No abstract

There have been few studies of large corpora of narrative notes collected from the health clinicians working at the point of care. This chapter describes the principle issues in analysing a corpus of 44 million words of clinical notes drawn from the Intensive Care Service of a Sydney hospital. The study identifies many of the processing difficulties in dealing with written materials that have a high degree of informality, written in circumstances where the authors are under significant time pressures, and containing a large technical lexicon, in contrast to formally published material. Recommendations on the processing tasks needed to turn such materials into a more usable form are provided. The chapter argues that these problems require a return to issues of 30 years ago that have been mostly solved for computational linguists but need to be revisited for this entirely new genre of materials. In returning to the past and studying the contents of these materials in retrospective studies we can plan to go forward to a future that provides technologies that better support clinicians. They need to produce both lexically and grammatically higher quality texts that can then be leveraged successfully for advanced translational research thereby bolstering its momentum.

This paper, reports on the results of research which is based originally on the 2009 criteria and corpus of ''The Obesity Challenge", defined by Informatics for IntegratingBiology and the Bedside (i2b2), a National Center for Biomedical Computing. In the original task, i2b2 asked participants to build software systems that could process a corpus of noisy patient's clinical discharge summaries and report on patients' condition. The ultimate aim was to compare the judgments of physicians in evaluating the patient condition to a machine performance over such a corpus.The authors used a collection of resources to lexically and semantically characterize the diseases and their associated signs, symptoms.In this approach, they combined dictionary look-up, rule-based, and machine-learning methods along with taking advantage of existing knowledge within the clinical notes to reduce the usage of customized rules and increase the consistency of the performance over various types of noisy corpora. The performance was strengthened by information extracted from the patient notes via an internal redundancy module to overcome False Positives (FPs) and False Negatives (FNs) arising from the noisy nature of corpus.The methods were applied to a collection of 507 previously unseen noisy patient discharge summaries, and the Judgments were evaluated against a manually provided gold standard. The overall ranking of the participating Research groups were primarily based on the macroaveraged F-measure over 16 Classes of diseases. The implemented method achieved the micro-aver aged Fmeasure of 96.9% (ranked within the top 7 out of 28 research groups) where there was no statistical significant difference between top 7 teams in micro F-measure.The highest F-Measure was 97.2%.Comparison of the results of this approach to results of other submitted classical approaches showed using existing knowledge within clinical notes can boost the accuracy of classifiers without extensive usage of rules and customization and therefore has potential for a more consistent performance and more efficient processing over various type of noisy corpora.

No abstract

Predicting the market value of a residential property accurately without inspection by professional valuer could be beneficial for vary of organization and people. Building an Automated Valuation Model could be beneficial if it will be accurate adequately. This paper examined 47 machine learning models (linear and non-linear). These models are fitted on 1967 records of units from 19 suburbs of Sydney, Australia. The main aim of this paper is to compare the performance of these techniques using this data set and investigate the effect of spatial information on valuation accuracy. The results demonstrated that tree models named eXtreme Gradient Boosting Linear, eXtreme Gradient Boosting Tree and Random Forest respectively have best performance among other techniques and spatial information such drive distance and duration to CBD increase the predictive model performance significantly.