentation reviews the multi-axial model used by LOINC for document names, provides examples, and discusses ongoing evaluation and expansion efforts through collaboration. Future directions include further harmonization of CDO terms and analyzing document content.
The document provides an introduction and overview of LOINC (Logical Observation Identifiers Names and Codes), a universal standard for identifying health measurements, observations, and documents. LOINC codes are organized using a six-axis model and include over 55,000 codes for laboratory tests, clinical observations, surveys, and claims attachments. The document outlines the history, development, and governance of LOINC, as well as examples of how LOINC codes are structured and used in clinical documents and messages.
The document provides an overview of the Regenstrief LOINC Mapping Assistant (RELMA) tool. It discusses RELMA's features for installing the tool, setting preferences, loading local observation files, searching for and mapping local terms to LOINC codes, and proposing new LOINC terms. The goal is to help laboratories map their local test names and codes to standardized LOINC codes to improve data interoperability, comparability and quality.
The document provides an introduction and overview of LOINC (Logical Observation Identifiers Names and Codes), including:
- LOINC codes clinical observations and laboratory tests using a six-axis model for consistent naming.
- It has over 55,000 codes covering laboratory tests, clinical observations, surveys, and claims attachments.
- LOINC is maintained by committees and aims to standardize coding of clinical data to facilitate exchange between systems.
This document provides an overview of LOINC codes for diagnostic imaging studies. It discusses the different classes and components of LOINC codes for imaging, including examples for radiology terms, orderable vs observation codes, views and positions, limited vs complete exams, guidance procedures, laterality, and modality subparts. It notes some challenges in coding imaging exams and areas where additional terms need development, such as for PET, interventional radiology, and combination modalities.
This document discusses LOINC's model for standardizing patient assessments and forms. It provides an overview of LOINC's current efforts to represent common health assessments, including various government forms and clinical screening tools. The presentation notes that while these assessments address similar concepts, there is significant variation in how the items are structured between different forms. It recommends starting with LOINC's standardized data model to help address inconsistencies and avoid unnecessary variation. Lessons learned include the high costs of losing comparability and that intellectual property issues pose large challenges for standardization.
The document discusses the origins and ongoing development of a document ontology within LOINC and HL7. It describes how the Clinical Document Ontology (CDO) provides consistent semantics for clinical document names to enable interoperability. The CDO uses a multi-axial model with domains like subject matter, role, setting, type of service, and kind of document. Iterative evaluations have helped expand and refine the CDO. Future work includes further harmonization and expanding the model to new document types.
The LOINC name does not include the instrument used in testing, specific details about the specimen, priority (e.g. STAT), where testing was done, who did the test, test interpretation, or anything else that is not an intrinsic part of the name of the result.
This document summarizes a presentation on the Logical Observation Identifiers Names and Codes (LOINC) database. It discusses the origins and purpose of LOINC as a universal standard for clinical observations. It also provides details on the growth of LOINC over time, its international adoption and translations into multiple languages. Large health organizations in the US and abroad have implemented LOINC to facilitate interoperability and data exchange.
This document provides an overview of LOINC codes for diagnostic imaging studies. It discusses the different classes and components of LOINC codes for imaging, including examples for radiology terms, orderable vs observation codes, views and positions, limited vs complete exams, guidance procedures, laterality, and modality subparts. It notes some challenges in coding imaging exams and areas where additional terms need development, such as for PET, interventional radiology, and combination modalities.
This document discusses standardizing patient assessments in LOINC. It summarizes LOINC's work enhancing its panel model to represent patient assessments, which allows representing individual assessment items, structured answer lists, and item instances within specific assessments. Challenges included variation between similar assessments, starting from paper forms rather than a uniform data model, and intellectual property issues. Ongoing work aims to standardize more assessments in LOINC to improve data sharing.
