The document proposes a multi-stage query language for medical document repositories to enable more effective retrieval of medical information. It involves transforming repositories into a user-level schema with understandable attributes. This would allow users to perform complex, granular queries and receive specific result segments matching their criteria rather than broad lists of documents. The approach aims to support both medical experts needing precise information and novice users with less domain knowledge.
Privacy and Publication: challenges and opportunities for clinical dataVarsha Khodiyar
Varsha Khodiyar discusses challenges and opportunities for sharing clinical trial data. Withholding data impacts human health by allowing reporting bias. There is increasing support for data transparency from funders, regulators, legislation, and journals. Journals are implementing stronger data sharing policies like mandating open data submission. While researchers generally support sharing de-identified data through repositories, concerns around patient privacy and inappropriate secondary analysis remain. Better solutions involve data use agreements and controlled access environments for requesting and reviewing data. Repositories should provide robust mechanisms for hosting and managing access to non-public clinical data.
This document discusses the development of the DATS (Data Tag Suite), which is needed for DataMed to index data sources in a scalable way, similar to how JATS indexes literature for PubMed. The DATS model was developed through a community-driven process involving use cases and existing metadata schemas. It includes core and extended elements to describe datasets and other digital research objects. The model is designed around the dataset entity and serialized in JSON and JSON-LD mapped to schema.org to increase visibility, accessibility, and searchability. Efforts are ongoing to further align DATS with schema.org and integrate it with related metadata standards and tools.
- The document discusses Harry Hochheiser's research in translational bioinformatics and challenges in data sharing.
- It describes the FaceBase project, which aims to compile biological data related to craniofacial development across multiple organisms and datasets.
- Effective data sharing is challenging due to the diversity of data types and projects involved; metadata and ontologies could help but have not been fully leveraged.
Providing quality information to doctors in remote areas is essential for better healthcare. Inadequate infrastructure poses a challenge to doctors. As their practice is isolated there is very limited access to quality information. Taking help of their colleagues for second opinion is difficult because of their availability and distance. As life of a patient is in danger, information regarding possible diagnosis becomes crucial. In absence of a solution in existing circumstances, doctor and patient suffer.
By leveraging existing internet and mobile technologies one can access high end medical applications. Our mobile app gives diagnostic data and necessary tests for any given patient data. It also has a search engine that retrieves clinical data from web. As the Natural language processing (NLP) is developed using standard medical database (snomed), the search engine yields results better than standard search engine like google. App can be activated by voice, direct text or electronic health record and gives critical diagnostic information to the clinician. The Diagnostic Decision Support System (DDS) is based on methods developed specifically for medical diagnostic domain by experts. By using our DDS one can minimize medical errors and hence treatment costs. The system works over internet and can be accessed remotely through computer or smart phone. The app acts as a colleague for the doctor. It works as a second opinion also.
Subsumptive reflection in SNOMED CT: a large description logic-based terminology for diagnosis
http://arxiv.org/abs/1512.03516
Identifying and tracking research resources using RRIDs: a practical approachdkNET
At this presentation, you will learn (1) Why you need to use Research Resource identifier (RRID) (2) What is Resource Identification Initiative (3) How dkNET.org supports RRID (4) What can you do with RRID
Providing quality information to doctors in remote areas is essential for better healthcare. Inadequate infrastructure poses a challenge to doctors. As their practice is isolated there is very limited access to quality information. Taking help of their colleagues for second opinion is difficult because of their availability and distance. As life of a patient is in danger, information regarding possible diagnosis becomes crucial. In absence of a solution in existing circumstances, doctor and patient suffer.
By leveraging existing internet and mobile technologies one can access high end medical applications. Our mobile app gives diagnostic data and necessary tests for any given patient data. It also has a search engine that retrieves clinical data from web. As the Natural language processing (NLP) is developed using standard medical database (snomed), the search engine yields results better than standard search engine like google. App can be activated by voice, direct text or electronic health record and gives critical diagnostic information to the clinician. The Diagnostic Decision Support System (DDS) is based on methods developed specifically for medical diagnostic domain by experts. By using our DDS one can minimize medical errors and hence treatment costs. The system works over internet and can be accessed remotely through computer or smart phone. The app acts as a colleague for the doctor. It works as a second opinion also.
Subsumptive reflection in SNOMED CT: a large description logic-based terminology for diagnosis
http://arxiv.org/abs/1512.03516
Privacy and Publication: challenges and opportunities for clinical dataVarsha Khodiyar
Varsha Khodiyar discusses challenges and opportunities for sharing clinical trial data. Withholding data impacts human health by allowing reporting bias. There is increasing support for data transparency from funders, regulators, legislation, and journals. Journals are implementing stronger data sharing policies like mandating open data submission. While researchers generally support sharing de-identified data through repositories, concerns around patient privacy and inappropriate secondary analysis remain. Better solutions involve data use agreements and controlled access environments for requesting and reviewing data. Repositories should provide robust mechanisms for hosting and managing access to non-public clinical data.
This document discusses the development of the DATS (Data Tag Suite), which is needed for DataMed to index data sources in a scalable way, similar to how JATS indexes literature for PubMed. The DATS model was developed through a community-driven process involving use cases and existing metadata schemas. It includes core and extended elements to describe datasets and other digital research objects. The model is designed around the dataset entity and serialized in JSON and JSON-LD mapped to schema.org to increase visibility, accessibility, and searchability. Efforts are ongoing to further align DATS with schema.org and integrate it with related metadata standards and tools.
- The document discusses Harry Hochheiser's research in translational bioinformatics and challenges in data sharing.
- It describes the FaceBase project, which aims to compile biological data related to craniofacial development across multiple organisms and datasets.
- Effective data sharing is challenging due to the diversity of data types and projects involved; metadata and ontologies could help but have not been fully leveraged.
Providing quality information to doctors in remote areas is essential for better healthcare. Inadequate infrastructure poses a challenge to doctors. As their practice is isolated there is very limited access to quality information. Taking help of their colleagues for second opinion is difficult because of their availability and distance. As life of a patient is in danger, information regarding possible diagnosis becomes crucial. In absence of a solution in existing circumstances, doctor and patient suffer.
By leveraging existing internet and mobile technologies one can access high end medical applications. Our mobile app gives diagnostic data and necessary tests for any given patient data. It also has a search engine that retrieves clinical data from web. As the Natural language processing (NLP) is developed using standard medical database (snomed), the search engine yields results better than standard search engine like google. App can be activated by voice, direct text or electronic health record and gives critical diagnostic information to the clinician. The Diagnostic Decision Support System (DDS) is based on methods developed specifically for medical diagnostic domain by experts. By using our DDS one can minimize medical errors and hence treatment costs. The system works over internet and can be accessed remotely through computer or smart phone. The app acts as a colleague for the doctor. It works as a second opinion also.
