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The rapid growth in Machine-to-Machine communication have facilitated advanced Internet of Things (IoT) based solutions to redefine several applications. Healthcare models have been modernized with IoT, termed as Internet of Medical Things (IoMT). The IoMT can be otherwise termed as healthcare IoT, which can be defined as collection of medical sensors and applications that are integrated via heterogeneous networks. Numerous healthcare providers employ IoMT solutions to enable the development of diseases and medications, enhance treatment procedures and the patient experience, and minimize costs and errors. IoMT systems permit remote monitoring of patients. Thus, it is able to deliver prompt diagnostics that can save a patient's life in the event of an emergency. Nevertheless, the security of these vital systems is a key obstacle to their widespread adoption. The first step in this direction is to detect an attack that an adversary or unauthorized person can use to get access to and manipulate medical data. This study provides state-of-the-art solutions for securing IoMT data acquisition, transmission, and storage. A comprehensive review of the potential physical and network attacks on IoMT systems is provided. The paper reveals that the majority of security countermeasures are effective against a variety of assaults. Consequently, a security architecture that integrates many security methodologies is also be presented. [ABSTRACT FROM AUTHOR]

Chronic Kidney Failure is the medical term for chronic kidney disease. It portrays the moderate disintegration of renal disappointment and how, assuming that constant kidney infection has advanced to a high-level stage, a high volume of fluid and undesirable electrolytes may develop in the body. We may see less evidence of chronic renal disease in the early phases. The treatment for chronic kidney disease focuses on slowing down the process of kidney damage. Without a trace of dialysis or kidney migration, persistent renal sickness can advance to the last periods of kidney annihilation, which is inoperable. The focal point of this examination is on early discovery of constant obstructive pneumonia illness utilizing different AI techniques, which are K-Nearest Neighbor, Decision Tree and Bayesian Classifier. [ABSTRACT FROM AUTHOR]

Medical records are official and legal documents that have legal value. Medical records have important aspects both for patients and for providers of health services where these patients get services. Medical records must be complete and readable including for codification of diagnoses and procedures. Inaccurate codes, especially for a claim, will result in miss reporting for reimbursmen purposes. The purpose of this research was to analyze the effect of clinical documentation on the accuracy of diagnosis codes in the inpatient medical records of Dr. Soekardjo Hospital Tasikmalaya City. The factors researched are completeness of data, the specificity of diagnosis, clarity of writing, and use of medical terms on Accuracy diagnosis code. The analysis to be carried out is univariate, Chi-square, and multivariate analysis. The results of the study there are a significant effect of the completeness of data and information, the specificity of the diagnosis and the clarity of writing the diagnosis on the accuracy of the diagnosis code, and the most dominant factor affecting the accuracy of the diagnosis code is the factor of writing clarity with p-value 0,000 and OR value of 12.543 [ABSTRACT FROM AUTHOR]

SNOMED CT is a comprehensive medical ontology used in health care sectors across the world covering a wide range of concepts that support diversity at the point of healthcare. However, not all these concepts are needed for every use case; it is better to concentrate on those parts that apply to the particular application while preserving the meaning of relevant concepts. This paper considers the application of a novel subontology extraction method to create a new resource, called the IPS terminology, which functions as a standalone ontology with the same features as SNOMED CT, but is designed for cross-border patient care. The IPS terminology has been released for free use under an open license, with the intention of promoting interoperability of health information worldwide. [ABSTRACT FROM AUTHOR]

