ABSTRACT
SNOMED CT has an enormous number of clinical concepts and mapping to SNOMED CT is considered as the foundation to achieve semantic interoperability in healthcare. Manual mapping is time-consuming and error-prone thus making this crucial step challenging. In addition, hierarchy retrieval of clinical concepts increases the challenges for the user. Terminology Servers provide an interface, which can be used to automate the process of retrieving data. In this work, it is shown that Snowstorm can significantly improve the efficiency of retrieval process if used with semi-automated workflows.
Subject(s)
Computers , Systematized Nomenclature of Medicine , Health FacilitiesABSTRACT
BACKGROUND: Although the drug is finished, identifiable, there is no universally accepted standard for naming them. The objective of this work is to evaluate qualitatively the HeTOP drug terminology server by two categories of students: (a) pharmacy students and (b) a control group. METHODS: A formal evaluation was built to measure the perception of users about the HeTOP drug server, using the three mains questions about "teaching interest", "skill interest" (or competence) and "ergonomics". RESULTS: The three pharmacy student subgroups gave the best and the worst score to the same categories. CONCLUSION: All three criteria are rated above 6.5 out of 10. The HeTOP drug terminology server is freely available to "non drug" specialists (URL: www.hetop.eu/hetop/drugs/).
Subject(s)
Students, Pharmacy , Humans , PharmacistsABSTRACT
Around 500,000 oncological diseases are diagnosed in Germany every year which are documented using the International Classification of Diseases for Oncology (ICD-O). Apart from this, another classification for oncology, OncoTree, is often used for the integration of new research findings in oncology. For this purpose, a semi-automatic mapping of ICD-O tuples to OncoTree codes was developed. The implementation uses a FHIR terminology server, pre-coordinated or post-coordinated SNOMED CT expressions, and subsumption testing. Various validations have been applied. The results were compared with reference data of scientific papers and manually evaluated by a senior pathologist, confirming the applicability of SNOMED CT in general and its post-coordinated expressions in particular as a viable intermediate mapping step. Resulting in an agreement of 84,00 % between the newly developed approach and the manual mapping, it becomes obvious that the present approach has the potential to be used in everyday medical practice.
Subject(s)
International Classification of Diseases , Systematized Nomenclature of Medicine , Germany , Medical OncologyABSTRACT
SNOMED CT has an enormous number of clinical concepts and mapping to SNOMED CT is considered as the foundation to achieve semantic interoperability in healthcare. Manual mapping is time-consuming and error-prone thus making this crucial step challenging. Terminology Servers provide an interface, which can be used to automate the process of retrieving data. Snowstorm is a terminology server developed by SNOMED International. In this work, the feasibility of using Snowstorm to automate the data retrieval and mapping has been discussed.
Subject(s)
Computers , Systematized Nomenclature of Medicine , Delivery of Health CareABSTRACT
BACKGROUND: The COVID-19 pandemic highlighted the importance of making research data from all German hospitals available to scientists to respond to current and future pandemics promptly. The heterogeneous data originating from proprietary systems at hospitals' sites must be harmonized and accessible. The German Corona Consensus Dataset (GECCO) specifies how data for COVID-19 patients will be standardized in Fast Healthcare Interoperability Resources (FHIR) profiles across German hospitals. However, given the complexity of the FHIR standard, the data harmonization is not sufficient to make the data accessible. A simplified visual representation is needed to reduce the technical burden, while allowing feasibility queries. OBJECTIVE: This study investigates how a search ontology can be automatically generated using FHIR profiles and a terminology server. Furthermore, it describes how this ontology can be used in a user interface (UI) and how a mapping and a terminology tree created together with the ontology can translate user input into FHIR queries. METHODS: We used the FHIR profiles from the GECCO data set combined with a terminology server to generate an ontology and the required mapping files for the translation. We analyzed the profiles and identified search criteria for the visual representation. In this process, we reduced the complex profiles to code value pairs for improved usability. We enriched our ontology with the necessary information to display it in a UI. We also developed an intermediate query language to transform the queries from the UI to federated FHIR requests. Separation of concerns resulted in discrepancies between the criteria used in the intermediate query format and the target query language. Therefore, a mapping was created to reintroduce all information relevant for creating the query in its target language. Further, we generated a tree representation of the ontology hierarchy, which allows resolving child concepts in the process. RESULTS: In the scope of this project, 82 (99%) of 83 elements defined in the GECCO profile were successfully implemented. We verified our solution based on an independently developed test patient. A discrepancy between the test data and the criteria was found in 6 cases due to different versions used to generate the test data and the UI profiles, the support for specific code systems, and the evaluation of postcoordinated Systematized Nomenclature of Medicine (SNOMED) codes. Our results highlight the need for governance mechanisms for version changes, concept mapping between values from different code systems encoding the same concept, and support for different unit dimensions. CONCLUSIONS: We developed an automatic process to generate ontology and mapping files for FHIR-formatted data. Our tests found that this process works for most of our chosen FHIR profile criteria. The process established here works directly with FHIR profiles and a terminology server, making it extendable to other FHIR profiles and demonstrating that automatic ontology generation on FHIR profiles is feasible.
