Semantic validation of standard based electronic health record documents with W3C XML Schema

++43 1 40400 6693 (Phone) [email protected] 2 Summary Objectives. The goal of this article is to examine whether W3C XML Schema provides a practicable solution for the semantic validation of standard based electronic health record (EHR) documents. With semantic validation we mean that the EHR documents are checked for conformance with the underlying archetypes and reference model. Methods. We describe an approach that allows XML Schemas to be derived from archetypes based on a specific naming convention. The archetype constraints are augmented with additional components of the reference model within the XML Schema representation. A copy of the EHR document that is transformed according to the before-mentioned naming convention is used for the actual validation against the XML Schema. Results. We tested our approach by semantically validating EHR documents conformant to three different ISO/EN 13606 archetypes respective to three sections of the CDA implementation guide "Continuity of Care Document (CCD)" and an implementation guide for diabetes therapy data. We further developed a tool to automate the different steps of our semantic validation approach. Conclusions. For two particular kinds of archetype prescriptions, individual transformations are required for the corresponding EHR documents. Otherwise, a fully generic validation is possible. In general, we consider W3C XML Schema as a practicable solution for the semantic validation of standard based EHR documents

WS-ARC service configuration manual

The central component of AR

Semi-automatic semantic enrichment of raw sensor data

One of the more recent sources of large volumes of generated data is sensor devices, where dedicated sensing equipment is used to monitor events and happenings in a wide range of domains, including monitoring human biometrics. In recent trials to examine the effects that key moments in movies have on the human body, we fitted fitted with a number of biometric sensor devices and monitored them as they watched a range of dierent movies in groups. The purpose of these experiments was to examine the correlation between humans' highlights in movies as observed from biometric sensors, and highlights in the same movies as identified by our automatic movie analysis techniques. However,the problem with this type of experiment is that both the analysis of the video stream and the sensor data readings are not directly usable in their raw form because of the sheer volume of low-level data values generated both from the sensors and from the movie analysis. This work describes the semi-automated enrichment of both video analysis and sensor data and the mechanism used to query the data in both centralised environments, and in a peer-to-peer architecture when the number of sensor devices grows to large numbers. We present and validate a scalable means of semi-automating the semantic enrichment of sensor data, thereby providing a means of large-scale sensor management

Open-standards rich media mobile platform & rapid deployment service creation tool

This paper builds upon the work carried out by Brunel University in the field of "Fast Prototyping And Semi-automated User Interface And Application Generation for Converged Broadcast and Cellular Terminals" [1]. This work involved the development of a service creation application for interactive services on mobile devices and methodologies and tools to speed up and deskill the deployment process. This paper aims at further enhancing these tools and presents an enhanced open standards reference platform for mobile digital TV and rich media services. By using a Scalar Vector Graphics (SVG)-driven Java MIDP application (as opposed to bitmapped raster graphics-driven MHP), rich media services can be broadcast to mobile devices running various Java-supported platforms with a user interface scalable to any screen size. Moreover, the Rich Media Mobile Browser is integrated into a service creation tool, therefore enabling rapid testing and deployment of rich mobile media services. The following sections detail the motivation behind the need for a platform which allows for rich media play-out on mobile devices, along with the rich media mobile viewing application and the tools used to create and test rich media with speed and ease

Preparing Laboratory and Real-World EEG Data for Large-Scale Analysis: A Containerized Approach.

Large-scale analysis of EEG and other physiological measures promises new insights into brain processes and more accurate and robust brain-computer interface models. However, the absence of standardized vocabularies for annotating events in a machine understandable manner, the welter of collection-specific data organizations, the difficulty in moving data across processing platforms, and the unavailability of agreed-upon standards for preprocessing have prevented large-scale analyses of EEG. Here we describe a "containerized" approach and freely available tools we have developed to facilitate the process of annotating, packaging, and preprocessing EEG data collections to enable data sharing, archiving, large-scale machine learning/data mining and (meta-)analysis. The EEG Study Schema (ESS) comprises three data "Levels," each with its own XML-document schema and file/folder convention, plus a standardized (PREP) pipeline to move raw (Data Level 1) data to a basic preprocessed state (Data Level 2) suitable for application of a large class of EEG analysis methods. Researchers can ship a study as a single unit and operate on its data using a standardized interface. ESS does not require a central database and provides all the metadata data necessary to execute a wide variety of EEG processing pipelines. The primary focus of ESS is automated in-depth analysis and meta-analysis EEG studies. However, ESS can also encapsulate meta-information for the other modalities such as eye tracking, that are increasingly used in both laboratory and real-world neuroimaging. ESS schema and tools are freely available at www.eegstudy.org and a central catalog of over 850 GB of existing data in ESS format is available at studycatalog.org. These tools and resources are part of a larger effort to enable data sharing at sufficient scale for researchers to engage in truly large-scale EEG analysis and data mining (BigEEG.org)