Heiner Oberkampf

Towards a technology roadmap for big data applications in the healthcare domain

Big Data technologies can be used to improve the quality and efficiency of healthcare delivery. The highest impact of Big Data applications is expected when data from various healthcare areas, such as clinical, administrative, financial, or outcome data, can be integrated. However, as of today, the seamless access to the various healthcare data pools is only possible in a very constrained and limited manner. For enabling the seamless access several technical requirements, such as data digitalization, semantic annotation, data sharing, data privacy and security as well as data quality need to be addressed. In this paper, we introduce a detailed analysis of these technical requirements and show how the results of our analysis lead towards a technical roadmap for Big Data in the healthcare domain.

UIMA2LOD: Integrating UIMA Text Annotations into the Linked Open Data Cloud

The LOD cloud is becoming the de-facto standard for sharing and connecting pieces of data, information and knowledge on the Web. As of today, means for the seamless integration of structured data into the LOD cloud are available. However, algorithms for integrating information enclosed in unstructured text sources are missing. In order to foster the (re)use of the high percentage of unstructured text, automatic means for the integration of their content are needed. We address this issue by proposing an approach for conceptual representation of textual annotations which distinguishes linguistic from semantic annotations and their integration. Additionally, we implement a generic UIMA pipeline that automatically creates a LOD graph from texts that (1) implements the proposed conceptual representation, (2) extracts semantically classified entities, (3) links to existing LOD datasets and (4) generates RDF graphs from the extracted information. We show the application and benefits of the approach in a case study on a medical corpus.

Intelligent Healthcare Applications

Currently three client systems are provided by Medico: the stationary clinical radiological workplace which consists of a semantic annotation prototype to be used as a semantic reporting tool for the radiologists in-daily routine; the semantic search prototype; and a mobile system. The semantic annotation tool is designed to enable the physician to validate and complete the automatically generated semantic annotations, whereas the semantic search prototype is to be used to ensure a diagnosis by searching for similar cases in medical databases. The mobile application (Usage of mobile applications for medical purposes might be restricted in several countries.) runs on the iPad and connects to the Semantic Server of Medico, which enables the radiologist to quickly view and manipulate the semantic annotations anywhere, e.g., at the bedside, using touch screen gestures and speech.

From Symptoms to Diseases --- Creating the Missing Link

A wealth of biomedical datasets is meanwhile published as Linked Open Data. Each of these datasets has a particular focus, such as providing information on diseases or symptoms of a certain kind. Hence, a comprehensive view can only be provided by integrating information from various datasets. Although, links between diseases and symptoms can be found, these links are far too sparse to enable practical applications such as a disease-centric access to clinical reports that are annotated with symptom information. For this purpose, we build a model of disease-symptom relations. Utilizing existing ontology mappings, we propagate semantic type information for disease and symptom across ontologies. Then entities of the same semantic type from different ontologies are clustered and object properties between entities are mapped to cluster-level relations. The effectiveness of our approach is demonstrated by integrating all available disease-symptom relations from different biomedical ontologies resulting in a significantly increased linkage between datasets.

Corpus-based Translation of Ontologies for Improved Multilingual Semantic Annotation

Ontologies have proven to be useful to enhance NLP-based applications such as information extraction. In the biomedical domain rich ontologies are available and used for semantic annotation of texts. However, most of them have either no or only few non-English concept labels and cannot be used to annotate non-English texts. Since translations need expert review, a full translation of large ontologies is often not feasible. For semantic annotation purpose, we propose to use the corpus to be annotated to identify high occurrence terms and their translations to extend respective ontology concepts. Using our approach, the translation of a subset of ontology concepts is sufficient to significantly enhance annotation coverage. For evaluation, we automatically translated RadLex ontology concepts from English into German. We show that by translating a rather small set of concepts (in our case 433), which were identified by corpus analysis, we are able to enhance the amount of annotated words from 27.36 % to 42.65 %.

Change and Version Management in Variability Models for Modular Ontologies

Modular ontology management tries to overcome the disadvantages of large ontologies regarding reuse and performance. A possibility for the formalization of the various combinations are variability models, which originate from the software product line domain. Similar to that domain, knowledge models can then be individualized for a specific application through selection and exclusion of modules. However, the ontology repository as well as the requirements of the domain are not stable over time. A process is needed, that enables knowledge engineers and domain experts to adapt the principles of version and change management to the domain of modular ontology management. In this paper, we define the existing change scenarios and provide support for keeping the repository, the variability model and also the configurations consistent using Semantic Web technologies. The approach is presented with a use case from the enterprise architecture domain as running example.

Knowledge-Based Extraction of Measurement-Entity Relations from German Radiology Reports

A large percentage of relevant radio logic patient information is currently only available in unstructured formats such as free text reports. In particular measurements are important since they are comparable and thus provide insight into the change of the health status over time, for example in response to some treatment. In radiology most of the measurements in reports describe the size of anatomical entities. Even though it is possible to extract measurements and anatomical entities from text using standard information extraction techniques, it is difficult to extract the relation between the measurement and the corresponding anatomical entity. Here we present a knowledge-based approach to extract this relation for size measurements using a model about typical size descriptions of anatomical entities in combination with hierarchical knowledge of existing medical ontologies. We evaluate our approach on two data sets of German radiology reports reaching an F1-measure of 0.85 and 0.79 respectively.

Management of Variability in Modular Ontology Development

The field of variability management deals with the formalization of mandatory, alternative and optional domain concepts in product line engineering. Ontologies in turn, describe domain knowledge in form of predicates, subjects and constraints in various forms. Based on existing ontology mapping approaches, we developed a method to organize a set of modular ontologies using the concepts of variability management (MOVO). This ontology driven variability model can be stepwise adapted to the needs of a business driven one, resulting in a variability model that fits the needs of business and makes modular ontologies reusable in a simple manner. In order to avoid a technological break and to benefit from the opportunities that ontologies offer, the resulting variability model is expressed in an ontology itself. The approach is evaluated by one case study with enterprise architecture ontologies.