Subsumptive reflection in SNOMED CT: a large description logic-based terminology for diagnosis
http://arxiv.org/abs/1512.03516
Identifying and tracking research resources using RRIDs: a practical approachdkNET
At this presentation, you will learn (1) Why you need to use Research Resource identifier (RRID) (2) What is Resource Identification Initiative (3) How dkNET.org supports RRID (4) What can you do with RRID
Providing quality information to doctors in remote areas is essential for better healthcare. Inadequate infrastructure poses a challenge to doctors. As their practice is isolated there is very limited access to quality information. Taking help of their colleagues for second opinion is difficult because of their availability and distance. As life of a patient is in danger, information regarding possible diagnosis becomes crucial. In absence of a solution in existing circumstances, doctor and patient suffer.
By leveraging existing internet and mobile technologies one can access high end medical applications. Our mobile app gives diagnostic data and necessary tests for any given patient data. It also has a search engine that retrieves clinical data from web. As the Natural language processing (NLP) is developed using standard medical database (snomed), the search engine yields results better than standard search engine like google. App can be activated by voice, direct text or electronic health record and gives critical diagnostic information to the clinician. The Diagnostic Decision Support System (DDS) is based on methods developed specifically for medical diagnostic domain by experts. By using our DDS one can minimize medical errors and hence treatment costs. The system works over internet and can be accessed remotely through computer or smart phone. The app acts as a colleague for the doctor. It works as a second opinion also.
Subsumptive reflection in SNOMED CT: a large description logic-based terminology for diagnosis
http://arxiv.org/abs/1512.03516
Using Open Science to accelerate advancements in auditory EEG signal processingRobert Oostenveld
In this presentation at the AESoP conference in Leuven, I will provide arguments for more open research methods. Open Science and Open Data is not only expected from us by our funding agencies, but actually starts making more and more sense from the perspective of the individual researchers. Specifically, I will introduce BIDS as new initiative to organize and share EEG data.
An Approach to Combining Disparate Clinical Study Data across Multiple Sponso...imgcommcall
1. The document describes a process for combining clinical study data from 12 prostate cancer studies spanning 20+ years and 7 sponsors that were participating in Project Data Sphere.
2. The key steps involved identifying domains of interest (labs, adverse events, demography), reviewing and mapping the raw data, programming the mapping, reviewing data quality, and combining the data sets.
3. The final harmonized data sets included over 8,000 subjects in the demography domain, over 1 million observations in the labs domain, and over 127,000 observations in the adverse events domain.
Compliance: Data Management Plans and Public Access to DataMargaret Henderson
Presented at The 8th Annual University of Massachusetts and New England Area Librarian e-Science Symposium, Wednesday, April 6, 2016
University of Massachusetts Medical School
Leveraging publication metadata to help overcome the data ingest bottleneck Todd Vision
This document discusses leveraging publication metadata to help address the data ingest bottleneck in scientific publishing. It proposes integrating data submission with manuscript submission to journals to make data archiving integral to the publication process. This integrated approach would help overcome issues around orphan data and allow linking of publications to underlying data through identifiers. Benefits include increased data findability, reuse, and credit to data creators. Challenges include gaining widespread adoption among journals and developers.
Ontology-Driven Clinical Intelligence: Removing Data Barriers for Cross-Disci...Remedy Informatics
The presentation describes how Remedy Informatics is advocating and innovating "flexible standardization" through an ontology-driven approach to clinical research. You will see in greater detail how a foundational, standardized Mosaic Ontology can be extended for more specific research applications and even more specific and focused disease research.
The document discusses challenges and opportunities presented by big medical data and describes an approach using in-memory technology. It proposes a medical knowledge cockpit that allows interactive exploration of distributed medical data sources. This would facilitate tasks like identifying relevant information for a patient's genes, finding suitable clinical trials, and interactively analyzing drug response data. The goal is to enable personalized medicine through real-time analysis of medical data from various sources.
The document summarizes the bioCADDIE team's work developing the DATS (Data Tag Suite) metadata model. It describes the iterative development process including collecting use cases, mapping existing schemas, and refining the model. The key features of the DATS model include a set of core and extended metadata elements with defined properties, definitions, and allowed values. Core elements are generic while extended elements include domain-specific elements. Serializations include JSON and JSON-LD using schema.org vocabulary. The goal is to enable scalable discovery and access to datasets.
Slides of the 2015 Bio Data World Congress show how our analyzegenomes.com services are combined to support precision medicine in the context of modern oncology treatment.
UCSF Informatics Day 2014 - Ida Sim, "Informatics Technologies: From a Data-C...CTSI at UCSF
This document discusses moving from a data-centric to a hypothesis-centric view of clinical and translational research using electronic health records and other informatics technologies. It notes that most current research is observational rather than interventional, and outlines ways informatics could better support hypothesis testing through virtual, community-based, and point-of-care clinical trials by integrating risk calculators, structured note templates, surveys, and other tools directly into clinical workflows and patient portals. The presentation calls for further developing these informatics capabilities to facilitate more interventional research at lower cost.
The document discusses distributing clinical decision support using the FHIR Clinical Reasoning Module. It describes implementing opioid prescribing guidelines as an example, including defining relevant terminology, calculating morphine milligram equivalents, and executing the logic through a CQL engine. Key benefits include sharing decision support content and allowing different systems to execute the same logic. Challenges include customizing support for local settings and integrating with existing EHRs.
DataTags, The Tags Toolset, and Dataverse IntegrationMichael Bar-Sinai
This presentation describes the concept of DataTags, which simplifies handling of sensitive datasets. It then shows the Tags toolset, and how it is integrated with Dataverse, Harvard's popular dataset repository.
The DataTags System: Sharing Sensitive Data with ConfidenceMerce Crosas
This talk was part of a session at the Research Data Alliance (RDA) 8th Plenary on Privacy Implications of Research Data Sets, during International Data Week 2016:
https://rd-alliance.org/rda-8th-plenary-joint-meeting-ig-domain-repositories-wg-rdaniso-privacy-implications-research-data
Slides in Merce Crosas site:
http://scholar.harvard.edu/mercecrosas/presentations/datatags-system-sharing-sensitive-data-confidence
How to Comply with Grants: Writing Data Management Plans and Providing Public...Margaret Henderson
This document provides an overview of federal data management plan and public access requirements. It discusses what constitutes research data and outlines what must be included in a data management plan. It then reviews policies from agencies such as NIH, NSF, DOD and others regarding submitting publications to public repositories and making data publicly available. The policies generally require making peer-reviewed publications open access within 12 months of publication and providing a plan for sharing and preserving research data. Noncompliance may result in withholding of funds.
