Detta dokument beskriver ett kandidatexamensarbete som genomförts av åtta civilingenjörsstudenter från Linköpings universitet. Examensarbetet bestod av att utveckla en webbapplikation för att möjliggöra navigation och visualisering av element ur den medicinska databasen SNOMED CT samt deras relationer. Den första delen av dokumentet består av gruppens gemensamma arbete och erfarenheter för att uppfylla kundens givna krav samt själva processen om hur det är att arbeta i ett större mjukvaruprojekt. Den andra delen består av studenternas individuella bidrag till examensarbetet som berör något spridda ämnen men som har en anknytning till mjukvaruutveckling och projektarbete.

Objective: Structured recording of examination findings, such as heart murmurs, is important for effective retrieval and analysis of data. Our study proposes two models for post-coordinating murmur findings and evaluates their ability to record murmurs found in clinical records. Methods: Two models were proposed for post-coordinating murmur findings: the Concept-dependent Attributes model and the Interprets/Has interpretation model. A micro-nomenclature was created based on each model by using the subset and extension mechanisms provided for by the SNOMED-CT® framework. Within each micro-nomenclature a partonomy of cardiac cycle timing values was generated. In order for each model to be capable of representing clinical data, a mechanism for handling range values was developed. One hundred murmurs taken from clinical records were entered into two systems that were built based on each model to enter and display murmur data. Results: Both models were able to record all 100 murmur findings; both required the addition of the same number of concepts into their respective micro-nomenclatures. However, the Interprets/Has interpretation model required twice the storage space for recording murmurs. Conclusion: We found little difference in the requirements for implementation of either model. In fact, data stored using these models could be easily inter-converted. This will allow system developers to choose a model based on their own preferences. If at a later date a method is chosen for modeling within SNOMED-CT, the data can be converted to conform if necessary. / Master of Science

Snomed CT is a widely used medical ontology which is formally expressed in a fragment of the Description Logic EL++. The underlying logics allow for expressive querying, yet make it costly to maintain and extend the ontology. Existing approaches for ontology generation mostly focus on learning superclass or subclass relations and therefore fail to be used to generate Snomed CT definitions. In this paper, we present an approach for the extraction of Snomed CT definitions from natural language texts, based on the distance relation extraction approach. By benefiting from a relatively large amount of textual data for the medical domain and the rich content of Snomed CT, such an approach comes with the benefit that no manually labelled corpus is required. We also show that the type information for Snomed CT concept is an important feature to be examined for such a system. We test and evaluate the approach using two types of texts. Experimental results show that the proposed approach is promising to assist Snomed CT development.

With a longer-living population and an increase in cancer incidence the health care’s workload has increased over the past decade. The treatment process of a cancer patient is dependant on clinical information collected and communicated from the pathology department. With a standardised and structured pathology report the information communicated can become easier to interpret and will fa- cilitate the search for important parameters. This master thesis aims to develop a template prototype to replace four static free-text templates used in the area of breast cancer pathology at the pathology department at Region Östergötland. The end product was intends to store docu- mented information in a structured manner through structured data, in order to obtain semantic interoperability. Semantic interoperability means that different systems are able to communicate with each other in such a way that the information is handled and interpreted equally by the systems. By using certain standards such as openEHR archetypes and SNOMED CT concepts, the data becomes uniform and unambiguous. When that is achieved, information can be sent more easily between systems such as patient health data if an individual moves between different cities where the hos- pitals have different medical records systems. The result of the master thesis is a single template that incorporates all the parts from the four static templates currently used at Region Östergötland. To avoid a large and cumbersome template for the end-user the template is built with con- ditions that changed the appearance of the template while it is being filled in, making it dynamic.

The overall research question of this PhD research was: “How can the clinical value of the Systematised Nomenclature of Medicine Clinical Terms (SNOMED CT) be demonstrated in the primary health care setting to enhance patient care?” The position taken in this research is that there is clinical value in using SNOMED CT. To inform the current state of knowledge, a literature review of SNOMED CT papers catalogued by PubMed and Embase between 2001 and 2012 was carried out, and interviews were conducted with 14 individuals from 13 health care organisations across eight countries. The results showed there was a lack of understanding of how to craft post-coordinated expressions, how to fully utilise the semantics of SNOMED CT in data retrieval, and a lack of evidence on how SNOMED CT added value. A proposed SNOMED CT Clinical Value Framework that organised the primary and secondary uses of SNOMED CT was created and a SNOMED CT design methodology was formalised that consisted of three components to aid in auditing, encoding and retrieval through a primary health care study. In this PhD research, the potential clinical value of SNOMED CT was demonstrated by improving the completeness of clinical records and facilitating decision support features such as alerting clinicians to potential drug-allergy interactions, and reminding clinicians to order routine tests. The realisation of the potential clinical value was based upon the accurate and unambiguous manner in which clinical terms were encoded using the encoding method, the efficient and effective retrieval of relevant concepts using the retrieval method, and to a lesser extent, the ensuring that the concepts used were consistent using the auditing method. / Graduate / 0566 / dlhk@uvic.ca

No description available.

