Thesis (M.A.(African languages)) --University of Limpopo, 2009. / Tshipikwa tsha ngudo iyi ho vha u sedzulusa zwine vhalala vha nga thusa ngazwo kha u kona u tandulula thaidzo dzine ra vha nadzo ano maḓuvha. Ri tshi sedza miṱa i khou pwashea ḓuvha ḽiṅwe na ḽiṅwe nga ṅwambo wa u shaya ngeletshedzo dza vhalala nga maanḓa vha vhushaka ha malofha. Zwo vhonala kha ino ngudo uri vhaṅwe vhaṅwali na vhasengulusi vho vhona zwi zwa ndeme u kona u ṱalutshedza ndeme ya vhalala na zwine vha eletshedza. U shumiswa ha theminolodzhi ya vhushaka ha malofha zwo kona u vhonala uri nga ngoho, ya swika hune ya tevhelwa na u ṱhonifhiwa zwi ḓo ita uri ḽi kone u lala miṱani yashu. Nga u nyadza ngeletshedzo dzi bvaho kha vhalala zwo sumbedzwa kha ino ngudo uri vhathu vha nga dzula vha tshi vhaisala vhutshiloni havho. Zwenezwo theminolodzhi ya vhushaka ha malofha ndi zwa ndeme uri i ṱhonifhiwe ngauri i ṋea tshirunzi miṱa na uri miṱa ya sa kwashekane na u fhalala.

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Les informations médicales recueillies dans le cadre du soin doivent être utilisables pour répondre à d’autres objectifs plus collectifs. Dans ce contexte de réutilisation des données d’un système d’information clinique pour de la recherche en épidémiologie, l’objectif de ce travail est d’étudier l’apport de l'intéropérabilité sémantique à travers un certain nombre de situations concrêtes que nous avons rencontrées et étudiées et qui illustrent la nature des problèmes de cohérence sémantiques liés au traitement des données médicales et de santé.La coexistence, à un moment donné, de plusieurs référentiels sémantiques ne doit pas être considéré comme un obstacle à l'interopérabilité. Des outils génériques peuvent être conçus et développés pour passer de façon transparente d'un composant à un autre avec aussi peu de perte d’information que possible. L’Unified Medical Language System (UMLS) est un de ses outils d’intégration sémantique. Son usage dans le cadre de ces travaux montre le caractère général de cette méthode et son potentiel pour résoudre cette classe de problèmes d’intéropérabilité sémantique.La richesse de chacune des terminologies permet, lorsqu’elles sont associées dans un même référentiel sémantique pivot, d’enrichir l’ensemble des terminologies prises individuellement pour une meilleur représentation des connaissances.L’interopérabilité sémantique améliore la disponibilité et la qualité des données réutilisables pour des recherches en santé publique. Elle permet d’enrichir les données existantes. Elle fournit les moyens d'accéder à de nouvelles sources de données, agrégées de manière valide, permettant des analyses comparatives ou des analyses plus riches. / Medical information collected during clinical care must be re-used to address other more collective goals. In this context of re-using data from a clinical information system for epidemiological research, the objective of this work is to study the contribution of semantic interoperability across a number of practical situations we have met and discussed which illustrate the nature of semantic consistency problems associated with processing of medical data.Coexistence at a given time, of several semantic repositories should not be considered as an obstacle to interoperability. Generic tools can be designed and developed to move seamlessly from one component to another with as little loss of information as possible. The Unified Medical Language System (UMLS) is one of the semantic integration tools. Its use in this work shows the generality of this method and its potential for solving this class of semantic interoperability problems.The richness of each of the terminology can, when combined into a single pivot semantic repository, enrich the set of terminologies individually for a better representation of knowledge.Semantic interoperability improves the availability and quality of reusable data for public health research. It also enriches existing data. It provides access to new sources of data, aggregated in a valid manner, allowing benchmarking or richer analysis.