Ontology-Driven Clinical Intelligence: A Path from the Biobank to Cross-Disea...Remedy Informatics
The discovery of clinical insights through effective management and reuse of data requires several conditions to be optimized: Data need to be digital, data need to be structured, and data need to be standardized in terms of metadata and ontology. This presentation describes a bioinformatics system that combines a next-generation biobank management model mapped to applicable international standards and guidelines with a master ontology that controls all input and output and is able to add unique properties to meet the specialized needs of clinicians for cross-disease research.
Inroads into Data: Getting Involved in Data at Your InstitutionMargaret Henderson
Every institution creates and uses data for many reasons. Data needs to be collected, described, stored, organized, retrieved, and shared, all things that librarians can help with. But how do you get started when there are many types of data and a range of services that can be offered? I will cover how to leverage the skills librarians already have to work with data and suggest some areas of data and service to get you started.
KConnect - making Medical Information Easier to Find: Semantic Annotation and...Peter Voisey
Outline of the work Findwise is doing in the KConnect EU innovation project and how linked data is used. It includes information about the KConnect technologies (including semantic annotation and semantic search) that make medical information easier to find - whatever it's type e.g. EHRs/EMRs, medical literature, guidelines etc.
How to conduct_a_systematic_or_evidence_reviewEaglefly Fly
This document provides guidance on conducting a systematic or evidence-based literature review. It discusses defining search terms, identifying relevant articles through database searches and other methods, applying inclusion/exclusion filters to evaluate articles, synthesizing results, and summarizing the evidence found to determine the best intervention. The goal is to reduce bias and provide a comprehensive review of a topic through an explicit and transparent process.
The slides that will accompany my live webcast for OpenCon 2014 attendees, all about open data in research. The benefits, the how to (both legally & technically), examples, pitfalls, and the future of open research data.
1. The document summarizes Kerstin Forsberg's presentation on semantics and linked data at AstraZeneca R&D. It discusses (1) an internal competitive intelligence tool called CI360, (2) public pre-competitive projects like Open PHACTS and standards bodies, (3) AstraZeneca's Linked Data Community of Practice, and (4) ongoing work on study identifiers and APIs.
2. It provides an overview of Kerstin Forsberg's background and goal of improving the utility of clinical trial data through semantic interoperability. It also outlines some of AstraZeneca's collaborations and contributions to linked data initiatives.
3. The presentation highlights AstraZeneca
Aggregating Educational Data for Patient EmpowermentCARRE project
N. Portokallidis, G. Drosatos, E. Kaldoudi, Aggregating Educational Data for Patient Empowerment, ELEVIT 2015: 6th Panhellenic Conference on Biomedical Technology, p. 79, Athens, Greece, 6-8 May 2015
Using Open Science to accelerate advancements in auditory EEG signal processingRobert Oostenveld
In this presentation at the AESoP conference in Leuven, I will provide arguments for more open research methods. Open Science and Open Data is not only expected from us by our funding agencies, but actually starts making more and more sense from the perspective of the individual researchers. Specifically, I will introduce BIDS as new initiative to organize and share EEG data.
An Approach to Combining Disparate Clinical Study Data across Multiple Sponso...imgcommcall
1. The document describes a process for combining clinical study data from 12 prostate cancer studies spanning 20+ years and 7 sponsors that were participating in Project Data Sphere.
2. The key steps involved identifying domains of interest (labs, adverse events, demography), reviewing and mapping the raw data, programming the mapping, reviewing data quality, and combining the data sets.
3. The final harmonized data sets included over 8,000 subjects in the demography domain, over 1 million observations in the labs domain, and over 127,000 observations in the adverse events domain.
Compliance: Data Management Plans and Public Access to DataMargaret Henderson
Presented at The 8th Annual University of Massachusetts and New England Area Librarian e-Science Symposium, Wednesday, April 6, 2016
University of Massachusetts Medical School
Leveraging publication metadata to help overcome the data ingest bottleneck Todd Vision
This document discusses leveraging publication metadata to help address the data ingest bottleneck in scientific publishing. It proposes integrating data submission with manuscript submission to journals to make data archiving integral to the publication process. This integrated approach would help overcome issues around orphan data and allow linking of publications to underlying data through identifiers. Benefits include increased data findability, reuse, and credit to data creators. Challenges include gaining widespread adoption among journals and developers.
Ontology-Driven Clinical Intelligence: Removing Data Barriers for Cross-Disci...Remedy Informatics
The presentation describes how Remedy Informatics is advocating and innovating "flexible standardization" through an ontology-driven approach to clinical research. You will see in greater detail how a foundational, standardized Mosaic Ontology can be extended for more specific research applications and even more specific and focused disease research.
The document discusses challenges and opportunities presented by big medical data and describes an approach using in-memory technology. It proposes a medical knowledge cockpit that allows interactive exploration of distributed medical data sources. This would facilitate tasks like identifying relevant information for a patient's genes, finding suitable clinical trials, and interactively analyzing drug response data. The goal is to enable personalized medicine through real-time analysis of medical data from various sources.
The document summarizes the bioCADDIE team's work developing the DATS (Data Tag Suite) metadata model. It describes the iterative development process including collecting use cases, mapping existing schemas, and refining the model. The key features of the DATS model include a set of core and extended metadata elements with defined properties, definitions, and allowed values. Core elements are generic while extended elements include domain-specific elements. Serializations include JSON and JSON-LD using schema.org vocabulary. The goal is to enable scalable discovery and access to datasets.
Slides of the 2015 Bio Data World Congress show how our analyzegenomes.com services are combined to support precision medicine in the context of modern oncology treatment.
UCSF Informatics Day 2014 - Ida Sim, "Informatics Technologies: From a Data-C...CTSI at UCSF
This document discusses moving from a data-centric to a hypothesis-centric view of clinical and translational research using electronic health records and other informatics technologies. It notes that most current research is observational rather than interventional, and outlines ways informatics could better support hypothesis testing through virtual, community-based, and point-of-care clinical trials by integrating risk calculators, structured note templates, surveys, and other tools directly into clinical workflows and patient portals. The presentation calls for further developing these informatics capabilities to facilitate more interventional research at lower cost.
The document discusses distributing clinical decision support using the FHIR Clinical Reasoning Module. It describes implementing opioid prescribing guidelines as an example, including defining relevant terminology, calculating morphine milligram equivalents, and executing the logic through a CQL engine. Key benefits include sharing decision support content and allowing different systems to execute the same logic. Challenges include customizing support for local settings and integrating with existing EHRs.
DataTags, The Tags Toolset, and Dataverse IntegrationMichael Bar-Sinai
This presentation describes the concept of DataTags, which simplifies handling of sensitive datasets. It then shows the Tags toolset, and how it is integrated with Dataverse, Harvard's popular dataset repository.