Medical terminology has an extensive and rich history in Latin and Greek languages. When Romans conquered Greece, the knowledge and language of both cultures merged, resulting in new medical concepts regarding disease treatment and containment. Medical records were chronicled by hand, creating medical terms and books. Although medical terms have been drawn from many languages, a large majority originate from Greek and Latin. Terms of Greek origin occur mainly in clinical terminology, while Latin terms make up the majority of anatomical terminology. Another reason for a large number of Greek medical terms is that the Greek language is quite suitable for building compound words. The article discusses the current state of anatomical terminology in Lithuania. The history of the Lithuanian nomenclature of anatomy dates back several centuries, during which the most frequently used Lithuanian anatomical terms were gradually developed. Every time, writing and publishing textbooks, methodological aids, and other anatomy books in the Lithuanian language, the authors interpreted various Latin anatomy terms in their own way and introduced new equivalents in the Lithuanian language. However, they often did not agree on the translation of various Latin terms into Lithuanian and their application to define various structures. The development and perfection of medical terminology is a long process. The most significant contribution in regulating Lithuanian anatomical terminology was made by Jurgis Žilinskas. The terms that we currently use can be found in his textbooks "Osteologija ir syndesmologija" ("Osteology and syndesmology") (1932) and "Splanchnologija" ("Splanchnology") (1934) (Litevkiene, Korosteliova 2012, 208). He initiated term regulation in his first textbook, "Lectures of Neurology" (1923), containing only Latin terms, well-formed according to Baseler's nomenclature. The nomenclature of anatomy compiled by him was applied in other anatomy textbooks and the Dictionary of Medical Terms. [ABSTRACT FROM AUTHOR]

Bones make up the skeleton of the body by attachment points for muscles, which allows human to run, jump, and do actions. It also protects organs from potential damages. Fracture is a medical term for a broken bone when bone has an outside force exerted upon it, there is possibility that the bone cannot withstand the amount of force and break it. These fractures can treat by implanting internal fixations at the fractures such as screw and plates. To diagnose the fractures and follow up the treatment, x-ray taken at the fractured area. X-ray image is one of the oldest photographic films that is mostly used in medical diagnosis and treatment. X-ray image is a very useful modality for the physicians and doctors to determine and analyse the bone fracture, which is an important symptom used for diagnosis, therefore x-ray produce only medium quality image, which will normally affect the information of the image. This paper aims to use a new method to detect the screw implants on human bone by using image binarization technique. Image binarization is the process of separating pixel values into a couple of groups, black as foreground and white as background. Image binarization is an important step in image thresholding. The main objective is to find a new method of image thresholding to of object detection. The method is by developing a new algorithm of image thresholding by making the already exist algorithm as references. The resulting method was analysis based on accuracy, sensitivity, and specificity. [ABSTRACT FROM AUTHOR]

Copyright of Language for International Communication: Linking Interdisciplinary Perspectives is the property of University of Latvia and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the early 1990s, most medical records were a hybrid of paper and electronic data deployed on combinations of mainframe and personal computers (Evans, 2016). The complexity and inadequacies of the mixed paper-electronic medical records was the driver behind the Institute of Medicine’s call to shift to a complete electronic medical record (EMR) system (Institute of Medicine, 1997). Advances in computing technology and the Internet during the last two decades has made access to health information online possible. However, as noted by Adler-Milstein (2017), the major technical issue still to be overcome is lack of semantics interoperability. This paper summarizes current approaches to resolving medical terminology differences, and identifies the interoperability errors driving the interoperability problem. The paper then reviews the theory behind quantum ontology primitives, proposes a new method for developing core reference hierarchical quantum ontology primitive trees integrated with ontology learning to mitigate interoperability errors among the primary medical terminologies, and discusses the potential for this method to increase semantics coverages among the medical terminologies. [ABSTRACT FROM AUTHOR]

Detection of difficult for understanding words is a crucial task for ensuring the proper understanding of medical texts such as diagnoses and drug instructions. We propose to combine supervised machine learning algorithms using various features with word embeddings which contain context information of words. Data in French are manually cross-annotated by seven annotators. On the basis of these data, we propose cross-validation scenarios in order to test the generalization ability of models to detect the difficulty of medical words. On data provided by seven annotators, we show that the models are generalizable from one annotator to another. [ABSTRACT FROM AUTHOR]