The document discusses the origins and development of the HL7/LOINC Document Ontology Model. It began with an analysis of over 2000 clinical document names from various healthcare organizations to identify common elements. This led to the creation of a multi-axial model for clinical document names that includes subject matter domain, role, setting, type of service, and kind of document. The model has undergone ongoing evaluation and expansion based on empirical analyses to improve coverage of document names. Future work includes further ontology evolution and refinement.
How to Control Your Asthma Tips by gokuldas hospital.Gokuldas Hospital
Respiratory issues like asthma are the most sensitive issue that is affecting millions worldwide. It hampers the daily activities leaving the body tired and breathless.
The key to a good grip on asthma is proper knowledge and management strategies. Understanding the patient-specific symptoms and carving out an effective treatment likewise is the best way to keep asthma under control.
NAVIGATING THE HORIZONS OF TIME LAPSE EMBRYO MONITORING.pdfRahul Sen
Time-lapse embryo monitoring is an advanced imaging technique used in IVF to continuously observe embryo development. It captures high-resolution images at regular intervals, allowing embryologists to select the most viable embryos for transfer based on detailed growth patterns. This technology enhances embryo selection, potentially increasing pregnancy success rates.
Are you looking for a long-lasting solution to your missing tooth?
Dental implants are the most common type of method for replacing the missing tooth. Unlike dentures or bridges, implants are surgically placed in the jawbone. In layman’s terms, a dental implant is similar to the natural root of the tooth. It offers a stable foundation for the artificial tooth giving it the look, feel, and function similar to the natural tooth.
The skin is the largest organ and its health plays a vital role among the other sense organs. The skin concerns like acne breakout, psoriasis, or anything similar along the lines, finding a qualified and experienced dermatologist becomes paramount.
Breast cancer: Post menopausal endocrine therapyDr. Sumit KUMAR
Breast cancer in postmenopausal women with hormone receptor-positive (HR+) status is a common and complex condition that necessitates a multifaceted approach to management. HR+ breast cancer means that the cancer cells grow in response to hormones such as estrogen and progesterone. This subtype is prevalent among postmenopausal women and typically exhibits a more indolent course compared to other forms of breast cancer, which allows for a variety of treatment options.
Diagnosis and Staging
The diagnosis of HR+ breast cancer begins with clinical evaluation, imaging, and biopsy. Imaging modalities such as mammography, ultrasound, and MRI help in assessing the extent of the disease. Histopathological examination and immunohistochemical staining of the biopsy sample confirm the diagnosis and hormone receptor status by identifying the presence of estrogen receptors (ER) and progesterone receptors (PR) on the tumor cells.
Staging involves determining the size of the tumor (T), the involvement of regional lymph nodes (N), and the presence of distant metastasis (M). The American Joint Committee on Cancer (AJCC) staging system is commonly used. Accurate staging is critical as it guides treatment decisions.
Treatment Options
Endocrine Therapy
Endocrine therapy is the cornerstone of treatment for HR+ breast cancer in postmenopausal women. The primary goal is to reduce the levels of estrogen or block its effects on cancer cells. Commonly used agents include:
Selective Estrogen Receptor Modulators (SERMs): Tamoxifen is a SERM that binds to estrogen receptors, blocking estrogen from stimulating breast cancer cells. It is effective but may have side effects such as increased risk of endometrial cancer and thromboembolic events.
Aromatase Inhibitors (AIs): These drugs, including anastrozole, letrozole, and exemestane, lower estrogen levels by inhibiting the aromatase enzyme, which converts androgens to estrogen in peripheral tissues. AIs are generally preferred in postmenopausal women due to their efficacy and safety profile compared to tamoxifen.
Selective Estrogen Receptor Downregulators (SERDs): Fulvestrant is a SERD that degrades estrogen receptors and is used in cases where resistance to other endocrine therapies develops.
