Spreadsheet Model Generator (SMOG): A Lightweight Tool for Object-Spreadsheet Mapping.

In the life science domain, experts are usually familiar with spreadsheet software and often use it in their daily work to collect and structure required domain knowledge. The processing and analysis of spreadsheet data is an important task that must be supported by efficient software solutions. A typical application scenario is for example an integration of spreadsheet data (specified or derived) in an ontology to provide reasoning or search. Different converter tools were developed to support a spreadsheet-to-ontology transformation. Such tools allow often only a relatively simple structure of the spreadsheet template or they require complex mapping processes to map the spreadsheet and ontological entities. In many cases, it is impossible to use the existing converter tools because the spreadsheet data must be processed first before the derived data can be integrated into the ontology. In such cases, an efficient and fast development of customized software solutions is of great importance. In this paper, we present a general spreadsheet processing framework to efficiently read and write spreadsheet data. The Spreadsheet Model Generator (SMOG) provides a simple mechanism to automatically generate the Java object model and mappings between object code and spreadsheet entities. Our solution greatly simplifies the implementation of spreadsheet access methods and enables an efficient development of spreadsheet-based applications. The SMOG has already been used successfully in several biomedical projects under real world conditions.

Ontology-based specification and generation of search queries for post-market surveillance.

BACKGROUND: The vigilant observation of medical devices during post-market surveillance (PMS) for identifying safety-relevant incidents is a non-trivial task. A wide range of sources has to be monitored in order to integrate all accessible data about the safety and performance of a medical device. PMS needs to be supported by an efficient search strategy and the possibility to create complex search queries by domain experts. RESULTS: We use ontologies to support the specification of search queries and the preparation of the document corpus, which contains all relevant documents. In this paper, we present (1) the Search Ontology (SON) v2.0, (2) an Excel template for specifying search queries, and (3) the Search Ontology Generator (SONG), which generates complex queries out of the Excel template. Based on our approach, a service-oriented architecture was designed, which supports and assists domain experts during PMS. Comprehensive testing confirmed the correct execution of all SONG functions. The applicability of our method and of the developed tools was evaluated by domain experts. The test persons concordantly rated our solution after a short period of training as highly user-friendly, intuitive and well applicable for supporting PMS. CONCLUSIONS: The Search Ontology is a promising domain-independent approach to specify complex search queries. Our solution allows advanced searches for relevant documents in different domains using suitable domain ontologies.

Querying archetype-based EHRs by search ontology-based XPath engineering.

BACKGROUND: Legacy data and new structured data can be stored in a standardized format as XML-based EHRs on XML databases. Querying documents on these databases is crucial for answering research questions. Instead of using free text searches, that lead to false positive results, the precision can be increased by constraining the search to certain parts of documents. METHODS: A search ontology-based specification of queries on XML documents defines search concepts and relates them to parts in the XML document structure. Such query specification method is practically introduced and evaluated by applying concrete research questions formulated in natural language on a data collection for information retrieval purposes. The search is performed by search ontology-based XPath engineering that reuses ontologies and XML-related W3C standards. RESULTS: The key result is that the specification of research questions can be supported by the usage of search ontology-based XPath engineering. A deeper recognition of entities and a semantic understanding of the content is necessary for a further improvement of precision and recall. Key limitation is that the application of the introduced process requires skills in ontology and software development. In future, the time consuming ontology development could be overcome by implementing a new clinical role: the clinical ontologist. CONCLUSION: The introduced Search Ontology XML extension connects Search Terms to certain parts in XML documents and enables an ontology-based definition of queries. Search ontology-based XPath engineering can support research question answering by the specification of complex XPath expressions without deep syntax knowledge about XPaths.

Structuring Legacy Pathology Reports by openEHR Archetypes to Enable Semantic Querying.

