An increasing and overwhelming amount of biomedical information is available in the research literature mainly in the form of free-text. Biologists need tools that automate their information search and deal with the high volume and ambiguity of free-text. Ontologies can help automatic information processing by providing standard concepts and information about the relationships between concepts. The Medical Subject Headings (MeSH) ontology is already available and used by MEDLINE indexers to annotate the conceptual content of biomedical articles. This paper presents a domain-independent method that uses the MeSH ontology inter-concept relationships to extend the existing MeSHbased representation of MEDLINE documents. The extension method is evaluated within a document triage task organized by the Genomics track of the 2005 Text REtrieval Conference (TREC). Our method for extending the representation of documents leads to an improvement of 18.3% over a non-extended baseline in terms of normalized utility, the metric defined for the task.
BackgroundNew sensing technologies and the decreasing cost of Information and Communication Technologies (ICTs) make possible the development of electronic Health (eHealth) monitoring systems. The challenges of such systems include the representation of data extracted from various sensor devices by knowledge workers through semantic enrichment and integration. Also, the data must be stored in a format suitable for querying and further analysis. This paper describes the demonstration of the HealthSense system which captures and queries personal health data extracted from wearable sensors. Figure 1 illustrates the transformation process. There are 4 layers, representing data in different formats, separated by the 3 processors that transform them. A detailed description of the 3 processors was presented in [1]. The HealthSense DemonstrationThe demonstration includes:-the wearing of sensor devices and recording of data, -the extraction of sensor data to a laptop, -the use of HealthSense to enrich, integrate, and store sensor data, and -the querying of the stored data from an XML database using XPath. The Sensor Devices -Polar S625XTM heart-rate monitor: this consists of a fabric band which fits around a person's chest and detects and logs their heart rate. -BodyMedia SenseWear R : this sensor array is worn around the upper arm and measures. It uses motion sensors and galvanic skin response sensors to measure activity. -Deluxe Wrist Blood Pressure Monitor HL168JC: this device can store up to 90 blood pressure and pulse readings. -iPod Nano 4G with Nike R + IPod Sport kit: this sensor records the distance covered during a walk or run and caloric consumption.
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 and behaviour. This chapter proposes an approach and an implementation of semi-automated enrichment of raw sensor data, where the sensor data can come from a wide variety of sources. The authors extract semantics from the sensor data using their XSENSE processing architecture in a multi-stage analysis. The net result is that sensor data values are transformed into XML data so that well-established XML querying via XPATH and similar techniques can be followed. The authors then propose to distribute the XML data on a peer-to-peer configuration and show, through simulations, what the computational costs of executing queries on this P2P network, will be. This approach is validated approach through the use of an array of sensor data readings taken from a range of biometric sensor devices, fitted to movie-watchers as they watched Hollywood movies. These readings were synchronised with video and audio analysis of the actual movies themselves, where we automatically detect movie highlights, which the authors try to correlate with observed human reactions. The XSENSE architecture is used to semantically enrich both the biometric sensor readings and the outputs of video analysis, into one large sensor database. This chapter thus presents and validates a scalable means of semi-automating the semantic enrichment of sensor data, thereby providing a means of large-scale sensor data management which is a necessary step in supporting data mining from sensor networks.
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