Combination Therapies
Combining endocrine therapy with other treatments enhances efficacy. Examples include:
Endocrine Therapy with CDK4/6 Inhibitors: Palbociclib, ribociclib, and abemaciclib are CDK4/6 inhibitors that, when combined with endocrine therapy, significantly improve progression-free survival in advanced HR+ breast cancer.
Endocrine Therapy with mTOR Inhibitors: Everolimus, an mTOR inhibitor, can be added to endocrine therapy for patients who have developed resistance to aromatase inhibitors.
Chemotherapy
Chemotherapy is generally reserved for patients with high-risk features, such as large tumor size, high-grade histology, or extensive lymph node involvement. Regimens often include anthracyclines and taxanes.
Summer is a time for fun in the sun, but the heat and humidity can also wreak havoc on your skin. From itchy rashes to unwanted pigmentation, several skin conditions become more prevalent during these warmer months.
Travel vaccination in Manchester offers comprehensive immunization services for individuals planning international trips. Expert healthcare providers administer vaccines tailored to your destination, ensuring you stay protected against various diseases. Conveniently located clinics and flexible appointment options make it easy to get the necessary shots before your journey. Stay healthy and travel with confidence by getting vaccinated in Manchester. Visit us: www.nxhealthcare.co.uk
Cell Therapy Expansion and Challenges in Autoimmune DiseaseHealth Advances
There is increasing confidence that cell therapies will soon play a role in the treatment of autoimmune disorders, but the extent of this impact remains to be seen. Early readouts on autologous CAR-Ts in lupus are encouraging, but manufacturing and cost limitations are likely to restrict access to highly refractory patients. Allogeneic CAR-Ts have the potential to broaden access to earlier lines of treatment due to their inherent cost benefits, however they will need to demonstrate comparable or improved efficacy to established modalities.
In addition to infrastructure and capacity constraints, CAR-Ts face a very different risk-benefit dynamic in autoimmune compared to oncology, highlighting the need for tolerable therapies with low adverse event risk. CAR-NK and Treg-based therapies are also being developed in certain autoimmune disorders and may demonstrate favorable safety profiles. Several novel non-cell therapies such as bispecific antibodies, nanobodies, and RNAi drugs, may also offer future alternative competitive solutions with variable value propositions.
Widespread adoption of cell therapies will not only require strong efficacy and safety data, but also adapted pricing and access strategies. At oncology-based price points, CAR-Ts are unlikely to achieve broad market access in autoimmune disorders, with eligible patient populations that are potentially orders of magnitude greater than the number of currently addressable cancer patients. Developers have made strides towards reducing cell therapy COGS while improving manufacturing efficiency, but payors will inevitably restrict access until more sustainable pricing is achieved.
Despite these headwinds, industry leaders and investors remain confident that cell therapies are poised to address significant unmet need in patients suffering from autoimmune disorders. However, the extent of this impact on the treatment landscape remains to be seen, as the industry rapidly approaches an inflection point.
Test bank for karp s cell and molecular biology 9th edition by gerald karp.pdfrightmanforbloodline
Test bank for karp s cell and molecular biology 9th edition by gerald karp.pdf
Test bank for karp s cell and molecular biology 9th edition by gerald karp.pdf
Test bank for karp s cell and molecular biology 9th edition by gerald karp.pdf
Know the difference between Endodontics and Orthodontics.Gokuldas Hospital
Your smile is beautiful.
Let’s be honest. Maintaining that beautiful smile is not an easy task. It is more than brushing and flossing. Sometimes, you might encounter dental issues that need special dental care. These issues can range anywhere from misalignment of the jaw to pain in the root of teeth.