The review of pathology test results for missed diagnoses in Emergency Departments is time-consuming, laborious, and can be inaccurate. An automated solution, with text mining and clinical terminology semantic capabilities, was developed to provide clinical decision support. The system focused on the review of microbiology test results that contained information on culture strains and their antibiotic sensitivities, both of which can have a significant impact on ongoing patient safety and clinical care. The system was highly effective at identifying abnormal test results, reducing the number of test results for review by 92%. Furthermore, the system reconciled antibiotic sensitivities with documented antibiotic prescriptions in discharge summaries to identify patient follow-ups with a 91% F-measure – allowing for the accurate prioritization of cases for review. The system dramatically increases accuracy, efficiency, and supports patient safety by ensuring important diagnoses are recognized and correct antibiotics are prescribed. [ABSTRACT FROM AUTHOR]

Discovering disease similarities are beneficial for the diagnosis and treatment of mental diseases. In this research, we proposed a data driven method, that is, integrating a variety of publicly available data resources including Unified Medical Language System (UMLS) Metathesaurus, Systematized Nomenclature of Medicine -- Clinical Terms (SNOMED CT) and cui2vec concept embedding to construct a mental disease similarity network. The resulting mental disease similarity network offered a new view for navigating and investigating disease relations; it also revealed popular mental disease in the literature in terms of the number of connections and similarities with other diseases. It shows that depressive disorder is directly connected with nine other popular diseases and connects 52 other diseases in the network. The top three popular mental diseases are depressive disorder, dysthymia (now known as persistent depressive disorder), and neurosis. Future research will focus on studying the clusters generated from the similarity network. [ABSTRACT FROM AUTHOR]

Medical terminologies are the basis of interoperability in medicine. They allow connecting the various systems and data and facilitate searches in databases. An example is the MedDRA terminology, used in particular for coding drug adverse events. However, these terminologies are often complex and involve a huge number of terms. Consequently, it is difficult to browse them or find the desired terms. Traditional approaches consist of lexical search, with the problems of synonymy and polysemy, or tree-based navigation, but the user often gets “lost” in the tree. Here, we propose a new approach for browsing medical terminologies: the use of pictograms and icons, for formulating the query in complement to a textual search box, and for displaying the search results. We applied this approach to the MedDRA terminology. We present both the methods and search algorithms and the resulting browsing interface, as well as the qualitative opinions of two pharmacovigilance experts. [ABSTRACT FROM AUTHOR]

An attractive feature of non-lattice-based ontology auditing methods is its ability to not only identify potential quality issues, but also automatically generate the corresponding fixes. However, exhaustive manual evaluation of the validity of suggested changes remains a challenge shared with virtually all auditing methods. To address this challenge, we explore machine learning techniques as an aid to systematically evaluate the strength of auto-suggested relational changes in the context of existing knowledge embedded in an ontology. We introduce a hybrid convolutional neural network and multilayer perception (CNN-MLP) classifier using a combination of graph, concept features and concept embeddings. We use lattice subgraphs to generate a curated, loosely-coupled training set of positive and negative instances to train the classifier. Our result shows that machine learning techniques have the potential to alleviate the manual effort required for validating and confirming changes generated by non-lattice-based auditing methods for SNOMED CT. [ABSTRACT FROM AUTHOR]

The Electronic Data Interchange (EDI) medical procedure code is the code used for health insurance claims in Korea. We mapped Korean EDI codes to SNOMED CT to explore the global interoperability of health insurance claims data. We developed rules for mapping based on the mapping guideline provided by SNOMED CT International. The first and second authors mapped 726 EDI codes used to claim reimbursement in five specialty areas to SNOMED CT. Eight subject matter experts reviewed the mapping results. Out of 726 procedure codes, 82.5% were exactly or partially mapped to SNOMED CT. An EDI code was mapped to an average of 2.04 SNOMED CT concepts. Twenty-one attributes were identified in the EDI codes mapped to SNOMED CT concepts. We identified strategies to improve the EDI code in this study. They include introducing hierarchical structures, adding inclusion and exclusion criteria for procedure codes, and improving EDI code labels. [ABSTRACT FROM AUTHOR]