A alta disponibilidade de informações em saúde por meio de sistemas de informação só pode ser proporcionada com a utilização de sistemas que sejam capazes de trocar dados de forma segura e consistente. Para isso, estes sistemas necessitam ser interoperáveis, capazes de trocar informações. Uma das características mais importantes de tais sistemas é a utilização de terminologias em saúde, permitindo a codificação dos termos clínicos de maneira robusta e consistente. Algumas das terminologias mais conhecidas e utilizadas são: SNOMED-CT, ICD-CM, ICD, LOINC, NANDA, TUSS, CBHPM, Tabela de Procedimentos SUS, entre outras. Quando os sistemas não se utilizam de uma mesma terminologia para codificação de um mesmo conceito é necessário a realização de mapeamentos e traduções entre as terminologias. O mapeamento entre terminologias consiste em estabelecer as associações pertinentes às terminologias para que cada termo pertencente a uma possa ser associado a algum termo da outra. Este mapeamento, geralmente, é criado por especialistas de domínio, que atuam analisando as duas terminologias em questão e estabelecendo manualmente estas associações. Neste trabalho, propomos uma metodologia que visa facilitar a realização deste tipo de mapeamento, por meio da utilização de dois recursos: Regras de Associação, para extração das associações preexistentes entre as terminologias em registros clínicos; e Busca Textual, para pareamento entre conceitos das duas terminologias baseado na identificação de termos comuns. O auxílio à criação destes mapeamentos é proporcionado por meio de sugestões de relações existentes entre as terminologias. Como resultado deste trabalho obtivemos uma metodologia genérica de mapeamento entre terminologias capaz de auxiliar com sucesso os especialistas. Em aproximadamente 40% dos casos os especialistas concordaram com uma das sugestões apresentadas. De forma complementar, obtivemos o mapeamento parcial entre duas terminologias: a ICD9-CM e a TUSS, utilizadas como caso de uso para validação da metodologia. / The high availability of health information through information systems can be provided only with the use of systems that are able to exchange data securely and consistently. To this end, these systems need to be interoperable, capable of exchanging information that is understood both at one end as the other. One of the most important characteristics of such systems is the use of terminologies in health, allowing the coding of clinical terms in a robust and consistent manner. Some of the most known and used terminologies are: SNOMED-CT, ICD-CM, ICD, LOINC, NANDA, TUSS, CBHPM, and SUS Procedures Table, among others. When systems do not use the same terminology for encoding the same concept, it is necessary to perform mappings and translations between the terminologies. The mapping between terminologies consists on establishing the relevant associations present in terminologies, so that each term belonging to one can be associated unambiguously to the terms belonging to another. This mapping is typically created by domain experts who work analyzing the two terms in question and manually setting these associations. In this paper, we propose a methodology that aims to facilitate this type of mapping, through the use of two frameworks: Association Rules, for the extraction of preexisting associations between the terminologies in clinical records; and Textual Search, for pairing between the two terminologies concepts based on the identification of common terms. The creation of these mappings by experts is aided by the method suggesting links between the terminologies through the Association Rules or Textual Search. As a result of this work we obtained a generic methodology for mapping between terminologies able to successfully assist the experts. In approximately 40% of cases the experts agreed with the suggestions. As a complement, we obtained a partial mapping between two specific terminologies for coding surgical procedures: the ICD9-CM and TUSS, used as use case to validate the methodology.