ABSTRACT
The terminology services, defined as part of the emerging FHIR standard, yield a promising approach to finally achieve a common handling of coding systems needed for semantic interoperability. As a precondition, legacy terminology data must be transformed into FHIR-compatible resources whereby varying source formats make a manual case-by-case solution impracticable. In this work, the practicability of using CSIRO's Ontoserver and the related Snapper tool as support of the transformation process were evaluated by applying them to the German Alpha-ID terminology.
ABSTRACT
BACKGROUND: Most electronic medical records still contain large amounts of free-text data. Semantic evaluation of such data requires the data to be encoded with sufficient classifications or transformed into a knowledge-based database. METHODS: We present an approach that allows databases accessible via SQL (Structured Query Language) to be searched directly through semantic queries without the need for further transformations. Therefore, we developed I) an extension to SQL named Ontology-SQL (O-SQL) that allows to use semantic expressions, II) a framework that uses a standard terminology server to annotate free-text containing database tables and III) a parser that rewrites O-SQL to SQL, so that such queries can be passed to the database server. RESULTS: I) We compared several semantic queries published to date and were able to reproduce them in a reduced, highly condensed form. II) The quality of the annotation process was measured against manual annotation, and we found a sensitivity of 97.62% and a specificity of 100.00%. III) Different semantic queries were analyzed, and measured with F-scores between 0.91 and 0.98. CONCLUSIONS: We showed that systematic analysis of free-text-containing medical records is possible with standard tools. The seamless connection of ontologies and standard technologies from the database field represents an important constituent of unstructured data analysis. The developed technology can be readily applied to relationally organized data and supports the increasingly important field of translational research.
Subject(s)
Data Mining/methods , Programming Languages , Semantics , Electronic Health RecordsABSTRACT
Introducción: En el Hospital de Clínicas de Paraguay, el proceso actual de búsqueda de terminologías para la codificación médica en estándares de salud toma mucho tiempo ya que se realiza manualmente. Se propone, optimizar el proceso actual de búsqueda a través de la implementación de un servidor de terminología médica utilizando servicios web y una librería de motor de búsqueda de texto. Método: Se propone una arquitectura cliente - servidor de tres capas (también conocida como arquitectura multi-nivel), organizada de la siguiente manera: capa de presentación, de negocios y capa de datos. Se eligió utilizar este patrón por la independencia entre las capas y la clara definición de cada una de ellas en cuanto al objetivo que persigue. El servidor de terminología se encuentra representado en la capa de negocios. Está compuesta por un conjunto de servicios web de tipo REST y una librería de motor de búsqueda de texto, denominada Apache Lucene. Experimentos y Resultados: Fueron realizados dos experimentos acordes a los objetivos específicos mencionados anteriormente. El servidor de terminología implementado responde hasta 19 veces más rápido que el proceso actual de búsqueda y resultó ser bastante competitivo contra Metamorphosys. Si bien ambas herramientas presentan un tiempo de respuesta promedio similar, el servidor de terminología es hasta 5 veces más rápido que Metamorphosys en sus valores atípicos. Conclusiones: El servidor de terminología implementado reduce el tiempo de búsqueda del proceso actual siendo más rápido que el proceso actual de búsqueda. Finalmente, ante la comparación del servidor implementado contra el buscador Metamorphosys, el servidor implementado se muestra competitivo contra dicho buscador ya que tienen tiempos de respuesta similares(AU)
Introduction: In the Hospital Clínicas of Paraguay, the current process of searching for terminologies for medical coding in health standards takes a long time since it is done manually. It is proposed to optimize the current search process through the implementation of a medical terminology server using web services and a text search engine library. Method: Three layer client-server architecture is proposed (also known as multilevel architecture), organized as follows: presentation layer, business layer and data layer. The use of this pattern was due to its contribution to the independence between the layers and the clear definition of them in terms of the objective pursued. The terminology server is represented in the business layer. It is composed of a set of REST web services and a text search engine library, called Apache Lucene. Experiments and Results: Two experiments were carried out according to the objective mentioned above. The implemented terminology server responds up to 19 times faster than the current search process and proved to be quite competitive against Metamorphosys. While both tools have a similar average response time, the terminology server is up to 5 times faster than Metamorphosys in their outliers. Conclusions: The terminology server implemented reduces the search time of the current process being faster than the current search process. Finally, before the comparison of the server implemented against the Metamorphosys search engine, the implemented server is competitive since they have similar response times(AU).
