Infection status outcome, machine learning method and virus type interact to affect the optimised prediction of hepatitis virus immunoassay results from routine pathology laboratory assays in unbalanced data

BACKGROUND Advanced data mining techniques such as decision trees have been successfully used to predict a variety of outcomes in complex medical environments. Furthermore, previous research has shown that combining the results of a set of individually trained trees into an ensemble-based classifier can improve overall classification accuracy. This paper investigates the effect of data pre-processing, the use of ensembles constructed by bagging, and a simple majority vote to combine classification predictions from routine pathology laboratory data, particularly to overcome a large imbalance of negative Hepatitis B virus (HBV) and Hepatitis C virus (HCV) cases versus HBV or HCV immunoassay positive cases. These methods were illustrated using a never before analysed data set from ACT Pathology (Canberra, Australia) relating to HBV and HCV patients. RESULTS It was easier to predict immunoassay positive cases than negative cases of HBV or HCV. While applying an ensemble-based approach rather than a single classifier had a small positive effect on the accuracy rate, this also varied depending on the virus under analysis. Finally, scaling data before prediction also has a small positive effect on the accuracy rate for this dataset. A graphical analysis of the distribution of accuracy rates across ensembles supports these findings. CONCLUSIONS Laboratories looking to include machine learning as part of their decision support processes need to be aware that the infection outcome, the machine learning method used and the virus type interact to affect the enhanced laboratory diagnosis of hepatitis virus infection, as determined by primary immunoassay data in concert with multiple routine pathology laboratory variables. This awareness will lead to the informed use of existing machine learning methods, thus improving the quality of laboratory diagnosis via informatics analyses.The project was funded by The Medical Advances Without Animals Trust (MAWA)

Infection status outcome, machine learning method and virus type interact to affect the optimised prediction of hepatitis virus immunoassay results from routine pathology laboratory assays in unbalanced data

BACKGROUND: Advanced data mining techniques such as decision trees have been successfully used to predict a variety of outcomes in complex medical environments. Furthermore, previous research has shown that combining the results of a set of individually trained trees into an ensemble-based classifier can improve overall classification accuracy. This paper investigates the effect of data pre-processing, the use of ensembles constructed by bagging, and a simple majority vote to combine classification predictions from routine pathology laboratory data, particularly to overcome a large imbalance of negative Hepatitis B virus (HBV) and Hepatitis C virus (HCV) cases versus HBV or HCV immunoassay positive cases. These methods were illustrated using a never before analysed data set from ACT Pathology (Canberra, Australia) relating to HBV and HCV patients. RESULTS: It was easier to predict immunoassay positive cases than negative cases of HBV or HCV. While applying an ensemble-based approach rather than a single classifier had a small positive effect on the accuracy rate, this also varied depending on the virus under analysis. Finally, scaling data before prediction also has a small positive effect on the accuracy rate for this dataset. A graphical analysis of the distribution of accuracy rates across ensembles supports these findings. CONCLUSIONS: Laboratories looking to include machine learning as part of their decision support processes need to be aware that the infection outcome, the machine learning method used and the virus type interact to affect the enhanced laboratory diagnosis of hepatitis virus infection, as determined by primary immunoassay data in concert with multiple routine pathology laboratory variables. This awareness will lead to the informed use of existing machine learning methods, thus improving the quality of laboratory diagnosis via informatics analyses

Respiratory syncytial virus--the unrecognised cause of health and economic burden among young children in Australia.

