Knowledge Representations

The AMIA biomedical informatics (BMI) core competencies have been designed to support and guide graduate education in BMI, the core scientific discipline underlying the breadth of the field's research, practice, and education. The core definition of BMI adopted by AMIA specifies that BMI is 'the interdisciplinary field that studies and pursues the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem solving and decision making, motivated by efforts to improve human health.' Application areas range from bioinformatics to clinical and public health informatics and span the spectrum from the molecular to population levels of health and biomedicine. The shared core informatics competencies of BMI draw on the practical experience of many specific informatics subdisciplines. The AMIA BMI analysis highlights the central shared set of competencies that should guide curriculum design and that graduate students should be expected to master.

The AMIA biomedical informatics (BMI) core competencies have been designed to support and guide graduate education in BMI, the core scientific discipline underlying the breadth of the field's research, practice, and education. The core definition of BMI adopted by AMIA specifies that BMI is 'the interdisciplinary field that studies and pursues the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem solving and decision making, motivated by efforts to improve human health.' Application areas range from bioinformatics to clinical and public health informatics and span the spectrum from the molecular to population levels of health and biomedicine. The shared core informatics competencies of BMI draw on the practical experience of many specific informatics subdisciplines. The AMIA BMI analysis highlights the central shared set of competencies that should guide curriculum design and that graduate students should be expected to master.

Objective Physicians who more intensively interact with electronic health records (EHRs) through their documentation style may pay greater attention to coded fields and clinical decision support and thus may deliver higher quality care. We measured the quality of care of physicians who used three predominating EHR documentation styles: dictation, structured documentation, and free text. Methods We conducted a retrospective analysis of visits by patients with coronary artery disease and diabetes to the Partners Primary Care Practice Based Research Network. The main outcome measures were 15 EHR-based coronary artery disease and diabetes measures assessed 30 days after primary care visits. Results During the 9-month study period, 7000 coronary artery disease and diabetes patients made 18 569 visits to 234 primary care physicians of whom 20 (9%) predominantly dictated their notes, 68 (29%) predominantly used structured documentation, and 146 (62%) predominantly typed free text notes. In multivariable modeling adjusted for clustering by patient and physician, quality of care appeared significantly worse for dictators than for physicians using the other two documentation styles on three of 15 measures (antiplatelet medication, tobacco use documentation, and diabetic eye exam); better for structured documenters for three measures (blood pressure documentation, body mass index documentation, and diabetic foot exam); and better for free text documenters on one measure (influenza vaccination). There was no measure for which dictators had higher quality of care than physicians using the other two documentation styles. Conclusions EHR-assessed quality is necessarily documentation-dependent, but physicians who dictated their notes appeared to have worse quality of care than physicians who used structured EHR documentation. Clinical trial registration number ClinicalTrials.gov Identifier: NCT00235040.

Objective This article reports on the alignment between the foundational domains and the delineation of practice (DoP) for health informatics, both developed by the American Medical Informatics Association (AMIA). Whereas the foundational domains guide graduate-level curriculum development and accreditation assessment, providing an educational pathway to the minimum competencies needed as a health informatician, the DoP defines the domains, tasks, knowledge, and skills that a professional needs to competently perform in the discipline of health informatics. The purpose of this article is to determine whether the foundational domains need modification to better reflect applied practice. Materials and Methods Using an iterative process and through individual and collective approaches, the foundational domains and the DoP statements were analyzed for alignment and eventual harmonization. Tables and Sankey plot diagrams were used to detail and illustrate the resulting alignment. Results...

This article discusses one type of data frame structure. Using the transit function of these models allows you to draw logical conclusions from the data stream using hierarchical relationships. Describing such knowledge and filling intellectual systems with it is a very difficult task, which requires additional work and time for the developers of artificial intelligence This procedural knowledge is an output control tool in frame systems, and with their help any output mechanism can be implemented

The use of formal languages in the specification of upper ontologies helps establishing precise and unambiguous definitions for its concepts and relations. In this context, the use of formal languages with support of proof-assistant software systems has potential of aiding the specification author in various aspects. We describe a study case in which the Isabelle/HOL language and the Isabelle proof-assistant environment are used to formalize a simplified version of the UFO-A ontology of endurants, to verify and correct the original axiomatization, and to optimize the specification theory's axioms and signature.

Four architectures for computer supported collaborative learning systems are analyzed using the model-view-controller design pattern and compared from the standpoints of coupling between activities of the users and suitability for educational use, as well as network load and ease of implementation. The architectures are illustrated with examples from the developmental history of Belvedere, an environment for collaborative construction of knowledge representations during problem solving. A hybrid architecture that supports model-level coupling is shown to provide the best design tradeoffs.

Objective Although trait-associated genes identified as complex versus single-gene inheritance differ substantially in odds ratio, the authors nonetheless posit that their mechanistic concordance can reveal fundamental properties of the genetic architecture, allowing the automated interpretation of unique polymorphisms within a personal genome.