The DataTags System: Sharing Sensitive Data with ConfidenceMerce Crosas
This talk was part of a session at the Research Data Alliance (RDA) 8th Plenary on Privacy Implications of Research Data Sets, during International Data Week 2016:
https://rd-alliance.org/rda-8th-plenary-joint-meeting-ig-domain-repositories-wg-rdaniso-privacy-implications-research-data
Slides in Merce Crosas site:
http://scholar.harvard.edu/mercecrosas/presentations/datatags-system-sharing-sensitive-data-confidence
How to Comply with Grants: Writing Data Management Plans and Providing Public...Margaret Henderson
This document provides an overview of federal data management plan and public access requirements. It discusses what constitutes research data and outlines what must be included in a data management plan. It then reviews policies from agencies such as NIH, NSF, DOD and others regarding submitting publications to public repositories and making data publicly available. The policies generally require making peer-reviewed publications open access within 12 months of publication and providing a plan for sharing and preserving research data. Noncompliance may result in withholding of funds.
Ontology-Driven Clinical Intelligence: A Path from the Biobank to Cross-Disea...Remedy Informatics
The discovery of clinical insights through effective management and reuse of data requires several conditions to be optimized: Data need to be digital, data need to be structured, and data need to be standardized in terms of metadata and ontology. This presentation describes a bioinformatics system that combines a next-generation biobank management model mapped to applicable international standards and guidelines with a master ontology that controls all input and output and is able to add unique properties to meet the specialized needs of clinicians for cross-disease research.
Inroads into Data: Getting Involved in Data at Your InstitutionMargaret Henderson
Every institution creates and uses data for many reasons. Data needs to be collected, described, stored, organized, retrieved, and shared, all things that librarians can help with. But how do you get started when there are many types of data and a range of services that can be offered? I will cover how to leverage the skills librarians already have to work with data and suggest some areas of data and service to get you started.
KConnect - making Medical Information Easier to Find: Semantic Annotation and...Peter Voisey
Outline of the work Findwise is doing in the KConnect EU innovation project and how linked data is used. It includes information about the KConnect technologies (including semantic annotation and semantic search) that make medical information easier to find - whatever it's type e.g. EHRs/EMRs, medical literature, guidelines etc.
How to conduct_a_systematic_or_evidence_reviewEaglefly Fly
This document provides guidance on conducting a systematic or evidence-based literature review. It discusses defining search terms, identifying relevant articles through database searches and other methods, applying inclusion/exclusion filters to evaluate articles, synthesizing results, and summarizing the evidence found to determine the best intervention. The goal is to reduce bias and provide a comprehensive review of a topic through an explicit and transparent process.
The slides that will accompany my live webcast for OpenCon 2014 attendees, all about open data in research. The benefits, the how to (both legally & technically), examples, pitfalls, and the future of open research data.
1. The document summarizes Kerstin Forsberg's presentation on semantics and linked data at AstraZeneca R&D. It discusses (1) an internal competitive intelligence tool called CI360, (2) public pre-competitive projects like Open PHACTS and standards bodies, (3) AstraZeneca's Linked Data Community of Practice, and (4) ongoing work on study identifiers and APIs.
2. It provides an overview of Kerstin Forsberg's background and goal of improving the utility of clinical trial data through semantic interoperability. It also outlines some of AstraZeneca's collaborations and contributions to linked data initiatives.
3. The presentation highlights AstraZeneca
Aggregating Educational Data for Patient EmpowermentCARRE project
N. Portokallidis, G. Drosatos, E. Kaldoudi, Aggregating Educational Data for Patient Empowerment, ELEVIT 2015: 6th Panhellenic Conference on Biomedical Technology, p. 79, Athens, Greece, 6-8 May 2015
This document discusses a project that aggregates educational data from multiple sources to empower patients. It retrieves educational objects from sources like MedlinePlus and Wikipedia. Experts and patients then rate and annotate the objects. The project has a component-based architecture that extracts metadata and enriches it before storing it on the CARRE server. It can provide personalized recommendations on educational material based on a patient's diagnosis or health status. Examples are given of recommending information on acute kidney disease and comparing sources on diabetes. The majority of educational material comes from Wikipedia. An open source implementation includes a frontend aggregator and backend annotator.
The document provides information about a research methodology workshop including defining research, the different types of research, and the steps involved in designing and conducting research. It discusses selecting a research topic based on criteria like relevance, feasibility, and ethics. It also covers literature searching strategies, sources for medical information online, and tips for effective internet usage for research purposes.
Making Your Data More Valuable to the People Who Could Use It Most
How can you turn your data into a valuable information service? Darrell Gunter will address this important issue in the context of its customer, Asklepios Group, which operates hospitals and highly specialized rehabilitation clinics in Europe and the United States. Asklepios struggled to share its knowledge across a variety of locations. On-duty doctor must be able to search and access information across a breadth of specialties quickly and efficiently. Its network of health care specialists doesn't equate to a community. The answer is to develop a system that provides doctors with virtual-knowledge services when treating patients, thus improving the level of care to patients. Darrell then discusses how you can make your data valuable to the people who need it most.
Stratergies for the intergration of information (IPI_ConfEX)Ben Gardner
The document discusses approaches to integrating internal and external data across pharmaceutical research. It describes utilizing a data warehousing strategy through a Research Information Factory (RIF) to create a single global repository for research data. However, integrating external data from various sources poses additional challenges. Tools like PharmaMatrix provide a pre-indexed mine of scientific literature linking drug targets to indications, but result sets can be large. The document suggests that Web 2.0 technologies like wikis, blogs and tagging could help turn integrated information into knowledge by enabling collaboration and sharing. Industry-wide data standards and common ontologies would also help facilitate external data integration.
Metadata for Data Rescue and Data at RiskNico Carver
1. The document discusses the design of a metadata scheme for describing data that is at risk of being lost, unused, or destroyed.
2. It outlines the major questions and principles that informed the design, including what essential metadata is needed to aid in data rescue efforts across scientific disciplines.
3. The proposed metadata scheme is described which includes elements like research area, physical form of data, content and context, current holder, dates, and risk level. A case study testing the scheme is also summarized.
What are today's challenges of big medical data and how can we use the immense data to turn it into potentials, e.g. for precision medicine. Get insights in application examples, where big medical data are incorporated and how in-memory database technology can enable it instantaneous analysis.
Effective Literature Searching: A Medical Informatics ExampleLydiaWitman
The document outlines the steps for conducting an effective literature search: 1) Formulate a research question, 2) Gather relevant sources such as bibliographic databases and gray literature, 3) Appraise the sources, evaluating quality and conflicts of interest, and 4) Formulate an answer based on the findings. It then provides an example search on whether barcoding and RFID can increase hospital patient safety. The search yielded over 100 sources, with most studies finding benefits but also limitations and cautions. The conclusion is that both technologies show promise when used in combination with other systems, but require implementation support and ongoing monitoring to reduce errors.
