Document re-ranking by generality in bio-medical information retrieval

Document ranking is an important process in information retrieval (IR). It presents retrieved documents in an order of their estimated degrees of relevance to query. Traditional document ranking methods are mostly based on the similarity computations between documents and query. In this paper we argue that the similarity-based document ranking is insufficient in some cases. There are two reasons. Firstly it is about the increased information variety. There are far too many different types documents available now for user to search. The second is about the users variety. In many cases user may want to retrieve documents that are not only similar but also general or broad regarding a certain topic. This is particularly the case in some domains such as bio-medical IR. In this paper we propose a novel approach to re-rank the retrieved documents by incorporating the similarity with their generality. By an ontology-based analysis on the semantic cohesion of text, document generality can be quantified. The retrieved documents are then re-ranked by their combined scores of similarity and the closeness of documents’ generality to the query’s. Our experiments have shown an encouraging performance on a large bio-medical document collection, OHSUMED, containing 348,566 medical journal references and 101 test queries.

Concept-based document readability in domain specific information retrieval

Domain specific information retrieval has become in demand. Not only domain experts, but also average non-expert users are interested in searching domain specific (e.g., medical and health) information from online resources. However, a typical problem to average users is that the search results are always a mixture of documents with different levels of readability. Non-expert users may want to see documents with higher readability on the top of the list. Consequently the search results need to be re-ranked in a descending order of readability. It is often not practical for domain experts to manually label the readability of documents for large databases. Computational models of readability needs to be investigated. However, traditional readability formulas are designed for general purpose text and insufficient to deal with technical materials for domain specific information retrieval. More advanced algorithms such as textual coherence model are computationally expensive for re-ranking a large number of retrieved documents. In this paper, we propose an effective and computationally tractable concept-based model of text readability. In addition to textual genres of a document, our model also takes into account domain specific knowledge, i.e., how the domain-specific concepts contained in the document affect the document’s readability. Three major readability formulas are proposed and applied to health and medical information retrieval. Experimental results show that our proposed readability formulas lead to remarkable improvements in terms of correlation with users’ readability ratings over four traditional readability measures.

Telemedical support for Iraq

The Swinfen Charitable Trust (SCT) provided two kinds of telemedical support to Iraq during 2004. Starting in January 2004, the Al-Yarmouk Teaching Hospital in Baghdad was able to refer cases into the well established global e-health network that the SCT has operated for the last five years. (In the first quarter of 2004, the SCT dealt with a total of 57 referrals from 15 hospitals in eight countries.) Two cases were referred from Baghdad in March 2004, both gynaecological, which were dealt with by consultants from the UK and Australia. The SCT administrators visited Basrah during April 2004 and met Iraqi doctors at the Shaibah Hospital as part of the international initiatives to improve health care there. Following this visit, the SCT network expanded to include another four hospitals in Iraq (Table 1). In addition, the SCT provided an electronic health records (EHR) system to support the rebuilding of maternity services, which has been led by the British Royal Colleges. The maternity records system is a Web-based EHR system, running on a secure server, which allows integrated access from antenatal clinics, from hospitals and from postnatal clinics in Iraq. Patients can view their own notes, thus promoting ownership of medical information, and doctors can view the notes of their own patients, from any Internet-connected PC. No special software is required by the user.

Evaluation of unsupervised semantic mapping of natural language with Leximancer concept mapping

The Leximancer system is a relatively new method for transforming lexical co-occurrence information from natural language into semantic patterns in an unsupervised manner. It employs two stages of co-occurrence information extraction-semantic and relational-using a different algorithm for each stage. The algorithms used are statistical, but they employ nonlinear dynamics and machine learning. This article is an attempt to validate the output of Leximancer, using a set of evaluation criteria taken from content analysis that are appropriate for knowledge discovery tasks.

How do world-class cricket batsmen anticipate a bowler's intention?

Four experiments are reported that examine the ability of cricket batsmen of different skill levels to pick up advance information to anticipate the type and length of balls bowled by swing and spin bowlers. The information available upon which to make the predictive judgements was manipulated through a combination of temporal occlusion of the display and selective occlusion or presentation of putative anticipatory cues. In addition to a capability to pick up advance information from the same cues used by intermediate and low-skilled players, highly skilled players demonstrated the additional, unique capability to pick up advance information from some specific early cues (especially bowling hand and arm cues) to which the less skilled players were not attuned. The acquisition of expert perceptual-motor skill appears to involve not only refinement of information extraction but also progression to the use of earlier, kinematically relevant sources of information.

Linking riparian vegetation spatial structure in Australian tropical savannas to ecosystem health indicators: semi-variogram analysis of high spatial resolution satellite imagery

Government agencies responsible for riparian environments are assessing the combined utility of field survey and remote sensing for mapping and monitoring indicators of riparian zone health. The objective of this work was to determine if the structural attributes of savanna riparian zones in northern Australia can be detected from commercially available remotely sensed image data. Two QuickBird images and coincident field data covering sections of the Daly River and the South Alligator River - Barramundie Creek in the Northern Territory were used. Semi-variograms were calculated to determine the characteristic spatial scales of riparian zone features, both vegetative and landform. Interpretation of semi-variograms showed that structural dimensions of riparian environments could be detected and estimated from the QuickBird image data. The results also show that selecting the correct spatial resolution and spectral bands is essential to maximize the accuracy of mapping spatial characteristics of savanna riparian features. The distribution of foliage projective cover of riparian vegetation affected spectral reflectance variations in individual spectral bands differently. Pan-sharpened image data enabled small-scale information extraction (< 6 m) on riparian zone structural parameters. The semi-variogram analysis results provide the basis for an inversion approach using high spatial resolution satellite image data to map indicators of savanna riparian zone health.