Objective To develop a set of high-severity, clinically significant drugedrug interactions (DDIs) for use in electronic health records (EHRs). Methods A panel of experts was convened with the goal of identifying critical DDIs that should be used for generating medication-related decision support alerts in all EHRs. Panelists included medication knowledge base vendors, EHR vendors, in-house knowledge base developers from academic medical centers, and both federal and private agencies involved in the regulation of medication use. Candidate DDIs were assessed by the panel based on the consequence of the interaction, severity levels assigned to them across various medication knowledge bases, availability of therapeutic alternatives, monitoring/management options, predisposing factors, and the probability of the interaction based on the strength of evidence available in the literature. Results Of 31 DDIs considered to be high risk, the panel approved a final list of 15 interactions. Panelists agreed that this list represented drugs that are contraindicated for concurrent use, though it does not necessarily represent a complete list of all such interacting drug pairs. For other drug interactions, severity may depend on additional factors, such as patient conditions or timing of co-administration. Discussion The panel provided recommendations on the creation, maintenance, and implementation of a central repository of high severity interactions. Conclusions A set of highly clinically significant drugdrug interactions was identified, for which warnings should be generated in all EHRs. The panel highlighted the complexity of issues surrounding development and implementation of such a list.

Goal-Oriented Requirements Engineering (GORE) has grown into an important area of research in the past decades. Still, some of its corners remain dark, since different GORE languages do not provide a well-founded conceptualization of the domain and are not consensual. This may lead to ambiguous or weak understanding of GORE concepts. In this paper, we introduce the Goal-Oriented Requirements Ontology (GORO), a domain ontology founded on the Unified Foundational Ontology (UFO) that intends to represent the nature and relations of concepts surrounding the GORE domain. We use GORO to explore and clarify the semantics used, sometimes implicitly, by well-known GORE languages.

Objective: To assess and refine competencies for the clinical research data management profession. Materials and Methods: Based on prior work developing and maintaining a practice standard and professional certification exam, a survey was administered to a captive group of clinical research data managers to assess professional competencies, types of data managed, types of studies supported, and necessary foundational knowledge. Results: Respondents confirmed a set of 91 professional competencies. As expected, differences were seen in job tasks between early- to mid-career and mid- to late-career practitioners. Respondents indicated growing variability in types of studies for which they managed data and types of data managed. Discussion: Respondents adapted favorably to the separate articulation of professional competencies vs foundational knowledge. The increases in the types of data managed and variety of research settings in which data are managed indicate a need for formal educat...

The applied disciplines of architecture and civil engineering require students to communicate multimodally, and to manipulate meaning across media and modes, such as image, writing or moving image. In their disciplinary studies for example, students must be able to transform the language of lectures and textbooks into models and diagrams. In their future workplaces, they will commonly be required to transform reports and legal documents into floor plans and digital & physical 3D models. Such multimodal literacy, however, is not typically reflected in their related subject-specific English language courses, especially in Germany, where a text-centric approach is favored. To better reflect the demands placed upon them, students in two courses of English for Architecture and Civil Engineering were tasked with creating digital, multimodal artifacts to explain a concept from either of these fields to a lay audience. The resultant artifacts used a wide variety of semiotic resources to mak...

Using data from focus group interviews, this study aims to analyze the perceived opportunities and challenges encountered by groups of secondary school teachers regarding the assessment of multimodal texts. Previous research has shown that digital technologies introduce new kinds of texts in the classroom. The possibility of combining verbal text with image, sound, and movement has an impact on students’ text production. However, the changing role of writing on the screen has left teachers uncertain of how to assess these new forms of texts. Apart from attending to digital competence, assessing multimodal texts may involve following writing conventions and organizing text using different resources and components. Quality in multimodal texts may concern using multimodal components to communicate complex ideas effectively, which may or may not be noticed by teachers. The data is based on group interviews with 11 secondary school teachers at two different schools. The study conducted a...

Acquiring knowledge from continuous and heterogeneous data streams is a prerequisite for IoT applications. Semantic technologies provide comprehensive tools and applicable methods for representing, integrating, and acquiring knowledge. However, resource-constraints, dynamics, mobility, scalability, and real-time requirements introduce challenges for applying these methods in IoT environments. We study how to utilize semantic IoT data for reasoning of actionable knowledge by applying state-of-the-art semantic technologies. For performing these studies, we have developed a semantic reasoning system operating in a realistic IoT environment. We evaluate the scalability of different reasoning approaches, including a single reasoner, distributed reasoners, mobile reasoners, and a hybrid of them. We evaluate latencies of reasoning introduced by different semantic data formats. We verify the capabilities of promising semantic technologies for IoT applications through comparing the scalability and real-time response of different reasoning approaches with various semantic data formats. Moreover, we evaluate different data aggregation strategies for integrating distributed IoT data for reasoning processes.