Dans le domaine de la santé, il existe un nombre très important de sources de connaissances, qui vont de simples terminologies, classifications et vocabulaires contrôlés à des représentations très formelles, que sont les ontologies. Cette hétérogénéité des sources de connaissances pose le problème de l’utilisation secondaire des données, et en particulier de l’exploitation de données hétérogènes dans le cadre de la médecine personnalisée ou translationnelle. En effet, les données à utiliser peuvent être codées par des sources de connaissances décrivant la même notion clinique de manière différente ou décrivant des notions distinctes mais complémentaires.Pour répondre au besoin d’utilisation conjointe des sources de connaissances encodant les données de santé, nous avons étudié trois processus permettant de répondre aux conflits sémantiques (difficultés résultant de leur mise en relation) : (1) l’alignement qui consiste à créer des relations de mappings (équivalence et/ou subsumption) entre les entités des sources de connaissances, (2) l’intégration qui consiste à créer des mappings et à organiser les autres entités dans une même structure commune cohérente et, enfin, (3) l’enrichissement sémantique de l’intégration qui consiste à créer des mappings grâce à des relations transversales en plus de celles d’équivalence et de subsumption.Dans un premier travail, nous avons aligné la terminologie d’interface du laboratoire d’analyses du CHU de Bordeaux à la LOINC. Deux étapes principales ont été mises en place : (i) le prétraitement des libellés de la terminologie locale qui comportaient des troncatures et des abréviations, ce qui a permis de réduire les risques de survenue de conflits de nomenclature, (ii) le filtrage basé sur la structure de la LOINC afin de résoudre les différents conflits de confusion.Deuxièmement, nous avons intégré RxNorm à la sous-partie de la SNOMED CT décrivant les connaissances sur les médicaments afin d’alimenter la SNOMED CT avec les entités de RxNorm. Ainsi, les médicaments dans RxNorm ont été décrits en OWL grâce à leurs éléments définitionnels (substance, unité de mesure, dose, etc.). Nous avons ensuite fusionné cette représentation de RxNorm à la structure de la SNOMED CT, résultant en une nouvelle source de connaissances. Nous avons ensuite comparé les équivalences inférées (entre les entités de RxNorm et celles de la SNOMED CT) grâce à cette nouvelle structure avec les équivalences créées de manière morphosyntaxique. Notre méthode a résolu des conflits de nomenclature mais s’est confrontée à certains conflits de confusion et d’échelle, ce qui a mis en évidence le besoin d’améliorer RxNorm et SNOMED CT.Finalement, nous avons réalisé une intégration sémantiquement enrichie de la CIM10 et de la CIMO3 en utilisant la SNOMED CT comme support. La CIM10 décrivant des diagnostics et la CIMO3 décrivant cette notion suivant deux axes différents (celui des lésions histologiques et celui des localisations anatomiques), nous avons utilisé la structure de la SNOMED CT pour retrouver des relations transversales entre les concepts de la CIM10 et de la CIMO3 (résolution de conflits ouverts). Au cours du processus, la structure de la SNOMED CT a également été utilisée pour supprimer les mappings erronés (conflits de nomenclature et de confusion) et désambiguïser les cas de mappings multiples (conflits d’échelle). / In the biomedical domain, there are almost as many knowledge resources in health as there are application fields. These knowledge resources, described according to different representation models and for different contexts of use, raise the problem of complexity of their interoperability, especially for actual public health problematics such as personalized medicine, translational medicine and the secondary use of medical data. Indeed, these knowledge resources may represent the same notion in different ways or represent different but complementary notions.For being able to use knowledge resources jointly, we studied three processes that can overcome semantic conflicts (difficulties encountered when relating distinct knowledge resources): the alignment, the integration and the semantic enrichment of the integration. The alignment consists in creating a set of equivalence or subsumption mappings between entities from knowledge resources. The integration aims not only to find mappings but also to organize all knowledge resources’ entities into a unique and coherent structure. Finally, the semantic enrichment of integration consists in finding all the required mapping relations between entities of distinct knowledge resources (equivalence, subsumption, transversal and, failing that, disjunction relations).In this frame, we firstly realized the alignment of laboratory tests terminologies: LOINC and the local terminology of Bordeaux hospital. We pre-processed the noisy labels of the local terminology to reduce the risk of naming conflicts. Then, we suppressed erroneous mappings (confounding conflicts) using the structure of LOINC.Secondly, we integrated RxNorm to SNOMED CT. We constructed formal definitions for each entity in RxNorm by using their definitional features (active ingredient, strength, dose form, etc.) according to the design patterns proposed by SNOMED CT. We then integrated the constructed definitions into SNOMED CT. The obtained structure was classified and the inferred equivalences generated between RxNorm and SNOMED CT were compared to morphosyntactic mappings. Our process resolved some cases of naming conflicts but was confronted to confounding and scaling conflicts, which highlights the need for improving RxNorm and SNOMED CT.Finally, we performed a semantically enriched integration of ICD-10 and ICD-O3 using SNOMED CT as support. As ICD-10 describes diagnoses and ICD-O3 describes this notion according to two different axes (i.e., histological lesions and anatomical structures), we used the SNOMED CT structure to identify transversal relations between their entities (resolution of open conflicts). During the process, the structure of the SNOMED CT was also used to suppress erroneous mappings (naming and confusion conflicts) and disambiguate multiple mappings (scale conflicts).

