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.

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.

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.

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.

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.

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.

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.