We describe the process of creating a User Interface Terminology (UIT) with the goal to generate a maximum of German language interface terms that are mapped to the reference terminology SNOMED CT. The purpose is to offer a high coverage of medical jargon in order to optimise semantic annotations of clinical documents by text mining systems. The first step consisted in the creation of an n-gram table to which words and short phrases from the English SNOMED CT description table were automatically extracted and entered. The second step was to fill up the n-gram table with human and machine translations, manually enriched by POS tags. Top-down and bottom-up methods for manual terminology population were used. Grammar rules were formulated and embedded into a term generator, which then created one-tomany German variants per SNOMED CT description. Currently, the German user interface terminology contains 4,425,948 entries, created out of 111,605 German n-grams, assigned to 95,298 English n-grams. With 341,105 active concepts and 542,462 (non FSN) descriptions, it corresponds to an average of 13 interface terms per concept and 8.2 per description. An analysis of the current quality of this resource by blinded human assessment terminology states equivalence regarding term understandability compared to a fully automated Web-based translator, which, however does not yield any synonyms, so that there are good reasons to further develop this semi-automated terminology engineering method and recommend it for other language pairs. [ABSTRACT FROM AUTHOR]

A method is described to use SNOMED CT’s history mechanism as a means to compute how the formal and linguistic intensions of its concepts change over versions. As a result of this, it is demonstrated that the intended principle of concept permanence is not always adhered to. It is shown that the evolution of formal intensions can be monitored fully automatically and that the proposed procedure includes a method to suggest missing subsumers in a concept’s transitive closure set by identifying mistakes that have been made in the past. Changes in linguistic intensions were found to be much more labor-intensive to identify. It is suggested that this could be improved if the history mechanism would come with more detailed motivations for change than the current and insufficiently used annotation to the effect that a fully specified name ‘fails to comply with the current editorial guidance’. [ABSTRACT FROM AUTHOR]

Medical Latin has been taught to medical students in Riga Stradiņš University (Latvia) since the foundation of the university in 1950. In the 21st century, along with the development of technologies, the Medical Latin course has undergone significant changes. E-assessment has been acknowledged as more convenient than traditional one. The previous study, which was carried out at the university in 2017, clearly showed no statistically significant difference of opinions on such e-test taking factors as immediate feedback, paper and time economy, stress and additional influencing factors among groups of students. During the academic year 2017/2018, the design of e-tests was improved in the light of recommendations of the teaching staff. The present study deals with the analyses of the results of e-tests (195 to 220 students involved) to find out to what extent they differ in different types of tasks and semesters. Several hypotheses were put forward and statistical methods were used to test them. Differences were found between the scores in several different task types and in student groups in different semesters, however, the total scores of both semesters did not differ. Further research should be related to the identification of students' needs in accordance with the studentcentred and assessment-centred aspects. [ABSTRACT FROM AUTHOR]

Recent years have seen the rapid increase in digitised medical information. In particular, the massive expansion of Electronic Health Records (EHRs), which are designed to document all information that is clinically relevant in a patient's use of a healthcare facility, has introduced unprecedented volumes of relatively unstructured data. This paper intends to determine the extent to which knowledge discovery in relation to both abbreviations and acronyms within heterogeneous data can be achieved. Heterogeneous data such as the narrative-based free-text notes found within patients' EHRs may use inconsistent ways to indicate contractions within the text and may use non-standard definitions for both abbreviations and acronyms. We approached this task through the retrieval and classification of contractions as well as using a novel method of combining multiple publicly available repositories. In order to provide better coverage of abbreviations, and also to address the issue of neologisms in general, word embeddings were applied to find semantically similar lexemes. [ABSTRACT FROM AUTHOR]

