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Poster CBIS 2012


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Poster presented at the XIII Brazilian Congress of Health Informatics -2012.
See: and for more information about semantic interoperability in healthcare.

#mlhim #semantic_interoperability #health_informatics

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Poster CBIS 2012

  1. 1. CONSTRAINT DEFINITION GENERATOR: AN OPEN SOURCE TOOL FOR MULTILEVEL MODELING OF HEALTHCARE INFORMATION SYSTEMS Eduardo César Pimenta Ribeiro1, Douglas Santos Möeller de Carvalho2, Jesiree Iglesias Quadros2, Lorena Silva de Moura2, Joyce Rocha de Matos Nogueira3, Timothy Wayne Cook3 and Luciana Tricai Cavalini4 Laboratório“Multilevel Healthcare Information Modeling” (MLHIM) Associado ao Instituto Nacional de Ciência e Tecnologia – Medicina Assistida por Computação CientíficaIntroduction. Semantic interoperability is crucial in recording information in purpose specific applications that need to synchronize to larger databases. Thereare no remaining obstacles related to hardware, including mobile computing and pervasive medicine, but software based on traditional data models are notfitted to deal with the significant spatial and temporal complexities of healthcare information 6. That is the case because health information systems based ontraditional data models are not interoperable and have high maintenance costs. The solution most fitted to the specific features of healthcare informationinvolves the separation between domain model and persistence of data. This multilevel modeling approach proposes the definition of at least two levels: theReference Model, which defines generic types of data and data structures and a Domain Model, defined by restrictions on the Reference Model 8. The goal ofthis paper is to present the technical aspects of the first knowledge modeling software developed for the MLHIM specifications.Methods. According to the MLHIM specifications, in order to develop any healthcare application, only the Reference Model is implemented in software. TheDomain Models are implemented in the XML Schema language and they represent constraints on the Reference Model, called Concept Constraint Definitions(CCD) as XML Schema files. The CCDs are used to add knowledge model information to applications built using the MLHIM Reference Model. The ConceptDefinition Generator (CDG) was devised to create the conceptual outline of any specific knowledge model defined by the domain expert. The CDG userinterface represents the CCD metadata and the MLHIM packages that are required for knowledge modeling: Content, Structures and Datatypes. Thehealthcare concept representation, when complete, is converted to a CCD, expressed as a XML Schema defining cardinality and constraints against theMLHIM Reference Model. The CCD can vary from the minimal to the maximal data definition for a specific concept, being context-sensitive in multicultural,multilingual and geopolitical aspects.Results and Discussion. The CDG code was developed by using the Python programming language. We adopted the wxPython graphical library, an opensource library based on wxWidgets which allows the application to run on any operation system, without requiring source code changes. The CDG sourcecode and an executable file are openly available from the Healthcare Knowledge Component Repository (HKCR) website on Thecurrent CDG user interface is shown on Figure 1. In order to automate the standardized data elements available on the National Institutes of Health’s CommonData Elements (NIH CDE) Browser, a CDG plug-in was developed. This script has the capability of automatically populate CCDs with the metadata, contextand data structures of data standards that can be modeled as Element root class CCDs with DvString data type, with no further additional DvString classconstraints. The script was also implemented in Python, and the urllib and lxml libraries were used. Currently, the following caBIG Data Element subsets havebeen converted into CCDs: Person Name, Person Age, Religion, Race, Ethnicity, Language, Organization, Address, Email Address, Organism Identification,Equipment, Genomic Identifers and Imaging Data Standards. Figure 1: The CDG user interfaceThe challenges related to the knowledge representation of healthcare concepts, towards the achievement of semantic interoperability of electronic healthrecords, are significantly complex. Because of this, the field of health informatics will probably continue to expand the multidisciplinary features of its experts,thus improving the intellectual debate on the field. Thus, information exchanges will be possible without failure to representing the specific domain concepts inhealth information systems17.Conclusion. The challenges related to the knowledge representation of healthcare concepts, towards the achievement of semantic interoperability ofelectronic health records, are significantly complex. As seen in this study, there are still many challenges to be faced in the field of knowledge representationof multilevel modeling of health information systems. It can be highlighted the need for the development of proper tools and the conversion of standardizedterminologies to knowledge modeling artifacts. Thus, information technology can be a powerful tool to support the practice of healthcare.Acknowledgements. This work is a product of the "Multilevel Healthcare Information Modeling" (LA-MLHIM) Laboratory, Associated to the National Institute ofScience and Technology – Medicine Assisted by Scientific Computing (INCT-MACC) – CNPq and FAPERJ funding. The author Eduardo Ribeiro receives anGraduated Technical Training scholarship from CNPq.[1] Hudson DL, Cohen ME. Uncertainty and complexity in personal health records. In: Conference Proceedings: 2010 Annual International Conference of the IEEE Engineering in Medicine andBiology Society, Piscataway 2010; IEEE, 6773-6.[2] Saleem JJ, Russ AL, Neddo A, Blades PT, Doebbeling BN, Foresman BH. Paper persistence, workarounds, and communication breakdowns in computerized consultation management. Int. J.Med. Inform. 2011; Elsevier Science Ireland Ltd., Shannon, 2011; 80(7): 466-79.[3] Ohmann C, Kuchinke W. Future developments of medical informatics from the viewpoint of networked clinical research. Interoperability and integration. Methods Inf. Med. 2009; 48(1): 45-54.[4] De Vlieger P, Boire JY, Breton V, Legre Y, Manset D, Revillard J, Sarramia D, Maigne L. Sentinel e-health network on grid: developments and challenges. Stud. Health Technol. Inform. 2010; 159:134-45.[5] Garde S, Hovenga E, Buck J, Knaup P. 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Ontologies, knowledge representation, artificial intelligence -hype or prerequisites for international pHealth Interoperability? Stud. Health Technol. Inform. 2011; 165: 11-20 .1.Graduação em Ciência da Computação, Universidade Federal de Minas Gerais (UFMG), Brasil; 2.Graduação em Medicina, Universidade Federal Fluminense (UFF), Brasil; 3.Instituto Nacional de Ciência e Visite-nos: www.mlhim.orgTecnologia – Medicina Assistida por Computação Científica (INCT-MACC), Brasil; 4.Departamento de Epidemiologia e Bioestatística, Universidade Federal Fluminense (UFF), Brasil -