Objective To develop an algorithm for the discovery of drug treatment patterns for endocrine breast cancer therapy within an electronic medical record and to test the hypothesis that information extracted using it is comparable to the information found by traditional methods. Materials The electronic medical charts of 1507 patients diagnosed with histologically confirmed primary invasive breast cancer. Methods The automatic drug treatment classification tool consisted of components for: (1) extraction of drug treatment-relevant information from clinical narratives using natural language processing (clinical Text Analysis and Knowledge Extraction System); (2) extraction of drug treatment data from an electronic prescribing system; (3) merging information to create a patient treatment timeline; and (4) final classification logic. Results Agreement between results from the algorithm and from a nurse abstractor is measured for categories: (0) no tamoxifen or aromatase inhibitor (AI) treatment; (1) tamoxifen only; (2) AI only; (3) tamoxifen before AI; (4) AI before tamoxifen; (5) multiple AIs and tamoxifen cycles in no specific order; and (6) no specific treatment dates. Specificity (all categories): 96.14%e100%; sensitivity (categories (0)e(4)): 90.27%e99.83%; sensitivity (categories (5)e(6)): 0e23.53%; positive predictive values: 80%e97.38%; negative predictive values: 96.91%e99.93%. Discussion Our approach illustrates a secondary use of the electronic medical record. The main challenge is event temporality. Conclusion We present an algorithm for automated treatment classification within an electronic medical record to combine information extracted through natural language processing with that extracted from structured databases. The algorithm has high specificity for all categories, high sensitivity for five categories, and low sensitivity for two categories.

The National Center for Biomedical Ontology is now in its seventh year. The goals of this National Center for Biomedical Computing are to: create and maintain a repository of biomedical ontologies and terminologies; build tools and web services to enable the use of ontologies and terminologies in clinical and translational research; educate their trainees and the scientific community broadly about biomedical ontology and ontology-based technology and best practices; and collaborate with a variety of groups who develop and use ontologies and terminologies in biomedicine. The centerpiece of the National Center for Biomedical Ontology is a web-based resource known as BioPortal. BioPortal makes available for research in computationally useful forms more than 270 of the world's biomedical ontologies and terminologies, and supports a wide range of web services that enable investigators to use the ontologies to annotate and retrieve data, to generate value sets and special-purpose lexicons, and to perform advanced analytics on a wide range of biomedical data.

This article discusses one type of data frame structure. Using the transit function of these models allows you to draw logical conclusions from the data stream using hierarchical relationships. Describing such knowledge and filling intellectual systems with it is a very difficult task, which requires additional work and time for the developers of artificial intelligence This procedural knowledge is an output control tool in frame systems, and with their help any output mechanism can be implemented

The AMIA Public Health Informatics 2011 Conference brought together members of the public health and health informatics communities to revisit the national agenda developed at the AMIA Spring Congress in 2001, assess the progress that has been made in the past decade, and develop recommendations to further guide the field. Participants met in five discussion tracks: technical framework; research and evaluation; ethics; education, professional training, and workforce development; and sustainability. Participants identified 62 recommendations, which clustered into three key themes related to the need to (1) enhance communication and information sharing within the public health informatics community, (2) improve the consistency of public health informatics through common public health terminologies, rigorous evaluation methodologies, and competency-based training, and (3) promote effective coordination and leadership that will champion and drive the field forward. The agenda and recommendations from the meeting will be disseminated and discussed throughout the public health and informatics communities. Both communities stand to gain much by working together to use these recommendations to further advance the application of information technology to improve health.

A shared concern of knowledge based systems (KBS) development and traditional information system development is the choice of modeling construct most natural to the information (knowledge) structures being modeled, and to human practitioners in the modeled application area. Research in cognitive psychology has shown that human reasoning about non-trivial real world problems is a complex activity that frequently incorporates multiple reasoning strategies, concerning multiple domains of knowledge which may be perceived as having different structures. Thus the interest in hybrid knowledge representations (KR's) and hybrid reasoning strategies which are potentially capable of capturing a greater portion of the richness of reality than single paradigm KR's. This paper proposes the use of the "smart object paradigm" for the design of complex knowledge based systems. The paradigm provides mechanisms for both hybrid knowledge representation and multiple inference strategies. Central to the paradigm is the concept of "smart objects." A smart object is an engineered artifact that combines a high level object structure with a rule based lower level language. The concept of smart objects was developed using criteria that evolved in the course of development of a large, complex knowledge based system. We present these criteria as desirable characteristics for KR's in general, and evaluate traditional KR's using them. The criteria are: provision for reusability of knowledge, the ability to partition and encapsulate the knowledge space, support for multiple inference strategies, and explicit meta-control of both the reasoning strategy, and the structure and control flow of the implemented system. Smart objects are shown to possess these characteristics and an overview of a prototype KBS implemented using the smart object paradigm makes their benefits concrete.