A alta disponibilidade de informações em saúde por meio de sistemas de informação só pode ser proporcionada com a utilização de sistemas que sejam capazes de trocar dados de forma segura e consistente. Para isso, estes sistemas necessitam ser interoperáveis, capazes de trocar informações. Uma das características mais importantes de tais sistemas é a utilização de terminologias em saúde, permitindo a codificação dos termos clínicos de maneira robusta e consistente. Algumas das terminologias mais conhecidas e utilizadas são: SNOMED-CT, ICD-CM, ICD, LOINC, NANDA, TUSS, CBHPM, Tabela de Procedimentos SUS, entre outras. Quando os sistemas não se utilizam de uma mesma terminologia para codificação de um mesmo conceito é necessário a realização de mapeamentos e traduções entre as terminologias. O mapeamento entre terminologias consiste em estabelecer as associações pertinentes às terminologias para que cada termo pertencente a uma possa ser associado a algum termo da outra. Este mapeamento, geralmente, é criado por especialistas de domínio, que atuam analisando as duas terminologias em questão e estabelecendo manualmente estas associações. Neste trabalho, propomos uma metodologia que visa facilitar a realização deste tipo de mapeamento, por meio da utilização de dois recursos: Regras de Associação, para extração das associações preexistentes entre as terminologias em registros clínicos; e Busca Textual, para pareamento entre conceitos das duas terminologias baseado na identificação de termos comuns. O auxílio à criação destes mapeamentos é proporcionado por meio de sugestões de relações existentes entre as terminologias. Como resultado deste trabalho obtivemos uma metodologia genérica de mapeamento entre terminologias capaz de auxiliar com sucesso os especialistas. Em aproximadamente 40% dos casos os especialistas concordaram com uma das sugestões apresentadas. De forma complementar, obtivemos o mapeamento parcial entre duas terminologias: a ICD9-CM e a TUSS, utilizadas como caso de uso para validação da metodologia. / The high availability of health information through information systems can be provided only with the use of systems that are able to exchange data securely and consistently. To this end, these systems need to be interoperable, capable of exchanging information that is understood both at one end as the other. One of the most important characteristics of such systems is the use of terminologies in health, allowing the coding of clinical terms in a robust and consistent manner. Some of the most known and used terminologies are: SNOMED-CT, ICD-CM, ICD, LOINC, NANDA, TUSS, CBHPM, and SUS Procedures Table, among others. When systems do not use the same terminology for encoding the same concept, it is necessary to perform mappings and translations between the terminologies. The mapping between terminologies consists on establishing the relevant associations present in terminologies, so that each term belonging to one can be associated unambiguously to the terms belonging to another. This mapping is typically created by domain experts who work analyzing the two terms in question and manually setting these associations. In this paper, we propose a methodology that aims to facilitate this type of mapping, through the use of two frameworks: Association Rules, for the extraction of preexisting associations between the terminologies in clinical records; and Textual Search, for pairing between the two terminologies concepts based on the identification of common terms. The creation of these mappings by experts is aided by the method suggesting links between the terminologies through the Association Rules or Textual Search. As a result of this work we obtained a generic methodology for mapping between terminologies able to successfully assist the experts. In approximately 40% of cases the experts agreed with the suggestions. As a complement, we obtained a partial mapping between two specific terminologies for coding surgical procedures: the ICD9-CM and TUSS, used as use case to validate the methodology.