The research is dedicated to the peculiarities of advertising language in Latvian medical journals from 1989 to 2014. The authors analysed several types of messages in the advertisements of medical devices and services, interaction of image and text, the role of image in filling the message with values and attitude. Analysis of the terms applied in the advertisements indicates an ongoing active process of term formation in the field of medicine, which is characterised by insufficient systematisation of the linguistic material and inconsistent usage of various term formation methods. [ABSTRACT FROM AUTHOR]

Copyright of Word: Aspects of Research: Conference Proceedings is the property of Liepaja University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Word: Aspects of Research: Conference Proceedings is the property of Liepaja University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Eligibility criteria are important for clinical research protocols or clinical practice guidelines for determining who qualify for studies and to whom clinical evidence is applicable, but the free-text format is not amenable for computational processing. In this paper, we described a practical method for transforming free-text clinical research eligibility criteria of Alzheimer's clinical trials into a structured relational database compliant with standards for medical terminologies and clinical data models. We utilized a hybrid natural language processing system and a concept normalization tool to extract medical terms in clinical research eligibility criteria and represent them using the OMOP Common Data Model (CDM) v5. We created a database schema design to store syntactic relations to facilitate efficient cohort queries. We further discussed the potential of applying this method to trials on other diseases and the promise of using it to accelerate clinical research with electronic health records. [ABSTRACT FROM AUTHOR]

The time has come to end unproductive competitions among different types of biomedical terminology artefacts. Tools and strategies to create the foundation of a seamless environment covering clinical jargon, clinical terminologies and classifications are necessary. Whereas language processing relies on human interface terminologies, which represent clinical jargon, their link to reference terminologies such as SNOMED CT is essential to guarantee semantic interoperability. There is also a need for interoperation between reference and aggregation terminologies. Simple mappings between nodes are not enough, because the three kinds of terminology systems represent different things: reference terminologies focus on context-free descriptions of classes of entities of a domain; aggregation terminologies contain rules that enforce the principle of single hierarchies and disjoint classes; interface terminologies represent the language used in a domain. We propose a model that aims at providing a better flow of standardized information, addressing multiple use cases in health care including clinical research, epidemiology, care management and reimbursement. [ABSTRACT FROM AUTHOR]

The National Cancer Institute Thesaurus (NCIt), developed and maintained by the National Cancer Institute, is an important reference terminology in the cancer domain. As a controlled terminology needs to continuously incorporate new concepts to enrich its conceptual content, automated and semi-automated methods for identifying potential new concepts are in high demand. We have previously developed a topological-pattern-based method for identifying new concepts in a controlled terminology to enrich another terminology, using the UMLS Metathesaurus. In this work, we utilize this method with the National Cancer Institute Metathesaurus to identify new concepts for NCIt. While previous work was only oriented towards identifying candidate import concepts for human review, we are now also adding an algorithmic method to evaluate candidate concepts and reject a well defined group of them. [ABSTRACT FROM AUTHOR]

Value sets (VSs) used in electronic clinical quality measures are lists of codes from standard terminologies ("extensional" VSs), whose purpose ("intension") is not always explicitly stated. We elicited the intension for the 09/01/2014 release of extensional medication value sets by comparison to drug classes from the October 2014 release of RxClass. Value sets matched drug classes if they shared common ingredients, as evidenced by Jaccard similarity score. We elicited the intension of 80 extensional value sets. The average Jaccard similarity was 0.65 for single classes and 0.80 for combination classes, with 34% (27/80) of the value sets having high similarity scores. Manual review by a pharmacist indicated 51% (41/80) of the drug classes selected as the best mapping for a value set matched the intension reflected in that value set name. This approach has the potential for facilitating the development and maintenance of medication value sets. [ABSTRACT FROM AUTHOR]