BACKGROUND: Clinical information is often stored as free text, e.g. in discharge summaries or pathology reports. These documents are semi-structured using section headers, numbered lists, items and classification strings. However, it is still challenging to retrieve relevant documents since keyword searches applied on complete unstructured documents result in many false positive retrieval results. OBJECTIVES: We are concentrating on the processing of pathology reports as an example for unstructured clinical documents. The objective is to transform reports semi-automatically into an information structure that enables an improved access and retrieval of relevant data. The data is expected to be stored in a standardized, structured way to make it accessible for queries that are applied to specific sections of a document (section-sensitive queries) and for information reuse. METHODS: Our processing pipeline comprises information modelling, section boundary detection and section-sensitive queries. For enabling a focused search in unstructured data, documents are automatically structured and transformed into a patient information model specified through openEHR archetypes. The resulting XML-based pathology electronic health records (PEHRs) are queried by XQuery and visualized by XSLT in HTML. RESULTS: Pathology reports (PRs) can be reliably structured into sections by a keyword-based approach. The information modelling using openEHR allows saving time in the modelling process since many archetypes can be reused. The resulting standardized, structured PEHRs allow accessing relevant data by retrieving data matching user queries. CONCLUSIONS: Mapping unstructured reports into a standardized information model is a practical solution for a better access to data. Archetype-based XML enables section-sensitive retrieval and visualisation by well-established XML techniques. Focussing the retrieval to particular sections has the potential of saving retrieval time and improving the accuracy of the retrieval.

Domain Modeling and Application Development of an Archetype- and XML-based EHRS. Practical Experiences and Lessons Learnt.

BACKGROUND: Access to patient data within the hospital or between hospitals is still problematic since a variety of information systems is in use applying different vendor specific terminologies and underlying knowledge models. Beyond, the development of electronic health record systems (EHRSs) is time and resource consuming. Thus, there is a substantial need for a development strategy of standardized EHRSs. We are applying a reuse-oriented process model and demonstrate its feasibility and realization on a practical medical use case, which is an EHRS holding all relevant data arising in the context of treatment of tumors of the sella region. In this paper, we describe the development process and our practical experiences. METHODS: Requirements towards the development of the EHRS were collected by interviews with a neurosurgeon and patient data analysis. For modelling of patient data, we selected openEHR as standard and exploited the software tools provided by the openEHR foundation. The patient information model forms the core of the development process, which comprises the EHR generation and the implementation of an EHRS architecture. Moreover, a reuse-oriented process model from the business domain was adapted to the development of the EHRS. RESULTS: The reuse-oriented process model is a model for a suitable abstraction of both, modeling and development of an EHR centralized EHRS. The information modeling process resulted in 18 archetypes that were aggregated in a template and built the boilerplate of the model driven development. The EHRs and the EHRS were developed by openEHR and W3C standards, tightly supported by well-established XML techniques. The GUI of the final EHRS integrates and visualizes information from various examinations, medical reports, findings and laboratory test results. CONCLUSION: We conclude that the development of a standardized overarching EHR and an EHRS is feasible using openEHR and W3C standards, enabling a high degree of semantic interoperability. The standardized representation visualizes data and can in this way support the decision process of clinicians.

Template and Model Driven Development of Standardized Electronic Health Records.

Digital patient modeling targets the integration of distributed patient data into one overarching model. For this integration process, both a theoretical standard-based model and information structures combined with concrete instructions in form of a lightweight development process of single standardized Electronic Health Records (EHRs) are needed. In this paper, we introduce such a process along side a standard-based architecture. It allows the modeling and implementation of EHRs in a lightweight Electronic Health Record System (EHRS) core. The approach is demonstrated and tested by a prototype implementation. The results show that the suggested approach is useful and facilitates the development of standardized EHRSs.

Archetype based patient data modeling to support treatment of pituitary adenomas.

The treatment of patients with pituitary adenoma requires the assessment of various patient data by the clinician. Because of their heterogeneity, they are stored in different sub-information systems, limiting a fast and easy access. The objective of this paper is to apply and test the tools provided by the openEHR Foundation to model the patient data relevant for diagnosis and treatment of the disease with the future intention to implement a centralised standard-based information platform. This platform should support the clinician in the treatment of the disease and improve the information exchange with other healthcare institutions. Some results of the domain modeling, so far obtained, are presented, and the advantages of openEHR emphasized. The free tools and the large database of existing structured and standard archetypes facilitated the modeling task. The separation of the domain modeling from the application development will support the next step of development of the information platform.