L'utilisation de termes équivalents ou sémantiquement proches est nécessaire pour augmenter la couverture et la sensibilité d'une application comme la recherche et l'extraction d'information ou l'annotation sémantique de documents. Dans le contexte de l'identification d'effets indésirables susceptibles d'être dûs à un médicament, la sensibilité est aussi recherchée afin de détecter plus exhaustivement les déclarations spontanées et de mieux surveiller le risque médicamenteux. C'est la raison qui motive notre travail. Dans notre travail de thèse, nous cherchons ainsi à détecter des termes sémantiquement proches et à les regrouper en utilisant plusieurs méthodes : des algorithmes de clustering non supervisés, des ressources terminologiques exploitées avec le raisonnement terminologique et des méthodes de Traitement Automatique de la Langue, comme la structuration de terminologies, où nous visons la détection de relations hiérarchiques et synonymiques. Nous avons réalisé de nombreuses expériences et évaluations des clusters générés, qui montrent que les méthodes proposées peuvent contribuer efficacement à la tâche visée. / The use of equivalent terms or semantically close is necessary to increase the coverageand sensitivity of applications such as information retrieval and extraction or semanticannotation of documents. In the context of the adverse drug reactions identification, sensitivityis also sought to detect more exhaustively spontaneous reports and better monitordrug risk. This is the reason that motivates our work. In our work, we thus seek to detectsemantically close terms and the together using several methods : unsupervised algorithms, terminological resources exploited with terminological reasoning and methodsof Natural Language Processing, such as terminology structuring, where we aim to detecthierarchical and synonymous relations. We conducted many experiments and evaluations of generated, which show that the proposed methods can efficiently contribute tothe task in question.

The purpose of this study was to identify the characteristics of cancer patients and the most frequently chosen nursing diagnoses, outcomes and interventions chosen for care plans from a large Midwestern acute care hospital. In addition the patients' outcome change scores and length of stay from the four oncology specialty units are investigated. Donabedian's structure-process-outcome model is the framework for this study. This is a descriptive retrospective study. The sample included a total of 2,237 patients admitted on four oncology units from June 1 to December 31, 2010. Data were retrieved from medical records, the nursing documentation system, and the tumor registry center. Demographics showed that 63% of the inpatients were female, 89% were white, 53 % were married and 26% were retired. Most patients returned home (82%); and 2% died in the hospital. Descriptive analysis identified that the most common nursing diagnoses for oncology inpatients were Acute Pain (78%), Risk for Infection (31%), and Nausea (26%). Each cancer patient had approximately 3.1 nursing diagnoses (SD=2.5), 6.3 nursing interventions (SD=5.1), and 3.7 nursing outcomes (SD=2.9). Characteristics of the patients were not found to be related to LOS (M=3.7) or outcome change scores for Pain Level among the patients with Acute Pain. Specifically, 88% of patients retained or improved outcome change scores. The most common linkage of NANDA-I, NOC, and NIC (NNN), a set of standardized nursing terminologies used in the study that represents nursing diagnoses, nursing-sensitive patient outcomes and nursing interventions, prospectively, was Acute Pain--Pain Level--Pain Management. Pain was the dominant concept in the nursing care provided to oncology patients. Risk for Infection was the most frequent nursing diagnosis in the Adult Leukemia and Bone Transplant Unit. Patients with both Acute Pain and Risk for Infection may differ among units; while the traditional study strategies rarely demonstrate this finding. Identifying the pattern of core diagnoses, interventions, and outcomes for oncology nurses can direct nursing care in clinical practice and provide direction for future research tot targets areas of high impact and guide education and evaluation of nurse competencies.