Often a major difficulty in the design of expert systems is the process of acquiring the requisite knowledge in the form of production rules. This paper presen? a novel class of neural netwadss which am trained m such a way that they provide an appealing soktiw to the problem of knowledge acquisitkm. "he value of the network parametem, after saftkient saining, am! then utilized to generate plmluction rule on the basis of preselected meaningful coordinates. Futbere, the paper provides a mathematical framework for achieving " a b l e geae~mpropertlcsviaanappropriatetFpiniad~ rithm (supervised decisiondincted karrrtng) with a structurn that provides acceptable lurowledgc represenof the data. The concepts and methods pnsented in the paper are illustrated tlmugb one practical example fnm medical diagnasis.

Objectives Left ventricular ejection fraction (EF) is a key component of heart failure quality measures used within the Department of Veteran Affairs (VA). Our goals were to build a natural language processing system to extract the EF from free-text echocardiogram reports to automate measurement reporting and to validate the accuracy of the system using a comparison reference standard developed through human review. This project was a Translational Use Case Project within the VA Consortium for Healthcare Informatics. Materials and methods We created a set of regular expressions and rules to capture the EF using a random sample of 765 echocardiograms from seven VA medical centers. The documents were randomly assigned to two sets: a set of 275 used for training and a second set of 490 used for testing and validation. To establish the reference standard, two independent reviewers annotated all documents in both sets; a third reviewer adjudicated disagreements. Results System test results for document-level classification of EF of <40% had a sensitivity (recall) of 98.41%, a specificity of 100%, a positive predictive value (precision) of 100%, and an F measure of 99.2%. System test results at the concept level had a sensitivity of 88.9% (95% CI 87.7% to 90.0%), a positive predictive value of 95% (95% CI 94.2% to 95.9%), and an F measure of 91.9% (95% CI 91.2% to 92.7%). Discussion An EF value of <40% can be accurately identified in VA echocardiogram reports. Conclusions An automated information extraction system can be used to accurately extract EF for quality measurement.

The Ocean Sampling Mobile Network (SAMON) simulator testbed has been developed at Penn State for designing and evaluating multirobot ocean-mapping missions, in realistic underwater environments, prior to in-water testing. The goal in developing the testbed is to enable web-based integration of high-fidelity simulators of heterogeneous autonomous undersea vehicles from multiple organizations and a variety of on-board and fixed sensors in a realistic ocean environment in order to formulate and evaluate intelligent control strategies for mission execution. A formal control language facilitates real-time interactions between heterogeneous autonomous components. A simulation experiment is described that demonstrates multistage inferencing and decision/control strategies for spatio-temporal coordination and multilayered adaptation of group behavior in response to evolving environmental physics or operational dynamics Index Terms-Ocean-sampling mobile network (SAMON), decision/control strategies, simulator testbeds, unmanned underwater vehicles.

The AMIA Public Health Informatics 2011 Conference brought together members of the public health and health informatics communities to revisit the national agenda developed at the AMIA Spring Congress in 2001, assess the progress that has been made in the past decade, and develop recommendations to further guide the field. Participants met in five discussion tracks: technical framework; research and evaluation; ethics; education, professional training, and workforce development; and sustainability. Participants identified 62 recommendations, which clustered into three key themes related to the need to (1) enhance communication and information sharing within the public health informatics community, (2) improve the consistency of public health informatics through common public health terminologies, rigorous evaluation methodologies, and competency-based training, and (3) promote effective coordination and leadership that will champion and drive the field forward. The agenda and recommendations from the meeting will be disseminated and discussed throughout the public health and informatics communities. Both communities stand to gain much by working together to use these recommendations to further advance the application of information technology to improve health.

In cartoons, meanings and messages are generated either through two semiotic modes that are verbal and visual or solely through visual modes. Interaction between language and cartoon images is often regarded as a straightforward and easy-to-process way to convey a message. This study aims to show that the meaning and cartoon messages are not always easy to understand, therefore readers should observe the language and visual information of each cartoon. This study is aimed at identifying the linguistic modes and the visual modes in the cartoons to convey the meaning and the explicit message or implied message. For this purpose, two theories are adopted;  Systemic  Functional Linguistics Theory by Halliday (1994) to analyze linguistic modes and multimodal approach by Kress and Van Leeuwen (1996, 2006) to analyze visual modes or images in cartoons. The research data was obtained from Usik-Usik cartoon book (Cynical Collection of the World of Education). The findings indicate that langu...

The use of formal languages in the specification of upper ontologies helps establishing precise and unambiguous definitions for its concepts and relations. In this context, the use of formal languages with support of proof-assistant software systems has potential of aiding the specification author in various aspects. We describe a study case in which the Isabelle/HOL language and the Isabelle proof-assistant environment are used to formalize a simplified version of the UFO-A ontology of endurants, to verify and correct the original axiomatization, and to optimize the specification theory’s axioms and signature.