This document discusses FDA's efforts to standardize regulatory data submissions through the use of data standards and the Janus initiative. It provides background on the large and growing volumes of submissions received by FDA. It then describes the challenges of disparate data formats and lack of standards. The vision is outlined for a standardized approach using exchange standards like SDTM and terminology standards. The Janus initiative aims to create a centralized data infrastructure within FDA to improve management and analysis of scientific data submissions and help modernize the review process.
Information overload for communities of practiceMurray Turoff
A study of emergency management professionals with emphasis on medical and public health done for NLM. These are slides of a paper presented at Web2008 during ICIS 2008 and you can request a copy of the paper from me directly as well as other work in this area. Check my website for the full NLM report
Paper was presented at European Survey Research Association 2013, in the session Research Data Management for Re-use: Bringing Researchers and Archivists closer.
Mind the Gap: Reflections on Data Policies and PracticeLizLyon
UKOLN is supported by the Mind the Gap project which reflects on data policies and practices. The document discusses the current state of data practices in institutions, challenges around open science and data sharing, and the need for improved data policies, planning tools, and codes of conduct to help researchers with issues like data storage, sharing, and long-term preservation. It also explores how emerging technologies and areas like genomics, personalized medicine, and citizen science will impact future data practices and policies.
The document provides an overview of the Research Capability Programme (RCP) which aims to enable use of NHS data for research purposes. It discusses the RCP's enabling phase where governance structures and stakeholder engagement were established. The implementation phase will develop infrastructure to provide research support services including access to data sources, cohort management, and anonymization/coding of data. Key challenges include ensuring opportunities are maximized, improving data linkage and quality, and navigating complex information governance issues.
Bringing Clinical Guidelines to the Point of Care with HITgueste165460
Describes how health information technology can be used to bring clinical practice guidelines to the point of care. Compares approaches of "intelligent designers" and "adaptive agents". Presented at the MN Health Services Research Conference, March 2009
Similar to Domain-specific Multi-stage Query Language for Medical Document Repositories (20)
This document provides an overview of the components that make up OpenEHR-based electronic health records (EHRs). It lists general download links for all OpenEHR components and the ADL Workbench archetype editor. It also describes the relationships between the Archetype Definition Language (ADL), which is used to define archetypes, and the resulting XML instances, as well as tools for parsing, editing, and publishing archetypes and ADL.
Risk and Credentials based Access ControlAastha Madaan
This document proposes a risk-aware integrity management framework for distributed healthcare systems. It integrates measures of trust and risk with an existing credentials-based access control model. The framework uses a knowledge representation called Many Worlds on a Frame to capture context for interactions. It associates trust values with user roles and privilege packages, and calculates risk scores based on the sensitivity of data and likelihood of undesirable events. The framework aims to provide flexible policies while mitigating risks like illegitimate access of sensitive health information in distributed healthcare environments.
The document discusses the promise of web science as an interdisciplinary approach to understanding the world wide web. It outlines how perspectives of the web have evolved from a database to a digital library to a cognitive and socio-cognitive space. The need for web science is described to better understand, engineer, and ensure the social benefits of the evolving web. Key aspects of web science include building models of web phenomena through an observatory approach and taking both observational and engineering perspectives.
Web Page Segmentation for Querying Healthcare RepositoryAastha Madaan
The document proposes a web page segmentation algorithm called VisHue to identify semantic segments in web pages. VisHue uses visual and heuristics-based features to generate a hierarchical structure of segments. It aims to improve query interfaces by allowing users to query segments treated as attributes. The performance of querying segments (Query by Tag) is evaluated using the MedlinePlus medical encyclopedia, showing benefits over traditional keyword search. The algorithm and querying interface are presented as promising ways to facilitate deep querying of web page content.
A Quasi Relational Query Language for Persistent Standardized EHRs: Using NoS...Aastha Madaan
The work draws a analogy of the OpenEHR model based EHR system with the CODASYL data model. It discusses the query power of the proposed language in the healthcare domain.
The presentation was given at the SOCM'16 workshop at the WWW16 conference. It corresponds to the research study titled "Observlets: Empowering Analytical Observations on Web Observatory".
IoT Observatory is a global distributed catalogue of applications and data related to various IoT domains. It supports users from various scientific, administrative and social domains to collaborate, engage and contribute their existing data analytics in a secure way with other users. This can support both academic and industrial domains to create real value of the IoT data in terms of research and economics.
English Drug and Alcohol Commissioners June 2024.pptxMatSouthwell1
Presentation made by Mat Southwell to the Harm Reduction Working Group of the English Drug and Alcohol Commissioners. Discuss stimulants, OAMT, NSP coverage and community-led approach to DCRs. Focussing on active drug user perspectives and interests
Basics of Electrocardiogram
CONTENTS
●Conduction System of the Heart
●What is ECG or EKG?
●ECG Leads
●Normal waves of ECG.
●Dimensions of ECG.
● Abnormalities of ECG
CONDUCTION SYSTEM OF THE HEART
ECG:
●ECG is a graphic record of the electrical activity of the heart.
●Electrical activity precedes the mechanical activity of the heart.
●Electrical activity has two phases:
Depolarization- contraction of muscle
Repolarization- relaxation of muscle
ECG Leads:
●6 Chest leads
●6 Limb leads
1. Bipolar Limb Leads:
Lead 1- Between right arm(-ve) and left arm(+ve)
Lead 2- Between right arm(-ve) and left leg(+ve)
Lead 3- Between left arm(-ve)
and left leg(+ve)
2. Augmented unipolar Limb Leads:
AvR- Right arm
AvL- Left arm
AvF- Left leg
3.Chest Leads:
V1 : Over 4th intercostal
space near right sternal margin
V2: Over 4th intercostal space near left sternal margin
V3:In between V2 and V4
V4:Over left 5th intercostal space on the mid
clavicular line
V5:Over left 5th intercostal space on the anterior
axillary line
V6:Over left 5th intercostal space on the mid
axillary line.
Normal ECG:
Waves of ECG:
P Wave
•P Wave is a positive wave and the first wave in ECG.
•It is also called as atrial complex.
Cause: Atrial depolarisation
Duration: 0.1 sec
QRS Complex:
•QRS’ complex is also called the initial ventricular complex.
•‘Q’ wave is a small negative wave. It is continued as the tall ‘R’ wave, which is a positive wave.
‘R’ wave is followed by a small negative wave, the ‘S’ wave.
Cause:Ventricular depolarization and atrial repolarization
Duration: 0.08- 0.10 sec
T Wave:
•‘T’ wave is the final ventricular complex and is a positive wave.
Cause:Ventricular repolarization Duration: 0.2 sec
Intervals and Segments of ECG:
P-R Interval:
•‘P-R’ interval is the interval
between the onset of ‘P’wave and onset of ‘Q’ wave.