These lecture slides, by Dr Sidra Arshad, offer a simplified look into the mechanisms involved in the regulation of respiration:
Learning objectives:
1. Describe the organisation of respiratory center
2. Describe the nervous control of inspiration and respiratory rhythm
3. Describe the functions of the dorsal and respiratory groups of neurons
4. Describe the influences of the Pneumotaxic and Apneustic centers
5. Explain the role of Hering-Breur inflation reflex in regulation of inspiration
6. Explain the role of central chemoreceptors in regulation of respiration
7. Explain the role of peripheral chemoreceptors in regulation of respiration
8. Explain the regulation of respiration during exercise
9. Integrate the respiratory regulatory mechanisms
10. Describe the Cheyne-Stokes breathing
Study Resources:
1. Chapter 42, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 36, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 13, Human Physiology by Lauralee Sherwood, 9th edition
2. Acknowledgements!
LOINC Development Team!
Kathy Mercer, Jaci Phillips, Ian Pyle, David Baorto,
Fran Brahmi!
!
RELMA Development Team!
John Hook, Mark Fisher, Karen Ahmed, Anandhi
Sowmyan, James Dennis!
!
LOINC Committee!
Supporters: NLM, Regenstrief, CHITREC,
Metropolitan Chicago Healthcare Council,
Chicago Department of Public Health!
3. Overview!
1. Origins of LOINC!
2. LOINC 101!
3. Using RELMA!
4. Searching and Mapping to LOINC!
5. Approaches to Mapping and
Domain-speci"c Help!
6. Other RELMA features!
7. Your turn!
8. Soda or Pop?!
what you see in the order list
!
Lab A! Lab B!
Test Name: Lyme Disease Serology! Test Name: Lyme Disease Antibody!
Measures: B. burgdorferi Ab IgG! Measures: B. burgdorferi Ab IgM!
Method: ELISA! Method: Immune blot!
Scale: quantitative! Scale: qualitative!
e.g.: Titer 1:40! e.g.: Positive!
! !
LOINC Code = 5062-5! LOINC Code = 6321-4!
9. Logical
Observation
Identi"ers
Names and
Codes!
A universal code system that facilitates exchange,
pooling, and processing of results!
10. If an observation is a question
and the observation value is
an answer…
!LOINC provides codes for
!questions
!Other vocabularies provide
!codes for the answers!
11. What is my patient’s
hemoglobin level?!
718-7:Hemoglobin:MCnc:Pt:Bld:Qn!
12. How fast does my
patient usually walk?!
41959-8:Walking speed:Vel:1W^mean:^Patient:Qn:Calculated
13. Indiana Network for Patient Care!
HL7 v.2.X Message!
MSH|^~&|HOSPITAL_A|SAMPLE_HOSPITAL_A|||$YearMonthDay|||||||||||||||
PID|||$patientId$||$patientName$||||||||||||||||||||
LocalCode^LocalName^CodeSystem^LOINCcode^LOINCname^CodeSystem
PV1|||||||$attendingDoctor$||$consultingDoctor$||||||||
OBR|1|||012^CBC/Auto Diff^HSPA^57021-8^CBC W Auto Diff^LN||$reqDate|||||||||
OBX|2|NM|123^WBC^HSP_A^26464-8^Leukocytes [#/volume] in Blood^LN||10.8|K/MM3|||||F|
OBX|3|NM|234^RBC^HSP_A^26453-1^Erythrocytes [#/volume] in Blood^LN||4.82|MIL/MM3|||||F|
OBX|4|NM|345^HGB^HSP_A^718-7^Hemoglobin [Mass/volume] in Blood^LN||15.7|GM/DL|||||F|
OBX|5|NM|456^HCT^HSP_A^20570-8^Hematocrit [Volume Fraction] of Blood^LN||45|%|||||||F|
!"
Message Processor! !"
Institutional Repository!
14. Result with a Coded Value!
Data type of
result (OBX-5) is a This code is This code is
coded element! from LOINC! from SNOMED!
OBX||CE|6609-2^Listeria ID^LN||36094007^L. monocytogenes^SCT
Code identifying this
observation ! Code identifying the
(what are these results? result (L. monocytogenes)!
Listeria culture)!
31. Large Implementations!
SIGA Saúde project !
Canada Health Infoway!
ePSOS!