: A suite of standards known as the Semantic Web is transforming the Internet to a semantic graph rather than a graph of hypertext links. This paper will describe how various ideas and initiatives in artificial intelligence knowledge representation influenced its design. We begin with the seminal work by Alan Turing and Alonzo Church that led to the definition of Turing Machines, enabled digital computing, and provided the mathematical theory of computation which has been one of the determining factors for Artificial Intelligence knowledge representation. We then provide a brief history of artificial intelligence knowledge representation starting with groundbreaking researchers such as Newell and Simon, then to the first &quot;AI boom&quot; driven primarily by rule-based expert systems followed by major initiatives such as Cyc and the DARPA Knowledge Sharing Initiative. We will discuss how innovations from these initiatives affected standards that in turn led to the suite of standards known as the Semantic Web. We conclude with a brief overview of the most important issues currently facing those who wish to see widespread adoption of Semantic Web technology in industry.

The AMIA biomedical informatics (BMI) core competencies have been designed to support and guide graduate education in BMI, the core scientific discipline underlying the breadth of the field's research, practice, and education. The core definition of BMI adopted by AMIA specifies that BMI is 'the interdisciplinary field that studies and pursues the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem solving and decision making, motivated by efforts to improve human health.' Application areas range from bioinformatics to clinical and public health informatics and span the spectrum from the molecular to population levels of health and biomedicine. The shared core informatics competencies of BMI draw on the practical experience of many specific informatics subdisciplines. The AMIA BMI analysis highlights the central shared set of competencies that should guide curriculum design and that graduate students should be expected to master.

ObjectiveTo develop a set of high-severity, clinically significant drug-drug interactions (DDIs) for use in electronic health records (EHRs).MethodsA panel of experts was convened with the goal of identifying critical DDIs that should be used for generating medication-related decision support alerts in all EHRs. Panelists included medication knowledge base vendors, EHR vendors, in-house knowledge base developers from academic medical centers, and both federal and private agencies involved in the regulation of medication use. Candidate DDIs were assessed by the panel based on the consequence of the interaction, severity levels assigned to them across various medication knowledge bases, availability of therapeutic alternatives, monitoring/management options, predisposing factors, and the probability of the interaction based on the strength of evidence available in the literature.ResultsOf 31 DDIs considered to be high risk, the panel approved a final list of 15 interactions. Panelists...

#2/11 gunver majgaard, morten misfeldt & jacob nielsen: How design-based research and action research contribute to the development of a new design for learning øystein gilje: Two lenses on texts and practices: Analysing remixing practices across timescales marie leijon: Presenting as a matter of design-exploring designs for learning rikke øgreen & staffan selander: Interview with Jonas Löwgren

A suite of standards known as the Semantic Web is transforming the Internet to a semantic graph rather than a graph of hypertext links. This paper will describe how various ideas and initiatives in artificial intelligence knowledge representation influenced its design. We begin with the seminal work by Alan Turing and Alonzo Church that led to the definition of Turing Machines, enabled digital computing, and provided the mathematical theory of computation which has been one of the determining factors for Artificial Intelligence knowledge representation. We then provide a brief history of artificial intelligence knowledge representation starting with groundbreaking researchers such as Newell and Simon, then to the first "AI boom" driven primarily by rule-based expert systems followed by major initiatives such as Cyc and the DARPA Knowledge Sharing Initiative. We will discuss how innovations from these initiatives affected standards that in turn led to the suite of standards known as the Semantic Web. We conclude with a brief overview of the most important issues currently facing those who wish to see widespread adoption of Semantic Web technology in industry.

Objective In the 6 years since the National Library of Medicine began monthly releases of RxNorm, RxNorm has become a central resource for communicating about clinical drugs and supporting interoperation between drug vocabularies. Materials and methods Built on the idea of a normalized name for a medication at a given level of abstraction, RxNorm provides a set of names and relationships based on 11 different external source vocabularies. The standard model enables decision support to take place for a variety of uses at the appropriate level of abstraction. With the incorporation of National Drug File Reference Terminology (NDF-RT) from the Veterans Administration, even more sophisticated decision support has become possible. Discussion While related products such as RxTerms, RxNav, MyMedicationList, and MyRxPad have been recognized as helpful for various uses, tasks such as identifying exactly what is and is not on the market remain a challenge.