•‘P-R’ interval cause atrial depolarization and conduction of impulses through AV node.
Duration:0.18 (0.12 to 0.2) sec
Q-T Interval:
•‘Q-T’ interval is the interval between the onset of ‘Q’
wave and the end of ‘T’ wave.
•‘Q-T’ interval indicates the ventricular depolarization
and ventricular repolarization,
i.e. it signifies the
electrical activity in ventricles.
Duration:0.4-0.42sec
S-T Segment:
•‘S-T’ segment is the time interval between the end of ‘S’ wave and the onset of ‘T’ wave.
Duration: 0.08 sec
R-R Interval:
•‘R-R’ interval is the time interval between two consecutive ‘R’ waves.
•It signifies the duration of one cardiac cycle.
Duration: 0.8 sec
Dimension of ECG:
How to find heart rhytm of the heart?
Regular rhytm:
Irregular rhytm:
More than or less than 4
How to find heart rate using ECG?
If heart Rhytm is Regular :
Heart rate =
300/No.of large b/w 2 QRS complex
= 300/4
=75 beats/mins
How to find heart rate using ECG?
If heart Rhytm is irregular:
Heart rate = 10×No.of QRS complex in 6 sec 5large box = 1sec
5×6=30
10×7 = 70 Beats/min
Abnormalities of ECG:
Cardiac Arrythmias:
1.Tachycardia
Heart Rate more than 100 beats/min
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Domain-specific Multi-stage Query Language for Medical Document Repositories
1. to Advance Knowledge for Humanityto Advance Knowledge for Humanity
Aastha Madaan
University of Aizu
1
Domain Specific Multi-stage Query LanguageDomain Specific Multi-stage Query Language
for Medical Document Repositoriesfor Medical Document Repositories
11/12/16 VLDB Phd Workshop 2013
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IntroductionIntroduction
Specialized Domains
Biomedical, agriculture, medical/healthcare
Insufficient Search Engine-like Keywords based search
Require Effective search and query mechanisms
Medical domain
Medical professionals Specific technical articles (particular topic/sub-
topics )
General public General information (disease or medicine).
How to retrieve medical information effectively
11/12/16 2VLDB Phd Workshop 2013
Query ”general AIDS information” medical search tool, such as
PubMed
Result 1000s of documents Different aspects of AIDS⊆
Such as , treatment, drug therapy, transmission, diagnosis, and history
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Introduction (1)Introduction (1)
Medical Information
Knowledge Evolved over 10s of years
Contains Well defined terms and processes
Available on the Web
Patient Specific Information
Knowledge-based Information
Medical Literature
Web Documents
Patient-encounter
Recordings
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Complexity Knowledge-based Resources
Heterogeneous End-user groups
Patients, researchers, doctors and other experts
Variation Information Requirements
Patient-treatment, self -diagnosis, general health information
Structure Medical Documents
Scientific papers, encyclopedias and other literature
Unique, well-defined
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Introduction (2)Introduction (2)
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Introduction (3): Specialized DocumentsIntroduction (3): Specialized Documents
Case of medical encyclopedias
Comprehensive medical guide Patients and clinicians
Authoritative source NLM (National Library of Medicine)
Paper based resources Electronic format
Examples MedlinePlus, WebMD, ADAMS,
Merriam-Webster Medical Dictionary
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Introduction (4): Why QueryIntroduction (4): Why Query
External knowledge base Clinicians
Evidence based medicine
During different stages of point-of-care
Patient Assessment plan
Treatment based on Patient diagnosis
Improve Quality of Care
Authoritative information required
Self Diagnosis Patients and their relatives
During Early appearance of symptoms/post-checkups
Enhance Personal Knowledge
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The Underlying StructureThe Underlying Structure
11/12/16 VLDB Phd Workshop 2013 7
The Hierarchical Structure
Topic of the Document
Subtopics
Miscellaneous/Related
Content
Subtopic 1 Subtopic 2 Subtopic n Content topic 1 Content topic 2 Content topic n
Content Content Content Content ContentContent
Flow of Contents Organized stages of point-of-
care
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11/12/16 8VLDB Phd Workshop 2013
Introduction (5): End UsersIntroduction (5): End Users
Variable
a. Demographical Characteristics
b. Tasks/Purpose
c. Computer/Domain Expertise
Practitioners and Researchers
Well-versed
Domain knowledge and terminologies
Require
Precise, complete, accurate and timely results
Patients and their relatives
NOT Well-versed
Domain knowledge and terminologies
Require
General information
Healthcare Workers
Specialized
Researchers
Patients, their relatives
9. to Advance Knowledge for Humanityto Advance Knowledge for Humanity
Evidence-based Queries
Intent: Diagnostic
Raised by: Clinicians/Experts
Target resources: Online Medical Repositories (e.g. medical encyclopedia)
Example: “Cases where helicobacter bacteria causes peptic ulcer”
Hypothesis-directed Queries
Intent: Non-diagnostic
Raised by: Novice users/patients
Target resources: Online Medical Repositories (e.g. medical encyclopedia)
Example: “Treatment in case of high fever and dizziness”
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Medical QueriesMedical Queries
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Query FlowsQuery Flows
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Occurrence Evidence-based and hypothesis-directed queries
Represent Stages of information seeking
Comprise Varying levels of query complexity
1. 2. 3. 4.
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Query: Find chances of "Cancer Risk" in patients showing symptom "Sleep Deprivation"
and have been exposed to "Radiation" (but not "Environmental Toxins" and does not have
"Genetic Disorder") .
Help Needed
Research GapResearch Gap
Results Large in number, irrelevant
Failure Keyword search, domain-specific search tools
Require Precise and easy-to-use database style query methods
Key steps:
1. Schema understandable by users
2. Identify Resources to query
3. Identify Granularity of results
Healthcare Expert
Paper-based resources
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Aim: Query Online Medical Information Effectively
Transform Document Repository User-Level Schema
Enable High-level Query Language
Target Audience Skilled and semi-skilled users
Utilize Query capabilities of a database query language
Facilitate In-depth Queries and Granular Results
Bridging the GapBridging the Gap
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Query the New WayQuery the New Way
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User-levelUser-level
SchemaSchema
High-level QueryHigh-level Query
languagelanguage
Traditional
Method
Proposed
Method
Resource
Resource
Keyword
Search
Query
Method
Medical
Expert
Medical
Expert
Results
-Lack specificity
-Long list of full documents
-Trustworthiness of resources unknown
Results
-Specific, granular
-Segments of documents query criteria
-Trustworthy/Authoritative sources only
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Proposed ApproachProposed Approach
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11/12/16 VLDB Phd Workshop 2013 15
Key FeaturesKey Features
User-Level Schema Offline Process
Universal , concept-level schema
Attributes
Understandable Domain experts and novice users
Provide Granular, context-based results
Use Web segmentation algorithm, Domain concepts
Multi-stage Query Language Online Process
Map multi-stage diagnostic process Step-by-step Query Flow
Interactive Querying Continuous query refinement
View Results Add concept View Results
Support Simple, Medium, Complex, Recursive Queries
Use User-level schema
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OutlineOutline
Two-step Framework
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Data ModelData Model
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Data ModelData Model
Tree Structured Repository
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H1H1
f1f1
f2f2
19. to Advance Knowledge for Humanityto Advance Knowledge for Humanity
Data Model (1)Data Model (1)
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Data Model (2): SchemaData Model (2): Schema
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Attributes Diagnostic concepts/terms
Stages of point-of-care
Do not change frequently
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Data Model (3): A XML DocumentData Model (3): A XML Document
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Title Causes
Symptoms
Treatment
Document corresponding to “Aarskog Syndrome”
MedlinePlus Medical Encyclopedia
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Data Model (4): Query EffortData Model (4): Query Effort
Query: Find if "Oxygen therapy" work for the treatment of "Chronic
Respiratory Failure" and symptoms are "Lethargy" OR "Shortness of breath”.
