Capturing personal health data from wearable sensors

Recently, there has been a significant growth in pervasive computing and ubiquitous sensing which strives to develop and deploy sensing technology all around us. We are also seeing the emergence of applications such as environmental and personal health monitoring to leverage data from a physical world. Most of the developments in this area have been concerned with either developing the sensing technologies, or the infrastructure (middleware) to gather this data and the issues which have been addressed include power consumption on the devices, security of data transmission, networking challenges in gathering and storing the data and fault tolerance in the event of network and/or device failure. Research is focusing on harvesting and managing data and providing query capabilities

Ontology-based document representation for biomedical information retrieval

In the current era of fast sequencing of entire genomes, more data is becoming available for analysis. This data analysis, in turn, leads to an increasing amount of scientfic publications. Consequently, biologists spend a considerable part of their time searching the biomedical literature. This avoids expensive experiment duplications in wet labs, and provides inspiration for new hypotheses. Unfortunately, the fast growth of biological information, in the form of free-text, has led to a lack of standard in the naming of biological entities. As a result, different genes are referred to with the same name, or acronym, and different names refer to tlze same gene. The ambiguity of free-text is problematic, as the success of a search often relies on the matching of a query term with a term contained in the document representation. Biomedical ontologies, when available, can help disambiguate the information expressed in free-text: they provide unique terms to represent concepts and therefore counterweiglzt the occurrence of synonyms and polysems in free-text. They also contain information about the relationships between concepts. This information can be used to understand and evaluate semantic similarities between concepts. The largest repository of biomedical research literature in the world, MEDLINE, is an entry point to biomedical information for most biologists (Hersh et al., 2004). The Medical Subject Headings (MeSH) is the controlled vocabulary used in MEDLINE to annotate the conceptual content of biomedical articles. The annotations include information about the importance of MeSH concepts in the article, and their contexts. The MeSH ontology is organized in several hierarchies that indicate the level of specificity of the MeSH concepts. This hierarchical information can be used to generate semantic similarities between concepts. Our inotivation is the inzprovelnent of MEDLINE search, as it is still a central information access point for biologists in spite of the growing availability of full journal articles on the Web. In particular, we focus on the use of the MeSH ontology to represent and retrieve biomedical articles. Although MeSH is widely used by current MELDINE search methods, we show that the information contained in MEDLINE MeSH annotations and tlze MeSH hierarchies is often overlooked. We hypothesize that MeSH-based document representation can ilzzprove MEDLINE information retrieval. Specifically, our hypothesis is that the integration of iliforniatioli about concept relevance (from the MEDLINE annotation), and interconcept similarities (from tlze MeSH hierarchies), will ilzzprove retrieval performance. We evaluate methods using such information to discriminate and compare MeSH concepts. Our methods are evaluated in the context of MEDLINE ad hoc document retrieval and document binary classifications. Our evaluatiolls use standard datasets and metrics recently used at the Genonzics track of the 2005 Text Retrieval Conference workshop

HealthSense: an application for querying raw sensor data

New 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

On the use of clustering and the MeSH controlled vocabulary to improve MEDLINE abstract search

Databases of genomic documents contain substantial amounts of structured information in addition to the texts of titles and abstracts. Unstructured information retrieval techniques fail to take advantage of the structured information available. This paper describes a technique to improve upon traditional retrieval methods by clustering the retrieval result set into two distinct clusters using additional structural information. Our hypothesis is that the relevant documents are to be found in the tightest cluster of the two, as suggested by van Rijsbergen's cluster hypothesis. We present an experimental evaluation of these ideas based on the relevance judgments of the 2004 TREC workshop Genomics track, and the CLUTO software clustering package

Structural term extraction for expansion of template-based genomic queries

This paper describes our experiments run to address the ad hoc task of the TREC 2005 Genomics track. The task topics were expressed with 5 different structures called Generic Topic Templates (GTTs). We hypothesized the presence of GTT-specific structural terms in the free-text fields of documents relevant to a topic instantiated from that same GTT. Our experiments aimed at extracting and selecting candidate structural terms for each GTT. Selected terms were used to expand initial queries and the quality of the term selection was measured by the impact of the expansion on initial search results. The evaluation used the task training topics and the associated relevance information. This paper describes the two term extraction methods used in the experiments and the resulting two runs sent to NIST for evaluation

Experiments in terabyte searching, genomic retrieval and novelty detection for TREC 2004

In TREC2004, Dublin City University took part in three tracks, Terabyte (in collaboration with University College Dublin), Genomic and Novelty. In this paper we will discuss each track separately and present separate conclusions from this work. In addition, we present a general description of a text retrieval engine that we have developed in the last year to support our experiments into large scale, distributed information retrieval, which underlies all of the track experiments described in this document