Assistance publique - Hôpitaux de Paris!
Hong Kong Hospital Authority!
Red Agrolab!
BiTAC!
Lots more…success is often silent!
43. Currently in LOINC!
US Government Forms!
CARE, MDSv2, MDSv3, OASIS B1, OASIS C, RFC!
US Surgeon General’s Family Health Portrait!
Brief Interview for Mental Status (BIMS)!
Confusion Assessment Method (CAM)!
Geriatric Depression Scale (GDS)!
HIV Signs and Symptoms Checklist!
Home Health Care Classi"cation!
howRU!
Living with HIV (LIV-HIV)!
Morse Fall Scale!
OMAHA!
PHQ (9 and 2)!
Quality Audit Marker (QAM)!
!
50. Anatomy of a LOINC Term!
5193-8:Hepatitis B virus surface Ab:ACnc:Pt:Ser:Qn:EIA!
5193-8! LOINC Code!
Hepatitis B virus surface Ab! Component
!
ACnc
! Property Measured!
Pt
! Timing!
Ser
! System!
Qn! Scale!
EIA! Method!
There are six major LOINC axes
!
51. NOT part of a LOINC Name!
Testing instrument!
Speci"c details about the specimen!
Priority (e.g. STAT)!
Where testing was done!
Who did the test!
Test interpretation!
Anything not part of naming the test!
Stu# carried in other parts of HL7 message!
52. Component!
The substance or entity that is measured,
evaluated, or observed!
Sodium!
Glucose!
Brucella sp. organism!
In$uenza A Virus antigen!
Cytomegalovirus antibody!
Lipids.total!
5193-8:Hepatitis B virus surface Ab:ACnc:Pt:Ser:Qn:EIA!
53. Component Structure !
Analyte Name^Challenge^Adjustments
!
Formal analyte name! Calcium!
Specify “subanalytes”! Coronavirus Ag!
May have subclasses! Calcium.ionized!
! !
Challenge! 1H post 100 g Glucose PO!
Two parts separated <time delay>post<challenge type>!
by “post”! !
! !
Adjustments! Adjusted to pH 7.4!
54. Property! * the most di"cult LOINC axis!
The characteristic or attribute of the analyte that
is measured, evaluated, or observed.!
Major Categories:!
!mass!
!substance!
!catalytic activity!
!arbitrary!
!number!
5193-8:Hepatitis B virus surface Ab:ACnc:Pt:Ser:Qn:EIA!
55. Property! * the most di"cult LOINC axis!
Fully Named Properties! mg/dL!
MCnc! mass concentration! umol/L!
SCnc! substance concentration! mg/g!
MCnt! mass content ! U/L!
CCnc! catalytic concentration!
Prid! presence or identity!
Imp! impression!
Type! “kind of”!
Property is related to units of measure!
5193-8:Hepatitis B virus surface Ab:ACnc:Pt:Ser:Qn:EIA!
56. Common Property Issues!
Fraction versus Ratio!
Fraction = Part/Whole!
!NFr: % Eosinophils / leukocytes!
!SFr: % HGB which is A2!
! photo via jurvetson!
Ratio = multiple analytes from same system!
!MCrto: BUN/Creatinine in urine!
!
Relative Ratio = measures from di"erent system!
!RelRto: actual to normal control!
!
57. Timing!
The interval of time over which the observation
or measurement was made!
Pt! point in time!
12H! 12 hour collection!
24H! 24 hour collection!
Non-Pt timings are often !
found with Ratio Property!
58. System!
The system (context) or specimen type upon which the
observation was made.!
Ser! serum!
Ser/Plas! serum or plasma!
Bld! whole blood !
Ur! urine!
Flu! body #uid! photo via AlishaV!
Tiss! tissue!
XXX! speci$ed elsewhere !
5193-8:Hepatitis B virus surface Ab:ACnc:Pt:Ser:Qn:EIA!