11/12/16 VLDB Phd Workshop 2013 22
Advanced keyword search Proposed Method
SELECT attribute = “Treatment”
WHERE
Attribute “Disease_name” = “Chronic
Respiratory Failure”
AND
Attribute “Treatment” = “Oxygen therapy”
AND
Attribute “Symptoms” =“Lethargy”
OR
Attribute “Symptoms” = “Shortness of breath”
Easy-to-UseNot Possible
Result segment
Context of user-query
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Data Model (5): Granular ResultsData Model (5): Granular Results
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Queried
Attributes/Segments
Query Results
Context Granular
Each result is a segment, combination of
Concept/context in query
Item of concern (content enclosed in a segment)
24. to Advance Knowledge for Humanityto Advance Knowledge for Humanity
An ExampleAn Example
Query: Find other symptoms where “chronic kidney failure” is
caused by “anemia”
Queried segment Symptoms
Segments in Query Causes = “anemia” and Disease_name =
“Chronic kidney failure”
Result Segment Symptoms
Context disease_name = “chronic kidney failure” & causes
= “anemia”
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Next StepNext Step Multi-stage Query LanguageMulti-stage Query Language
26. to Advance Knowledge for Humanityto Advance Knowledge for Humanity
Proposed Query Language (1)Proposed Query Language (1)
XQBE [Braga, 2005] User level schema
Create queries Drag and drop interface
Query : “Find cases where a person is inflicted with “peptic ulcer” due to
“helicobacter pylon bacteria”
11/12/16 VLDB Phd Workshop 2013 26
Attributes understandable by
end users
1. Case = disease_name
Value = ??
2. Due to = Causes
Value = “helicobacter pylon bacteria”
3. Inflicted with = Symptoms
Value = “peptic ulcer”
Query Effort
Minimal learning curve
Computer-expertise not required
27. to Advance Knowledge for Humanityto Advance Knowledge for Humanity
Multi-stage Query-by-Concept
Concept Query-able attribute
Topic, sub-topic, medical concept
Query Effort
Dynamic selection of attributes
No computer expertise
Query Process
11/12/16 VLDB Phd Workshop 2013 27
Proposed Query Language (2)Proposed Query Language (2)
An Example: Cases where fever is caused
due to infliction of Pneumonia and
Tuberculosis
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Another ExampleAnother Example
Query: Find cases where 3 clinical concepts (“cough”, “no
sore-throat”, and “no sterol injection”) occur in context of
symptoms along with a sub-concept (“non sterol injection at
the left side”)
XQBE on Specialized Medical Repositories
Multi-stage Query-by-concept Query Language
11/12/16 VLDB Phd Workshop 2013 28
29. to Advance Knowledge for Humanityto Advance Knowledge for Humanity
Evaluation PlanEvaluation Plan
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Data Sets
Document repository
MedlinePlus Health topics (900+) , encyclopedia (4000+), drugs (12000+)
Set of Queries
50 test queries (multi-staged) Using literature survey and consultation with
medical users
Quantitative Studies
Evaluation Metrics
Accuracy of segment extraction (schema creation) Precision and Recall
Reduction in search space Query Results
Qualitative Studies
Usability Studies
Actual End-users
Query Performance
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Initial AchievementsInitial Achievements
HTML documents XML schema as per proposed model
XQuery on XML
Integration with XQBE
Query by concept Enumeration using paper and pencil
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ChallengesChallenges
Scalability Schema extraction of similar repositories
Understand and Implement Query operations needed
Understand User characteristics to be considered
User Interface Query Language
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3211/12/16 VLDB Phd Workshop 2013
Related WorkRelated Work
Domain-specific Information Retrieval [Yan, 2011]
Similarity and popularity based models Insufficient for domain experts
“Information granulation” needs to be considered in huge document repositories
Form-based Query Interfaces [Jayapandian, 2009]
Easy-to-use
Limited access to the database
Complex queries large number of forms
Varying medical concepts large number of fields in forms
Beyond single page web search results [Varadarajan, 2008]
Provide granular results for user’s search
Return segments from multiple or related web documents as results
High-level Graphical Query Languages [Braga, 2005]
Easy-to-use and understand
Little or no programming effort required by the user
Common languages QBE, XQBE
33. to Advance Knowledge for Humanityto Advance Knowledge for Humanity
Summary and ConclusionsSummary and Conclusions
3311/12/16 VLDB Phd Workshop 2013
Proposed Multi-stage Query Language
1. Aim Making Online medical information usable
2. Transformation User-Level Schema
3. Facilitates Granular/Context-based Results
4. Support Healthcare Experts
5. Minimize Learning curve for novice users
6. Reduce Dependency on keyword based searches
Provide Web user level activity Healthcare experts no or
little programming effort
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References (1)References (1)
3411/12/16 VLDB Phd Workshop 2013
[1] D. Braga, A. Campi, and S. Ceri. Xqbe (xquery by example): A visual interface to the standard xml query language. ACM
Trans. Database Syst., 30(2):398–443, June 2005.
[2] D. Cai, S. Yu, J.-R. Wen, and W.-Y. Ma. Extracting content structure for web pages based on visual representation. In
Proceedings of the 5th APWeb, pages 406–417. Springer-Verlag, 2003.
[3] M.-A. Cartright, R. W. White, and E. Horvitz. Intentions and attention in exploratory health search. In Proceedings of the 34th
Intl. ACM SIGIR conference, pages 65–74, New York, NY, USA, 2011.ACM.
[4] S. Cohen, Y. Kanza, Y. Kogan, W. Nutt, Y. Sagiv, and A. Serebrenik. Equix-a search and query language for xml. Journal of
the American Society for Information Science and Technology, 53:2002, 2000.