CDC's requirements for Salmonella surveillance reporting include submission of serovars from the recognized naming scheme, Kauffmann-White (K-W), using identifiers curated by the Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT®). Translating the serotype formula of a Salmonella isolate to the correct identifier has been a multistep manual process for users. Our goal was to determine whether a degree of automation could be achieved using an ontology based on K-W. We investigated information artifacts presently available, namely K-W, SNOMED CT and CDC's Public Health Information Network - Vocabulary Access and Distribution System (PHIN-VADS). As SNOMED CT creates identifiers and associates them with serovar names, we performed detailed analysis on its coverage of K-W. An overall error rate of 13.1% included simple omissions and transcription errors. We limited our assessment of K-W and PHIN-VADS to the functional characteristics of the resources they distribute. K-W creates serovar names but does not provide identifiers. PHIN-VADS includes the identifiers but not antigenic formulae for most isolates. In summary, neither K-W nor PHIN-VADS contained all information users require. Two different ontology prototypes were developed. Prototype I placed K-W serovars as terminal nodes in the hierarchy and these were given logic-based definitions. Prototype II added isolate classes as serovar subtypes. Only the isolate classes had complete logical definitions. Both prototypes were logically sound and functioned as expected. Prototype I paralleled existing SNOMED CT content but required more robust description logic than currently employed in SNOMED CT. Prototype II was more compatible with current functionality of SNOMED CT but created identifiers that would not meet current requirements for public health reporting. Prototype I was fully populated as the Salmonella Serotype Designation Ontology (SSDO). As it stands, SSDO reliably places isolates in the appropriate classes, with few and predictable exceptions. Although SNOMED CT cannot accommodate its functionality at this time, SSDO can serve as the basis for a stand-alone application. Ultimately whether by improving functionality of existing systems or providing a framework for an ancillary automated system, this work should facilitate real-time reporting and analysis of surveillance data that will prevent new or reduce severity of infectious disease outbreaks. / Ph. D.

Animal classification needs vary by use and application. The Linnaean taxonomy is an important animal classification scheme but does not portray key animal identifying information like sex, age group, physiologic stage, living environment and role in production systems such as farms. Ontologies are created and used for defining, organizing and classifying information in a domain to enable learning and sharing of information. This work develops an ontology of animal classes that form the basis for communication of animal identifying information among animal managers, medical professionals caring for animals and biomedical researchers involved in disciplines as diverse as wildlife ecology and dairy science. The Animals in Context Ontology (ACO) was created from an extension and subset of the Systematized Nomenclature of Medicine — Clinical Terms (SNOMED-CT). The principles of the Open Biological and Biomedical Ontologies (OBO) Foundry were followed and freely available tools were used. ACO includes normal development and physiologic animal classes as well animal classes where humans have assigned the animal's role. ACO is interoperable with and includes classes from other OBO Foundry ontologies such as the Gene Ontology (GO). Meeting many of the OBO Foundry principles was straightforward but difficulties were encountered with missing and problematic content in some of the OBO ontologies. Additions and corrections were submitted to four ontologies. Some information in ACO could not be represented formally because of inconsistency in husbandry practices. ACO classes are of interest to science, medicine and agriculture, and can connect information between animal and human systems to enable knowledge discovery. / Master of Science

The description logic EL⁺ has recently proved practically useful in the life science domain with presence of several large-scale biomedical ontologies such as Snomed ct. To deal with ontologies of this scale, standard reasoning of classification is essential but not sufficient. The ability to extract relevant fragments from a large ontology and to incrementally classify it has become more crucial to support ontology design, maintenance and reuse. In this paper, we propose a pragmatic approach to module extraction and incremental classification for EL⁺ ontologies and report on empirical evaluations of our algorithms which have been implemented as an extension of the CEL reasoner.

Our ultimate goal as obstetric and neonatal care providers is to improve care for mothers and their babies. Continuous quality improvement (CQI) involves iterative cycles of practice change and audit of ongoing clinical care identifying practices that are associated with good outcomes. A vital prerequisite to this evidence based medicine is data collection. In Canada, much of the country is covered by separate fragmented silos known as regional reproductive care databases or perinatal health programs. A more centralized system which includes collaborative efforts is required. Moving in this direction would serve many purposes: efficiency, economy in the setting of limited resources and shrinking budgets and lastly, interaction among data collection agencies. This interaction may facilitate translation and transfer of knowledge to care-givers and patients. There are however many barriers towards such collaborative efforts including privacy, ownership and the standardization of both digital technologies and semantics. After thoroughly examining the current existing perinatal data collection among Perinatal Health Programs (PHPs), and the Canadian Perinatal Network (CPN) database, it was evident that there is little standardization of definitions. This serves as one of the most important barriers towards data sharing. To communicate effectively and share data, researchers and clinicians alike must construct a common perinatal language. Communicative tools and programs such as SNOMED CT® offer a potential solution, but still require much work due to their infancy. A standardized perinatal language would not only lay the definitional foundation in women’s health and obstetrics but also serve as a major contribution towards a universal electronic health record.