59. System Structure!
System^Super System!
Super System!
Patient is the default!
Used to indicate!
blood product unit!
bone marrow donor!
fetus!
photo via Xurble!
818-5:A Ag:ACnc:Pt:RBC^BPU:Ord: !
11670-7:Blood $ow.mean:Vel:Pt:Aortic arch^fetus:Qn:US.doppler
!
61. Method!
Only needed if interpretation a#ected!
Di#erent normal ranges!
Test sensitivity!
Listed at the generic level!
Agglutination!
Immunoassay!
Probe with target ampli"cation!
5193-8:Hepatitis B virus surface Ab:ACnc:Pt:Ser:Qn:EIA
62. LOINC Parts !
Uses !
translation, synonymy, building hierarchies,
creating display names, linking descriptions!
66. What’s it Good For?!
Browse LOINC!
Map your local terms to LOINC!
!import/export!
!translate local words to LOINC-speak!
!manual/automated mapping!
70. Search Syntax!
Google-like!
AND, OR, -<word>, +<word>!
Limit by category (e.g. micro)!
Wildcards (? and *)!
!
Assumes EXACT MATCH
! unless you use wildcards!
71. More RELMA Features !
User Speci"ed Search Limits !
Selectable trees for:!
Class!
Multiaxial (component/system)!
System (specimen)!
Component!
Method!
73. Installation Steps!
Need Windows (or a VM on a Mac)!
Got disk space? 2Gb is recommended!
Start installer <drive>:RELMASetup!
Specify installation directory keep default!
Two database "les installed!
RELMA.MDB – LOINC terms database!
LMOF3.MDB – Local Master Observation File!
Two sample "les included !
Run it!!
74. File Locations!
Database and Ancillary Files!
Windows XP!
!
C:Documents and SettingsAll UsersDocumentsRELMA
Windows Vista and 7!
!C:UsersPublicDocumentsRELMA
!
Sample "les!
Windows XP!
!
C:Documents and SettingsAll UsersDocumentsRELMASamples
Windows Vista and 7!
!C:UsersPublicDocumentsRELMASamples
!
90. 4 Ways to Build LMOF!
1. Hand enter! bad!
2. Make your own table ! bad!
3. Import text "le from good!
your test catalog! !
4. Import from real HL7 best!
v2.x messages !
91. Make a Local Dataset!
Extract from Your Test Catalog!
Battery code!
Battery description or name!
*Local code !
*Test description or name!
Include method if important!
Units!
Example Values!
Laboratory Section!
92. Make a Delimited Text File!
Need separate "elds!
Use any delimiter!
Fields in any order!
Required "elds (minimum):!
Local Code!
Local Description!
Units (highly recommended)!
93. Importing Local Terms!
1. Start it up!
2. Find local term "le!
C:Documents and SettingsAll UsersDocumentsRELMASamples
C:UsersPublicDocumentsRELMASamples
3. Name your working set!
4. Identify "eld delimiter in your "le!
5. Match your "elds to LMOF
attributes!
6. Import!!
111. Prep Your Data for Mapping!
Improve mapping success!
!Expand abbreviations!
!Ignore “administrative” words!
!Standardize idiosyncratic words!
!Standardize time references!
RELMA can help!
147. View LOINC Term Details!
View details for a
speci"c LOINC Term !
Right clicking on a LOINC
term brings up a Task Menu !
148. LOINC Term Details!
Can scroll down a single
formatted page !
Can scroll through
Change to expanded returned subset of
details view! terms !
Change text size!
178. A Few Tests Give Most Results!
100%!
Cumulative Laboratory Observation Volume (%)!
90%!
80%!
70%!
60%!
50%!
40%!
30%!
20%!
10%!
0%!
0! 500! 1000! 1500! 2000! 2500! 3000! 3500! 4000!
Number of Laboratory Observation Codes (N)!
