Monash University

About: Monash University is a education organization based out in Melbourne, Victoria, Australia. It is known for research contribution in the topics: Population & Poison control. The organization has 35920 authors who have published 100681 publications receiving 3027002 citations.

Self-management education and regular practitioner review for adults with asthma

Abstract: Background A key component of many asthma management guidelines is the recommendation for patient education and regular medical review. A number of controlled trials have been conducted to measure the effectiveness of asthma education programmes. These programmes improve patient knowledge, but their impact on health outcomes is less well established. This review was conducted to examine the strength of evidence supporting Step 6 of the Australian Asthma Management Plan: "Educate and Review Regularly"; to test whether health outcomes are influenced by education and self-management programmes. Objectives The objective of this review was to assess the effects of asthma self-management programmes, when coupled with regular health practitioner review, on health outcomes in adults with asthma. Search methods We searched the Cochrane Airways Group trials register and reference lists of articles. Selection criteria Randomised trials of self-management education in adults over 16 years of age with asthma. Data collection and analysis Two reviewers assessed trial quality and extracted data independently. We contacted study authors for confirmation. Main results We included thirty six trials, which compared self-management education with usual care. Self-management education reduced hospitalisations (relative risk (RR) 0.64, 95% confidence interval (CI) 0.50 to 0.82); emergency room visits (RR 0.82, 95% CI 0.73 to 0.94); unscheduled visits to the doctor (RR 0.68, 95% CI 0.56 to 0.81); days off work or school (RR 0.79, 95% CI 0.67 to 0.93); nocturnal asthma (RR 0.67, 95% CI 0.0.56 to 0.79); and quality of life (standard mean difference 0.29,CI 0.11 to 0.47). Measures of lung function were little changed. Authors' conclusions Education in asthma self-management which involves self-monitoring by either peak expiratory flow or symptoms, coupled with regular medical review and a written action plan improves health outcomes for adults with asthma. Training programmes that enable people to adjust their medication using a written action plan appear to be more effective than other forms of asthma self-management.

Synthesis without meta-analysis (SWiM) in systematic reviews: reporting guideline.

Abstract: In systematic reviews that lack data amenable to meta-analysis, alternative synthesis methods are commonly used, but these methods are rarely reported. This lack of transparency in the methods can cast doubt on the validity of the review findings. The Synthesis Without Meta-analysis (SWiM) guideline has been developed to guide clear reporting in reviews of interventions in which alternative synthesis methods to meta-analysis of effect estimates are used. This article describes the development of the SWiM guideline for the synthesis of quantitative data of intervention effects and presents the nine SWiM reporting items with accompanying explanations and examples.

Isolation-Based Anomaly Detection

Abstract: Anomalies are data points that are few and different. As a result of these properties, we show that, anomalies are susceptible to a mechanism called isolation. This article proposes a method called Isolation Forest (iForest), which detects anomalies purely based on the concept of isolation without employing any distance or density measure---fundamentally different from all existing methods.As a result, iForest is able to exploit subsampling (i) to achieve a low linear time-complexity and a small memory-requirement and (ii) to deal with the effects of swamping and masking effectively. Our empirical evaluation shows that iForest outperforms ORCA, one-class SVM, LOF and Random Forests in terms of AUC, processing time, and it is robust against masking and swamping effects. iForest also works well in high dimensional problems containing a large number of irrelevant attributes, and when anomalies are not available in training sample.