Search techniques in clinical text need to make fine-grained semantic distinctions, since medical terms may be negated, about someone other than the patient, or at some time other than the present. While natural language processing (NLP) approaches address these fine-grained distinctions, a task like patient cohort identification from electronic health records (EHRs) simultaneously requires a much more coarse-grained combination of evidence from the text and structured data of each patient's health records. We thus introduce aligned-layer language models, a novel approach to information retrieval (IR) that incorporates the output of other NLP systems. We show that this framework is able to represent standard IR queries, formulate previously impossible multi-layered queries and customize the desired degree of linguistic granularity. [ABSTRACT FROM AUTHOR]

Standardized terminology is the prerequisite of data exchange in analysis of clinical processes. However, data from different electronic health record systems are based on idiosyncratic terminology systems, especially when the data is from different hospitals and healthcare organizations. Terminology standardization is necessary for the medical data analysis. We propose a crowdsourcing-based terminology mapping method, CrowdMapping, to standardize the terminology in medical data. CrowdMapping uses a confidential model to determine how terminologies are mapped to a standard system, like ICD- 10. The model uses mappings from different health care organizations and evaluates the diversity of the mapping to determine a more sophisticated mapping rule. Further, the CrowdMapping model enables users to rate the mapping result and interact with the model evaluation. CrowdMapping is a work-in-progress system, we present initial results mapping terminologies. [ABSTRACT FROM AUTHOR]

Medical Ontologies play a central role in integrating heterogeneous databases of various model organisms. Hepatobiliary system is very important to human vital processes. It has an ability to regulate the other systems. Furthermore, it may be affected by many pathologic conditions, which affect other organs negatively. This paper investigates the current studies on Ontological engineering approach and Ontology techniques for Hepatobiliary System Diseases. We present conceptual view for the Hepatobiliary system and its infected diseases. Besides, we propose a new classification schema for the research efforts investigated so far. We classified the research efforts investigated so far based on the Hepatobiliary system organs: Liver, Gallbladder, Bile duct and Pancreas. Besides, we discuss the current research gaps found in this research area. [ABSTRACT FROM AUTHOR]

Situation of aging and appearance of the 1st generation of elderly who prefer other consumer practices than medical services reflects in the article. The article highlights the results of our study which deals with the elderly, their information practices in Russia and trend of its utilization. The main goal of our quality research on a base 22 deep interviews was to evaluate the need of elderly computer users to continue them elaboration. The analysis of computer literacy training programs for elderly people led us to the conclusion that are not oriented as this goal and so on to the conclusion to e-government services. Such training is likely become a new prestigious sort of leisure for elderly, it corresponds to the logic of the consumer society". We continue the researches which subject wrote in the paper in SGEM-14. [ABSTRACT FROM AUTHOR]

Due to fundamental differences in design and editorial policies, semantic interoperability between two de facto standard terminologies in the healthcare domain - the International Classification of Diseases (ICD) and SNOMED CT (SCT), requires combining two different approaches: (i) axiom-based, which states logically what is universally true, using an ontology language such as OWL; (ii) rule-based, expressed as queries on the axiom-based knowledge. We present the ICD-SCT harmonization process including: a) a new architecture for ICD-11, b) a protocol for the semantic alignment of ICD and SCT, and c) preliminary results of the alignment applied to more than half the domain currently covered by the draft ICD-11. [ABSTRACT FROM AUTHOR]

The increasing adoption of Electronic Health Records (EHR) by developing countries comes with the need to develop common terminology standards to assure semantic interoperability. In Kenya, where the Ministry of Health has rolled out an EHR at 646 sites, several challenges have emerged including variable dictionaries across implementations, inability to easily share data across systems, lack of expertise in dictionary management, lack of central coordination and custody of a terminology service, inadequately defined policies and processes, insufficient infrastructure, among others. A Concept Working Group was constituted to address these challenges. The country settled on a common Kenya data dictionary, initially derived as a subset of the Columbia International eHealth Laboratory (CIEL) / Millennium Villages Project (MVP) dictionary. The initial dictionary scope largely focuses on clinical needs. Processes and policies around dictionary management are being guided by the framework developed by Bakhshi-Raiez et al. Technical and infrastructure-based approaches are also underway to streamline workflow for dictionary management and distribution across implementations. Kenya's approach on comprehensive common dictionary can serve as a model for other countries in similar settings. [ABSTRACT FROM AUTHOR]