Vreeman DJ, Finnell JT, Overhage JM. A Rationale for Parsimonious Laboratory Term Mapping by Frequency. AMIA Annu Symp Proc. 2007;:771-775.
!
179.
180. Setting Search Limits!
include Trial, Deprecated or
Discouraged LOINC codes with
returned terms !
181. LOINC Term Status!
Trial!
! as the concept and associated attributes may change.
Concept is experimental in nature. Use with caution
Discouraged!
! mappings to this concept are discouraged although
Concept is not recommended for current use. New
existing mappings may continue to be valid in
! context.
Deprecated!
Concept is retired and should not be used, but it is
retained in LOINC for historical purposes.
182. Include Deprecated LOINC Terms!
Can’t map to a deprecated term!
Warning before mapping to discouraged term!
Discouraged LOINC Terms appear as an
inverted triangle!
Deprecated LOINC Terms appear as strikethrough text
with a “Do Not” symbol!
183. LOINC Hierarchies – Class Tree!
Three top-level
branches !
Tree Navigation
Buttons!
191. Hierarchy Wrap-up!
All trees operate the same way!
All grids operate the same way!
Tree is searchable !
Combine tree limits with others!
“Mulligan” button!
192. Search Hints and Tips!
Zero hit keywords are ignored!
!(may need to rephrase)!
!
Common causes of Zero results!
!Too many keywords!
!Limits and keyword contradictions!
!Local term weirdness!
!
Units are GREAT discriminators!
194. What Does It Do?!
Batch processes local term "le to get
N- closest candidate LOINCs!
!
Uses words and units from local terms!
!
Produces a ranked list for your review!
195. Recent Improvements!
Faster. Way faster.!
Better clean-up routine!
New specimen guesser!
Better local to LOINC word "nder!
Break ties with frequency data!
196. Does it Work?!
Lab terms (internal analysis)!
!#1 ranking: 60-70ish%!
!In Top 5: 70-80ish%!
!
Radiology terms!
!#1 ranking: 80-90ish%!
!In Top 5: 90ish%!
!Using CPT-based restriction helped!
!(but we couldn’t distribute it)!
Vreeman DJ, McDonald CJ. A Comparison of Intelligent Mapper and Document Similarity Scores
for Mapping Local Radiology Terms to LOINC. Proc AMIA Symp. 2006;809-813.!
!
Vreeman DJ and McDonald CJ. Automated Mapping of Local Radiology Terms to LOINC. Proc
AMIA Symp. 2005;769-773.!
197. (Lab) Auto Mapper!
launch it here!
a couple of options
!
print report or work through
the ranked list when done !
203. Additional Pearls and
Guidance!
Clem McDonald, MD!
Director, Lister Hill National Center for Biomedical Communications – National Library of Medicine !
204. Other RELMA ®
Features!
Oh yes, there’s more!!
205. Using Your Mapped Terms!
You’ve done the work. Now reap the glory.!
photo via janeandd!
225. Proposing a New Term!
Are you sure?!
Give us all you got!
RELMA is best!
Send them in groups!
226. What We Need!
• Local test/observation name!
• Local order (panel) name!
• Description of the test !
• Name of send out lab (if applicable)!
• Name of healthcare organization that stimulated the request for
this term (if you are submitting on behalf of someone else)!
• Units of measure (for quantitative observations)!
• Answer lists (for qualitative observations)!
• Sample results, reports (if applicable)!
• Package inserts, test kit documentation (if applicable)!
• Vendor, instrument, and/or reagent kit used to perform this test (if
applicable)!
• Description of the project or activity that stimulated the request
for this term, or any other documentation you have!
232. Review Proposed Terms!
Choose whether to
send or postpone!
All proposed terms
fully editable!
233. Submitting New Terms!
Still need to email us the "le!
!submissions@loinc.org!
!
More Information!
!http://loinc.org/submissions!
!
LOINC Submissions Policy!
!http://loinc.org/submissions-policy!
!
!!