[5] S. M. Freire, E. Sundvall, D. Karlsson, and P. Lambrix. Performance of XML Databases for Epidemiological Queries in
Archetype-Based EHRs. In Proceedings Scandinavian Conference on Health Informatics 2012, volume 70 of Linkping Electronic
Conference Proceedings, pages 51–57. Linkping University Electronic Press, 2012.
[6] M. Gschwandtner, M. Kritz, and C. Boyer. Requirements of the health professional research. In Technical Report D8.1.2.
Khresmoi Project, 2011.
[7] A. Hanbury. Medical information retrieval, an instance of domain. In SIGIR'12. ACM, August 2012.
[8] S. Hunt, J. J. Cimino, and D. E. Koziol. A comparison of clinicians’s access to online knowledge resources using two types of
information retrieval applications in an academic hospital setting. J Med Libr Assoc, 101(1):26–31, 2013.
[9] http://www.who.int/classifications/icd/en/, 2011.
[10] M. Jayapandian and H. V. Jagadish. Automating the design and construction of query forms. ICDE, page 125, 2006.
[11] F. Li and H. V. Jagadish. Usability, databases, and hci. IEEE Data Eng. Bull., 35(3):37–45, 2012. [12] http://loinc.org/, 2011.
[13] A. Marian and W. Wang. Flexible querying of personal information. IEEE Data Eng. Bull., 32(2):20–27, 2009.
[14] http://www.nlm.nih.gov/bsd/pmresources.html, 2011.
[15] http://www.nlm.nih.gov/medlineplus/, 2009.
[16] http://www.linkedin.com/groups/ Choice-OpenEHR-persistence-layer-144276.S.208531138?qid=208adbca-fc26-4ada-bf02-
7efe5a9e5661&trk=group_most_recent_rich-0-b-ttl&goback=%2Egmr_144276, 2013.
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References (2)References (2)
[17] http://www.ncbi.nlm.nih.gov/pubmed, 2011.
[18] S. A. Rahman, S. Bhalla, and T. Hashimoto. Query-by-object interface for information requirement elicitation in m-commerce. Int.
J. Hum. Comput. Interaction, 20(2):135–160, 2006.
[19] X. Y. Raymond, Y. Lau, D. Song, X. Li, and J. Ma. Toward a semantic granularity model for domain-specific information retrieval.
ACM Trans. On Information Systems., 29(3), July 2011.
[20] S. Sachdeva and S. Bhalla. Implementing high-level query language interfaces for archetype-based electronic health records
database. In COMAD, 2009.
[21] http://www.ihtsdo.org/snomed-ct/, 2011.
[22] R. Varadarajan, V. Hristidis, and T. Li. Beyond single-page web search results. IEEE Transactions on Knowledge and Data
Engineering, 20(3):411–424, 2008.
[23] A. Yasir, M. Kumara Swamy, P. Krishna Reddy, and S. Bhalla. Enhanced query-by-object approach for information requirement
elicitation in large databases. In Big Data Analytics, volume 7678 of Lecture Notes in Computer Science, pages 26–41. Springer, 2012.
[24] M. Jayapandian, H.V. Jagadish : Automating the Design and Construction of Query Forms. IEEE Trans. Knowl. Data Eng.
21(10) :1389-1402 (2009).
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QuestionsQuestions
3611/12/16 VLDB Phd Workshop 2013
Editor's Notes
Good Morning everyone, I am Aastha and today I am going to talk about our effort to build a quasi-relational QL for the Standardized electronic health records which have been persisted using a NoSQL database.
-To deliver quality healthcare services, the healthcare domain requires the support of IT during various phases of patient-care. These include the primary purposes and the secondary purposes as well.
IT is capable to provide scalable and flexible infrastructures to support the expanding, geographically distributed needs of the healthcare domain.
EHRs in a hospital are – heterogeneous in nature
It comprises of dictations from physicians, structured data from the sensors, images of XRAY. And various parameters that define patient health
As the healthcare moves towards BigData – the proper analysis and querying of the data within EHRs is required
This can be supported to some extent by use of – data standards for EHRs,
These help representing the data uniformly across organizations which are geographically distributed
For interoperable exchange of data – the prominent standards existent are – HL7, CEN 13606 and OpenEHR
Among these OpenEHR is the latest and increasingly accepted standard.
In this study we consider the EHRs based on OpenEHR as our prime focus
Let us visualize how a seemingly simple single patient encounter generates complex and large amount of EHR data which is to be analyzed for improving healthcare services.
During a single patient encounter, several patient-visit parameters may be involved during a check-up. These may include say, BP, glucose-level, BMI, Medication List and temperature.
Each of these parameters is actually a complex, full-blown semantic object in itself and several properties are associated with each.
These can be directly mapped to an Archetype artifact of the OpenEHR standard
The term “Archetype” gives a full-fledged description of a medical concept
Multiple concepts of say the archetypes together form an archetypal HER
So far a set of 352 archetypes is defined by the standard,
Hence we can say that an EHR can be an aggregation of maximum 352 archetypes
Further, these are persisted in a database.
As we see in the figure, the complex patient encounter data is persisted in the DB
And the healthcare worker looking for support of querying during patient care is generally aware of the patient id and wants to fetch his or her EHR
This can be well modeled using the NoSQL DBs
These may include the key-value type of store/the big table type of stores/ the graph or the document oriented DBs
By using the NoSQL database
a) the user does not require pre-defined schema, relationships and keys.
b) they are low-cost, schema-free and horizontally scalable to accompany new resources when the need arises.
c) significant for cloud-based solutions, common database approach for cloud based clinical systems.
The general purpose queries of the users in this case may not be simple.
They may require consideration of complex structure of the underlying database.
Up till now we have seen that each of the patient-encounter parameter is mapped to an archetype or concept
Multiple archetypes or concepts together form an archetypal EHR
For each EHR there may exist multiple versions
These are persisted with help of the reference model of the OpenEHR
Each of the archetypes is represented via an ADL
The Ocean informatics proposes a declarative AQL query language based on the ADL Paths
The health-worker needs to understand the syntaxes of this language in order to compose queries. This may not be his choice
Hence there are two missing points here:
A) the method of persistence
B) an easy to use query language for the healthcare workers
Each of the archetype is serialized into a JSON document and stored in the MongoDB
Each of the documents is stored with a unique id and patient id
Next we try to explain what is our proposed system all about and where is it in the context of the EHRs, big data and persistence terminologies
The proposed quasi-relational query language system is directly interfaced with the persisted patient data
The user can directly formulate the query using the flattened screen forms
These screen forms are the templates generated from various combinations of the archetypes
The system at present takes a subset of the archetypes of the OpenEHR archetype repository