Sustainability has fast evolved in business practices, and yet its meaning is often elusive and ambiguous. The component of sustainability is playing an important role in the company’s brand positioning. The popularity and the importance of sustainability in business operations and business representation are still rapidly growing. The linkage of the branding and sustainability messaging has a strong correlation with the consumer’s perception. Moreover, the brand messaging construction with the right terminologies’ selection is one of the brand communication activities. However, the aspect of “sustainability manufacturing” caught great attention in recent years. Therefore, our study project is based on comprehending the authentic brand positioning with the right sustainability terminology usage aiming to deliver strong sustainability messaging. More precisely, the project's primary aim has been to investigate and explore the impact of sustainability terminology used by manufacturing companies in Sweden on consumer perception. For this aim, we conducted a mono-method quantitative study with a specially developed questionnaire based on gathered secondary data from the 21 annual reports of the manufacturing companies in Sweden from the year 2020. We have selected ten terminologies from the reports and conducted the questionnaire with 100 participants. Based on these results, regular consumers were surveyed to get a quantitative number of the importance, ambiguity, and credibility of the sustainability terminologies. The literature review attempted to link the fields of the study from a top-down design with manufacturing companies at the top, creating these sustainable goods. Manufacturing companies were using terms like ‘waste management ‘materials efficiency ‘employee health and safety’ and ‘community relations’ as a branding mechanism to display their commitment toward a long-term sustainability goal. These concepts are further discussed with universal definitions of corporate social responsibility while the final concept is based on theoretical knowledge of the consumers themselves and their perceptions as they are the end consumer of these ‘eco-friendly’ products. The sustainability literature foundation was smoothly interconnected with the branding and the consumer’s perception theories. To conclude, the results of our study with regards to the consumer importance, customer's privacy does have a significant influence on the sustainability aspect of businesses. In the case of the aspect of ambiguity, the sorted terminologies are as follows: product and service safety and waste management. In the case of the credibility aspect, water management, energy management, employee health and safety, and ecological impacts are bringing the most credibility from the consumers' perception. This study contributes to business sustainability while attempting to link manufacturing companies with the end consumers. It allows both parties to share a similar perception between terminologies. Overall, the research study helps to comprehend the right utilization of sustainability terminologies based on the consumers’ perception in order to increase the sustainability branding credibility and trustworthiness in the business operations.

In recent years, many Personal Health Record (PHR) systems have been developed to retrieve patients&rsquo / Electonic Health Records (EHRs) from external sources. However, current PHRs can provide access to only a small number of EHR systems, since there are many dierent interfaces, data formats and medical terminologies among dierent systems. When this is the case, all these diversity yields high integration costs. Development of such systems is dicult and expensive because of the reasons such as accessing to evidence based medical information, utilization of social networks to share information, incorporation of available medical knowledge models, etc. Due to the technical diversity of external information systems, a developer of a PHR system faces a dicult integration process when he wants to integrate a new source or service. Integration of medical devices is also important and necessary in a PHR system. However, most of the medical device vendors use proprietary formats and protocols for communicating their devices with external systems / again, causing high integration eorts and costs. In this thesis, these problems and challenges are addressed by providing an on-line personal iv healthcare management platform, i.e. eSaglikKaydim which is built on top of a highly modular architecture and provides services based on worldwide standards. In this way, eSaglikKaydim platform can be integrated with any external health information service and medical device so that it maximizes the data variety retrieved from all kinds of external health data sources. The work presented in this thesis is part of the OSAmI project supported by European ITEA and funded by the TU&uml / BI