Objectives Electronic health records (EHR) can allow for the generation of large cohorts of individuals with given diseases for clinical and genomic research. A ratelimiting step is the development of electronic phenotype selection algorithms to find such cohorts. This study evaluated the portability of a published phenotype algorithm to identify rheumatoid arthritis (RA) patients from EHR records at three institutions with different EHR systems. Materials and Methods Physicians reviewed charts from three institutions to identify patients with RA. Each institution compiled attributes from various sources in the EHR, including codified data and clinical narratives, which were searched using one of two natural language processing (NLP) systems. The performance of the published model was compared with locally retrained models. Results Applying the previously published model from Partners Healthcare to datasets from Northwestern and Vanderbilt Universities, the area under the receiver operating characteristic curve was found to be 92% for Northwestern and 95% for Vanderbilt, compared with 97% at Partners. Retraining the model improved the average sensitivity at a specificity of 97% to 72% from the original 65%. Both the original logistic regression models and locally retrained models were superior to simple billing code count thresholds. Discussion These results show that a previously published algorithm for RA is portable to two external hospitals using different EHR systems, different NLP systems, and different target NLP vocabularies. Retraining the algorithm primarily increased the sensitivity at each site. Conclusion Electronic phenotype algorithms allow rapid identification of case populations in multiple sites with little retraining. Electronic health records (EHR) can improve patient care and safety, reduce costs, and improve guideline adherence. As EHR contain a longitudinal record of patient disease, treatment, and outcomes, EHR can also be a valuable tool for conducting clinical and genomic research studies. Several recent studies have demonstrated that genomic research can be performed using subjects derived entirely from EHR. 1e5 Typically, research populations are derived using 'phenotype algorithms' that combine structured data with unstructured, narrative data from the EHR. These algorithms often take significant human effort and time to develop, requiring domain expertise, programming skills, and iterative evaluation and development. Given the potentially significant up-front development cost, it is of interest to determine if such algorithms can be easily ported to new institutions. The accuracy of such phenotype algorithms applied across multiple institutions with heterogeneous EHR has not been broadly evaluated, although recent work in the Electronic Medical Records and Genomics (eMERGE) Network has demonstrated this for some algorithms. 5a 5b Rheumatoid arthritis (RA) is the most common autoimmune inflammatory arthritis worldwide and affects 1.3 million adults in the USA. It has previously been studied using phenotype algorithms to identify EHR case cohorts. 1 2 7 Early genetic studies of EHR-linked cohorts of RA patients have been replicated in known associations. 1 2 Further development of collections of EHR-linked cohorts for RA and other phenotypes may enable not only enhanced understanding of disease risks but also the investigation of outcomes and treatment responses. Previous phenotyping studies have demonstrated some of the challenges to defining populations retrospectively in the EHR. Liao et al 7 developed an electronic algorithm to identify RA patients using logistic regression operating on billing codes, laboratory and medication data, and natural language processing (NLP) concepts, with a 94% positive predictive value (PPV) and sensitivity of 63%. In this study, we test the portability of a trained algorithm developed at one institution to identify RA status for patients at two separate institutions using independent EHR systems. We demonstrate that this algorithm can be successfully ported to new institutions while maintaining a high PPV. Algorithm portability could eliminate a significant amount of redundant effort and allow the collection of larger, more homogenous disease cohorts from multiple sites.

The AMIA Public Health Informatics 2011 Conference brought together members of the public health and health informatics communities to revisit the national agenda developed at the AMIA Spring Congress in 2001, assess the progress that has been made in the past decade, and develop recommendations to further guide the field. Participants met in five discussion tracks: technical framework; research and evaluation; ethics; education, professional training, and workforce development; and sustainability. Participants identified 62 recommendations, which clustered into three key themes related to the need to (1) enhance communication and information sharing within the public health informatics community, (2) improve the consistency of public health informatics through common public health terminologies, rigorous evaluation methodologies, and competency-based training, and (3) promote effective coordination and leadership that will champion and drive the field forward. The agenda and recommendations from the meeting will be disseminated and discussed throughout the public health and informatics communities. Both communities stand to gain much by working together to use these recommendations to further advance the application of information technology to improve health.

Advances in computing power and new computational techniques have changed the way researchers approach biology, medicine, and indeed all of science. In biomedicine, one of the most fruitful approaches has been to use software tools and knowledge resources known as ontologies-machineprocessable descriptions of scientific domains-that can promote the integration of disparate data sources. We have shown that such resources can enable data aggregation, improve search, and allow the detection of new associations that were previously not detectable. It is now possible to demonstrate computationally correlations among genes, diseases, treatments, and outcomes, to use these correlations to efficiently direct research into potentially fruitful areas, and to translate the insights from this research to the practice of medicine. Achieving these integrative analyses requires software systems that take advantage of the semantics of these areas and that can intelligently negotiate domains and knowledge sources, identifying commonality across systems that use different and conflicting vocabularies, while understanding apparent differences that may be concealed by the use of superficially similar terms. 1 An appropriate ontology provides the cornerstone of software for bridging systems, domains, and resources. 2 Ontologies are the foundation of all semantic technologies in e-science, and are a critical component of multi-disciplinary and translational research in biomedicine. The National Center for Biomedical Ontology (NCBO) has become a leading scientific organization for bringing semantic technology to biomedicine. With core performance sites at Stanford University, the Mayo Clinic, the University of Victoria, and the University at Buffalo, our team works to create and disseminate national infrastructure that supports the use of computer-stored knowledge in the form of ontologies.

Goal-Oriented Requirements Engineering (GORE) has grown into an important area of research in the past decades. Still, some of its corners remain dark, since different GORE languages do not provide a well-founded conceptualization of the domain and are not consensual. This may lead to ambiguous or weak understanding of GORE concepts. In this paper, we introduce the Goal-Oriented Requirements Ontology (GORO), a domain ontology founded on the Unified Foundational Ontology (UFO) that intends to represent the nature and relations of concepts surrounding the GORE domain. We use GORO to explore and clarify the semantics used, sometimes implicitly, by well-known GORE languages.

