A Toolchain for Big Data Analyses in the Intelligent Cognitive Operating Room.

This project aims to evaluate existing big data infrastructures for their applicability in the operating room to support medical staff with context-sensitive systems. Requirements for the system design were generated. The project compares different data mining technologies, interfaces, and software system infrastructures with a focus on their usefulness in the peri-operative setting. The lambda architecture was chosen for the proposed system design, which will provide data for both postoperative analysis and real-time support during surgery.

Service-oriented Device Connectivity interface for a situation recognition system in the OR.

PURPOSE: Context awareness in the operating room (OR) is important to realize targeted assistance to support actors during surgery. A situation recognition system (SRS) is used to interpret intraoperative events and derive an intraoperative situation from these. To achieve a modular system architecture, it is desirable to de-couple the SRS from other system components. This leads to the need of an interface between such an SRS and context-aware systems (CAS). This work aims to provide an open standardized interface to enable loose coupling of the SRS with varying CAS to allow vendor-independent device orchestrations. METHODS: A requirements analysis investigated limiting factors that currently prevent the integration of CAS in today's ORs. These elicited requirements enabled the selection of a suitable base architecture. We examined how to specify this architecture with the constraints of an interoperability standard. The resulting middleware was integrated into a prototypic SRS and our system for intraoperative support, the OR-Pad, as exemplary CAS for evaluating whether our solution can enable context-aware assistance during simulated orthopedical interventions. RESULTS: The emerging Service-oriented Device Connectivity (SDC) standard series was selected to specify and implement a middleware for providing the interpreted contextual information while the SRS and CAS are loosely coupled. The results were verified within a proof of concept study using the OR-Pad demonstration scenario. The fulfillment of the CAS' requirements to act context-aware, conformity to the SDC standard series, and the effort for integrating the middleware in individual systems were evaluated. The semantically unambiguous encoding of contextual information depends on the further standardization process of the SDC nomenclature. The discussion of the validity of these results proved the applicability and transferability of the middleware. CONCLUSION: The specified and implemented SDC-based middleware shows the feasibility of loose coupling an SRS with unknown CAS to realize context-aware assistance in the OR.

Technical Environment for Developing the SNIK Ontology of Information Management in Hospitals.

The SNIK project converts textbooks about information management in hospitals to a domain ontology that provides a shared vocabulary for institutions to model and integrate processes, data and infrastructure. To accommodate user groups with different requirements and technical backgrounds, and to support incremental and cooperative development, we create a system architecture to publish, visualize, browse and query the ontology, as well as to evaluate and improve the data quality.

Visualization of Large Ontologies in University Education from a Tool Point of View.

The realization of ontology visualization requirements in university education is a challenging task and should be supported by appropriate tools. This applies in particular, if the ontology to be visualized is based on a large text corpus that comprises a huge number of concepts, relations and annotations. In SNIK, we developed such an ontology of information management in hospitals in order to support the transfer of knowledge in the context of the university education. The challenge is to identify tools and methods, which are capable to support ontology visualization and usage as efficiently as possible. Related research fields (e.g. bioinformatics) are confronted with similar visualization problems. These tools and methods used could provide a suitable solution in our research field. In total, we assessed eight tools concerning the visualization of large ontologies to evaluate their suitability representing knowledge in the field of medical informatics.

An Ontology-Based Scenario for Teaching the Management of Health Information Systems.

The terminology for the management of health information systems is characterized by complexity and polysemy which is both challenging for medical informatics students and practitioners. SNIK, an ontology of information management (IMI) in hospitals, brings together IM concepts from different literature sources. Based on SNIK, we developed a blended learning scenario to teach medical informatics students IM concepts and their relationships. In proof-of-concept teaching units, students found the use of SNIK in teaching and learning motivating and useful. In the next step, the blended learning scenario will be rolled out to an international course for medical informatics students.

An Approach to Support Collaborative Ontology Construction.

The increasing number of terms used in textbooks for information management (IM) in hospitals makes it difficult for medical informatics students to grasp IM concepts and their interrelations. Formal ontologies which comprehend and represent the essential content of textbooks can facilitate the learning process in IM education. The manual construction of such ontologies is time-consuming and thus very expensive [3]. Moreover, most domain experts lack skills in using a formal language like OWL [2] and usually have no experience with standard editing tools like Protégé http://protege.stanford.edu [4,5]. This paper presents an ontology modeling approach based on Excel2OWL, a self-developed tool which efficiently supports domain experts in collaboratively constructing ontologies from textbooks. This approach was applied to classic IM textbooks, resulting in an ontology called SNIK. Our method facilitates the collaboration between domain experts and ontologists in the development process. Furthermore, the proposed approach enables ontologists to detect modeling errors and also to evaluate and improve the quality of the resulting ontology rapidly. This approach allows us to visualize the modeled textbooks and to analyze their semantics automatically. Hence, it can be used for e-learning purposes, particularly in the field of IM in hospitals.