The integration of heterogeneous ontologies is often hampered by different upper level categories and relations. We report on an on-going effort to align clinical terminology/ontology SNOMED CT with the formal upper-level ontology BioTop- Lite. This alignment introduces several constraints at the OWL-DL level. The mapping was done manually by analysing formal and textual definitions. Descriptive logic classifiers interactively checked mapping steps, using small modules for increasing performance. We present an effective workflow, using modules of several scales. However, only part of the classes and relations could easily be mapped. The implications for future evolution of SNOMED CT are discussed. It seems generally feasible to use a highly constrained upperlevel ontology as an upper level for the benefit of future SNOMED CT versions that are more interoperable with other biomedical ontologies. [ABSTRACT FROM AUTHOR]

In this paper, the authors report on a study aimed at harmonising two nursing terminologies, the Clinical Care Classification (CCC) and the International Classification for Nursing Practice (ICNP® ). As the electronic health record evolves and the need for interoperability extends beyond local and national borders, a degree of standardisation across healthcare terminologies become essential. Harmonising across terminologies results in a) increased consensus relating to domain content and b) improvements in the terminologies involved. Findings from this study suggest that there is much overlap of content in nursing terminologies. The continued harmonisation between nursing terminologies and other healthcare terminologies are recommended to achieve international interoperability. [ABSTRACT FROM AUTHOR]

Objectives: To explore automatic methods for the classification of biomedical vocabularies based on their content. Methods: We create semantic group profiles for each source vocabulary in the UMLS and compare the vectors using a Euclidian distance. We explore several techniques for visualizing individual semantic group profiles and the entire distance matrix, including donut pie charts, heatmaps, dendrograms and networks. Results: We provide donut pie charts for individual source vocavularies, as well as a heatmap, dendrogram and network for a subset of 78 vocabularies from the UMLS. Conclusions: Our approach to fingerprinting biomedical terminologies is completely automated and can easily be applied to all source vocabularies in the UMLS, including upcoming versions of the UMLS. It supports the exploration, selection and comparison of the biomedical terminologies integrated into the UMLS. The visualizations are available at (http://mor.-nlm.nih.gov/pubs/supp/2015-medinfo-br/index.html). [ABSTRACT FROM AUTHOR]

Social media sites, such as Twitter, are a rich source of many kinds of information, including health-related information. Accurate detection of entities such as diseases, drugs, and symptoms could be used for biosurveillance (e.g. monitoring of flu) and identification of adverse drug events. However, a critical assessment of performance of current text mining technology on Twitter has not been done yet in the medical domain. Here, we study the development of a Twitter data set annotated with relevant medical entities which we have publicly released. The manual annotation results show that it is possible to perform high-quality annotation despite of the complexity of medical terminology and the lack of context in a tweet. Furthermore, we have evaluated the capability of state-of-the-art approaches to reproduce the annotations in the data set. The best methods achieve F-scores of 55-66%. The data analysis and the preliminary results provide valuable insights on identifying medical entities in Twitter for various applications. [ABSTRACT FROM AUTHOR]

Knowledge of medical-related terminology and communication skills are essential for multinational partners participating in a wide variety of missions. A study was held to identify the vocabulary learning needs, efficient strategies, and opportunities to arrange language assistance through mobile learning. Specific communication situations and terminology gaps were identified during the needs analysis conducted with questionnaires and focus group interviews. Literature research brought to light benefits of m-learning and its relevancy to implement recommended vocabulary learning techniques. A framework for medical terminology assistance and content samples for individual context-related learning are suggested. [ABSTRACT FROM AUTHOR]