Capabilities to exchange health information are critical to accelerate discovery and its diffusion to healthcare practice. However, the same ethical and legal policies that protect privacy hinder these data exchanges, and the issues accumulate if moving data across geographical or organizational borders. This can be seen as one of the reasons why many health technologies and research findings are limited to very narrow domains. In this paper, we compare how using and disclosing personal data for research purposes is addressed in Australian, Austrian, Finnish, Swiss, and US policies with a focus on text data analytics. Our goal is to identify approaches and issues that enable or hinder international health information exchanges. As expected, the policies within each country are not as diverse as across countries. Most policies apply the principles of accountability and/or adequacy and are thereby fundamentally similar. Their following requirements create complications with re-using a...

Documentation of archaeological and cultural heritage sites is at the heart of the archaeological process and an important component in cultural heritage research and presentation; it is an essential step without which interpretation and analysis are not possible. It is what makes archaeology and cultural heritage “scientific”. Archaeological knowledge is an important part of human knowledge and it is necessary in many applications, such as knowledge-based systems for archaeology. A formal, clear and declarative description for communicating, sharing and reusing archaeological knowledge among humans and software entities is of crucial importance. Formal ontologies have been viewed as a promising means to tackle this problem. An ontology is an explicit formal declaration of how to represent object concepts and other classes assumed to exist in some area of interest (a domain) and the relationships between them. In this sense an ontology is a specification of a conceptualization. A do...

The Arden Syntax is an HL7 standard language for representing medical knowledge as logic statements. Despite nearly 2 decades of availability, Arden Syntax has not been widely used. This has been attributed to the lack of a generally available compiler to implement the logic, to Arden's complex syntax, to the challenges of mapping local data to data references in the Medical Logic Modules (MLMs), or, more globally, to the general absence of decision support in healthcare computing. An XML representation (ArdenML) may partially address the technical challenges. MLMs created in ArdenML can be converted into executable files using standard transforms written in the Extensible Stylesheet Language Transformation (XSLT) language. As an example, we have demonstrated an approach to executing MLMs written in ArdenML using the Drools business rule management system. Extensions to ArdenML make it possible to generate a user interface through which an MLM developer can test for logical errors.

Objectives Electronic health records (EHR) can allow for the generation of large cohorts of individuals with given diseases for clinical and genomic research. A ratelimiting step is the development of electronic phenotype selection algorithms to find such cohorts. This study evaluated the portability of a published phenotype algorithm to identify rheumatoid arthritis (RA) patients from EHR records at three institutions with different EHR systems. Materials and Methods Physicians reviewed charts from three institutions to identify patients with RA. Each institution compiled attributes from various sources in the EHR, including codified data and clinical narratives, which were searched using one of two natural language processing (NLP) systems. The performance of the published model was compared with locally retrained models. Results Applying the previously published model from Partners Healthcare to datasets from Northwestern and Vanderbilt Universities, the area under the receiver operating characteristic curve was found to be 92% for Northwestern and 95% for Vanderbilt, compared with 97% at Partners. Retraining the model improved the average sensitivity at a specificity of 97% to 72% from the original 65%. Both the original logistic regression models and locally retrained models were superior to simple billing code count thresholds. Discussion These results show that a previously published algorithm for RA is portable to two external hospitals using different EHR systems, different NLP systems, and different target NLP vocabularies. Retraining the algorithm primarily increased the sensitivity at each site. Conclusion Electronic phenotype algorithms allow rapid identification of case populations in multiple sites with little retraining.

Objective Although trait-associated genes identified as complex versus single-gene inheritance differ substantially in odds ratio, the authors nonetheless posit that their mechanistic concordance can reveal fundamental properties of the genetic architecture, allowing the automated interpretation of unique polymorphisms within a personal genome.

The Arden Syntax is an HL7 standard language for representing medical knowledge as logic statements. Despite nearly 2 decades of availability, Arden Syntax has not been widely used. This has been attributed to the lack of a generally available compiler to implement the logic, to Arden's complex syntax, to the challenges of mapping local data to data references in the Medical Logic Modules (MLMs), or, more globally, to the general absence of decision support in healthcare computing. An XML representation (ArdenML) may partially address the technical challenges. MLMs created in ArdenML can be converted into executable files using standard transforms written in the Extensible Stylesheet Language Transformation (XSLT) language. As an example, we have demonstrated an approach to executing MLMs written in ArdenML using the Drools business rule management system. Extensions to ArdenML make it possible to generate a user interface through which an MLM developer can test for logical errors.