Subject(s)
Humans , Male , Female , Computer Systems/standards , Medical Informatics/methods , User-Computer Interface , ParaguayABSTRACT
Introducción: En el Hospital de Clínicas de Paraguay, el proceso actual de búsqueda de terminologías para la codificación médica en estándares de salud toma mucho tiempo ya que se realiza manualmente. Se propone, optimizar el proceso actual de búsqueda a través de la implementación de un servidor de terminología médica utilizando servicios web y una librería de motor de búsqueda de texto. Método: Se propone una arquitectura cliente - servidor de tres capas (también conocida como arquitectura multi-nivel), organizada de la siguiente manera: capa de presentación, de negocios y capa de datos. Se eligió utilizar este patrón por la independencia entre las capas y la clara definición de cada una de ellas en cuanto al objetivo que persigue. El servidor de terminología se encuentra representado en la capa de negocios. Está compuesta por un conjunto de servicios web de tipo REST y una librería de motor de búsqueda de texto, denominada Apache Lucene. Experimentos y Resultados: Fueron realizados dos experimentos acordes a los objetivos específicos mencionados anteriormente. El servidor de terminología implementado responde hasta 19 veces más rápido que el proceso actual de búsqueda y resultó ser bastante competitivo contra Metamorphosys. Si bien ambas herramientas presentan un tiempo de respuesta promedio similar, el servidor de terminología es hasta 5 veces más rápido que Metamorphosys en sus valores atípicos. Conclusiones: El servidor de terminología implementado reduce el tiempo de búsqueda del proceso actual siendo más rápido que el proceso actual de búsqueda. Finalmente, ante la comparación del servidor implementado contra el buscador Metamorphosys, el servidor implementado se muestra competitivo contra dicho buscador ya que tienen tiempos de respuesta similares(AU)
Introduction: In the Hospital Clínicas of Paraguay, the current process of searching for terminologies for medical coding in health standards takes a long time since it is done manually. It is proposed to optimize the current search process through the implementation of a medical terminology server using web services and a text search engine library. Method: Three layer client-server architecture is proposed (also known as multilevel architecture), organized as follows: presentation layer, business layer and data layer. The use of this pattern was due to its contribution to the independence between the layers and the clear definition of them in terms of the objective pursued. The terminology server is represented in the business layer. It is composed of a set of REST web services and a text search engine library, called Apache Lucene. Experiments and Results: Two experiments were carried out according to the objective mentioned above. The implemented terminology server responds up to 19 times faster than the current search process and proved to be quite competitive against Metamorphosys. While both tools have a similar average response time, the terminology server is up to 5 times faster than Metamorphosys in their outliers. Conclusions: The terminology server implemented reduces the search time of the current process being faster than the current search process. Finally, before the comparison of the server implemented against the Metamorphosys search engine, the implemented server is competitive since they have similar response times(AU)
Subject(s)
Humans , Computer Systems/standards , Medical Informatics/methods , User-Computer Interface , ParaguayABSTRACT
BACKGROUND: Unstructured health documents (e.g. discharge summaries) represent an important and unavoidable source of information. METHODS: A semantic annotator identified all the concepts present in the health documents from the clinical data warehouse of the Rouen University Hospital. RESULTS: 2,087,784,055 annotations were generated from a corpus of about 11.9 million documents with an average of 175 annotations per document. SNOMED CT, NCIt and MeSH were the top 3 terminologies that reported the most annotation. DISCUSSION: As expected, the most general terminologies with the most translated concepts were those with the most concepts identified.