Exchange and re-use of data and information is key when seeking semantic interoperability across heterogeneous EHRs. In eHealth communities there is a great focus on designing and implementing interoperable and useful EHRs. Furthermore, a focus on development of comprehensive terminology systems to achieve a common description of clinical concepts exists. However, these efforts are done in parallel and unknown pitfalls surface when integrating massive systems. The objective of this paper addresses the role of terminology systems and EHRs when seeking semantic interoperability across heterogeneous EHRs. We highlight pitfalls and the consequences of these. The methodology used consists of a review of the scientific literature on EHRs and terminology systems followed by a workshop with practitioners conformed to the result of the review. The results indicate one major pitfall: ambiguity. This pitfall has more facets; both multiple mappings and semantic ambiguity. A severe consequence of these can be misinterpretation of meaning and intend with a given concept. [ABSTRACT FROM AUTHOR]

Secondary use of electronic health record (EHR) data requires a detailed description of metadata, especially when data collection and data re-use are organizationally and technically far apart. This paper describes the concept of the SMITH consortium that includes conventions, processes, and tools for describing and managing metadata using common standards for semantic interoperability. It deals in particular with the chain of processing steps of data from existing information systems and provides an overview of the planned use of metadata, medical terminologies, and semantic services in the consortium. [ABSTRACT FROM AUTHOR]

ABSTRACT Recent research using Twitter as an information communication channel has shown how event organizers convey and disseminate their agendas across industries and disciplines. However, little research has been carried out on the user's choice of information components when composing a tweet through the lens of information theory. This research employs a comparative case study to examine how medical-terminology hashtags and corresponding lay-language hashtags have been used to help to communicate healthcare messages on the Twitter platform. The main result of this case study revealed patterns that both retweeting behavior and the use of a variety of components to construct a tweet contribute to higher entropy values which imply that these are more informative ways to communicate healthcare messages. [ABSTRACT FROM AUTHOR]

Context: Arden Syntax is a standard that encodes knowledge as Medical Logic Modules (MLMs) but lacks a standard query data model and terminology. Objective: Assess to what extent SNOMED CT can represent MLM query data elements. Method: 340 MLMs containing 3268 queries were examined. Result: SNOMED can be used to represent all data elements except for clinical decision support system terms such as "alert". Conclusion: SNOMED CT adequately represents data queried using the Arden Syntax. [ABSTRACT FROM AUTHOR]

The purpose of this study is to integrate Japanese medical device adverse event terminologies for evaluating terminological consistency. We represented hierarchy and relations among terms using Resource Description Framework (RDF). There were 3521 classes and 14650 properties. As a result of evaluating the consistency of the description in SPARQL, it was evident that the same notations existed within different terminologies (category terms and terms) and some terms had plural definitions. [ABSTRACT FROM AUTHOR]

We created the Terminology Status Application Programming Interface (API) to assist users in mapping obsolete codes to current RxNorm, SNOMED CT and LOINC concepts. Use cases include support for information retrieval, maintenance of value sets and analytics of legacy clinical databases. Our terminology status APIs typically receive over 4 million calls per month on average. [ABSTRACT FROM AUTHOR]

In order to cover the requirements for interoperability in the Norwegian context, we studied the terminology binding of archetypes to terminology expressions created with the SNOMED-CT compositional grammar. As a result we identified important challenges categorized as technical, expressivity, human and models mismatch. [ABSTRACT FROM AUTHOR]

The article focuses on a study which developed an approach to standardize clinical information based on the Systematized Nomenclature of Medicine--Clinical Terms (SNOMED-CT) model. An extended paper-based partogram used during labour at Aalborg Hospital was the material chosen for standardization. Results of the study showed that majority of the concepts were found in the primary hierarchies. It notes the possible use of the documentation of clinical information as a classification.