Goal-Oriented Requirements Engineering (GORE) has grown into an important area of research in the past decades. Still, some of its corners remain dark, since different GORE languages do not provide a well-founded conceptualization of the domain and are not consensual. This may lead to ambiguous or weak understanding of GORE concepts. In this paper, we introduce the Goal-Oriented Requirements Ontology (GORO), a domain ontology founded on the Unified Foundational Ontology (UFO) that intends to represent the nature and relations of concepts surrounding the GORE domain. We use GORO to explore and clarify the semantics used, sometimes implicitly, by well-known GORE languages.

MISSION Advances in computing power and new computational techniques have changed the way researchers approach biology, medicine, and indeed all of science. In biomedicine, one of the most fruitful approaches has been to use software tools and knowledge resources known as ontologies-machineprocessable descriptions of scientific domains-that can promote the integration of disparate data sources. We have shown that such resources can enable data aggregation, improve search, and allow the detection of new associations that were previously not detectable. It is now possible to demonstrate computationally correlations among genes, diseases, treatments, and outcomes, to use these correlations to efficiently direct research into potentially fruitful areas, and to translate the insights from this research to the practice of medicine. Achieving these integrative analyses requires software systems that take advantage of the semantics of these areas and that can intelligently negotiate domains and knowledge sources, identifying commonality across systems that use different and conflicting vocabularies, while understanding apparent differences that may be concealed by the use of superficially similar terms. 1 An appropriate ontology provides the cornerstone of software for bridging systems, domains, and resources. 2 Ontologies are the foundation of all semantic technologies in e-science, and are a critical component of multidisciplinary and translational research in biomedicine. 3 The National Center for Biomedical Ontology (NCBO) has become a leading scientific organization for bringing semantic technology to biomedicine. With core performance sites at Stanford University, the Mayo Clinic, the University of Victoria, and the University at Buffalo, our team works to create and disseminate national infrastructure that supports the use of computer-stored knowledge in the form of ontologies.

Objective To extract physician-asserted drug side effects from electronic medical record clinical narratives. Materials and methods Pattern matching rules were manually developed through examining keywords and expression patterns of side effects to discover an individual side effect and causative drug relationship. A combination of machine learning (C4.5) using side effect keyword features and pattern matching rules was used to extract sentences that contain side effect and causative drug pairs, enabling the system to discover most side effect occurrences. Our system was implemented as a module within the clinical Text Analysis and Knowledge Extraction System. Results The system was tested in the domain of psychiatry and psychology. The rule-based system extracting side effects and causative drugs produced an F score of 0.80 (0.55 excluding allergy section). The hybrid system identifying side effect sentences had an F score of 0.75 (0.56 excluding allergy section) but covered more side effect and causative drug pairs than individual side effect extraction. Discussion The rule-based system was able to identify most side effects expressed by clear indication words. More sophisticated semantic processing is required to handle complex side effect descriptions in the narrative. We demonstrated that our system can be trained to identify sentences with complex side effect descriptions that can be submitted to a human expert for further abstraction. Conclusion Our system was able to extract most physician-asserted drug side effects. It can be used in either an automated mode for side effect extraction or semi-automated mode to identify side effect sentences that can significantly simplify abstraction by a human expert.

Objective To develop an algorithm for the discovery of drug treatment patterns for endocrine breast cancer therapy within an electronic medical record and to test the hypothesis that information extracted using it is comparable to the information found by traditional methods. Materials The electronic medical charts of 1507 patients diagnosed with histologically confirmed primary invasive breast cancer. Methods The automatic drug treatment classification tool consisted of components for: (1) extraction of drug treatment-relevant information from clinical narratives using natural language processing (clinical Text Analysis and Knowledge Extraction System); (2) extraction of drug treatment data from an electronic prescribing system; (3) merging information to create a patient treatment timeline; and (4) final classification logic. Results Agreement between results from the algorithm and from a nurse abstractor is measured for categories: (0) no tamoxifen or aromatase inhibitor (AI) treatment; (1) tamoxifen only; (2) AI only; (3) tamoxifen before AI; (4) AI before tamoxifen; (5) multiple AIs and tamoxifen cycles in no specific order; and (6) no specific treatment dates. Specificity (all categories): 96.14%e100%; sensitivity (categories (0)e(4)): 90.27%e99.83%; sensitivity (categories (5)e(6)): 0e23.53%; positive predictive values: 80%e97.38%; negative predictive values: 96.91%e99.93%. Discussion Our approach illustrates a secondary use of the electronic medical record. The main challenge is event temporality. Conclusion We present an algorithm for automated treatment classification within an electronic medical record to combine information extracted through natural language processing with that extracted from structured databases. The algorithm has high specificity for all categories, high sensitivity for five categories, and low sensitivity for two categories.

The National Center for Biomedical Ontology is now in its seventh year. The goals of this National Center for Biomedical Computing are to: create and maintain a repository of biomedical ontologies and terminologies; build tools and web services to enable the use of ontologies and terminologies in clinical and translational research; educate their trainees and the scientific community broadly about biomedical ontology and ontology-based technology and best practices; and collaborate with a variety of groups who develop and ...