Respiratory syncytial virus (RSV) presents very similar to influenza and is the principle cause of bronchiolitis in infants and young children worldwide. Yet, there is no systematic monitoring of RSV activity in Australia. This study uses existing published data sources to estimate incidence, hospitalisation rates, and associated costs of RSV among young children in Australia. Published reports from the Laboratory Virology and Serology Reporting Scheme, a passive voluntary surveillance system, and the National Hospital Morbidity Dataset were used to estimate RSV-related age-specific hospitalisation rates in New South Wales and Australia. These estimates and national USA estimates of RSV-related hospitalisation rates were applied to Australian population data to estimate RSV incidence in Australia. Direct economic burden was estimated by applying cost estimates used to derive economic cost associated with the influenza virus. The estimated RSV-related hospitalisation rates ranged from 2.2-4.5 per 1,000 among children less than 5 years of age to 8.7-17.4 per 1,000 among infants. Incidence ranged from 110.0-226.5 per 1,000 among the under five age group to 435.0-869.0 per 1,000 among infants. The total annual direct healthcare cost was estimated to be between $24 million and$50 million. Comparison with the health burdens attributed to the influenza virus and rotavirus suggests that the disease burden caused by RSV is potentially much higher. The limitations associated with using a passive surveillance system to estimate disease burden, and the need to explore further assessments and to monitor RSV activity are discussed

Clinical chemistry in higher dimensions: machine-learning and enhanced prediction from routine clinical chemistry data

Big Data is having an impact on many areas of research, not the least of which is biomedical science. In this review paper, big data and machine learning are defined in terms accessible to the clinical chemistry community. Seven myths associated with machine learning and big data are then presented, with the aim of managing expectation of machine learning amongst clinical chemists. The myths are illustrated with four examples investigating the relationship between biomarkers in liver function tests, enhanced laboratory prediction of hepatitis virus infection, the relationship between bilirubin and white cell count, and the relationship between red cell distribution width and laboratory prediction of anaemia.This work was supported by the Quality Use of Pathology Programme (QUPP), The Commonwealth Department of Health

It’s all foreign to me: learning through the language of genetics and molecular biology

Molecular Biology and Genetics are taught to undergraduate students at the second and third year level of their University of Canberra degrees. The assumption is, particularly for the third year students who have passed previous units/subjects in fundamental chemistry, biology and biochemistry, that the concepts and many of the details confronted in the more specialised discipline areas like genetics/molecular biology will be familiar. Anecdotally, this has not been true and the specialised language, for a majority of students, leads to a loss of engagement with the content of the unit and hence the learning outcomes. While there are issues around retention of knowledge from previously studied foundation units, it seems a bigger problem is that students find the lecture material, readings and other study material impenetrable

Activating multiple senses in learning Statistics

Introduction to Statistics at the University of Canberra (UC) is a service unit taken by students from a variety of disciplines. However, it is common for students to dislike and under-perform in Statistics. We sought to address these issues by redesigning the way that Statistics is taught. Given the importance of acquiring statistical language to learning Statistics, we decided to employ language learning techniques in Statistics classes. The project brought together a statistician and an educational expert to reconceptualise the syllabus, and focused on developing different methods of delivery. New teaching materials including online exercises and new ways of delivery involving multiple senses of hearing, speaking and moving were designed and produced, placing greater emphasis on applying statistics and interpreting data. Two cohorts of students were evaluated, the control cohort (CG, 2007 Semester 1) with a traditional teaching style, and the experimental cohort (EG) taught with non-traditional methods, as summarised above (2008 Semester 1). Students in EG showed a greater improvement in defining key concepts such as population and standard deviation and have improved attitudes towards the role of statistics to their disciplines and performed significantly better in class tests and examinations

One potato, two potato, three potato, four: the use of Hot Potatoes software in science language comprehension

Our paper concentrates on innovation in teaching two ‘hard’ sciences, namely Genetics and Statistics. Reform in the teaching of Statistics moved through the higher education sector in Australia in the 1990s. Emphasis was placed on statistical thinking and active learning rather than recipes and derivations. New-style textbooks and laboratory manuals were published that employed teaching techniques from a variety of disciplines, but not from language teaching. Teaching in Genetics, generally, is in a transmissive style and as the language of Genetics is as foreign as a foreign language, texts written become inaccessible to many students. An earlier study (Zhang and Lidbury 2006) has examined a range of language techniques in the teaching of tertiary Genetics and Molecular Biology, and has recently focussed on language learning via the Hot Potatoes software. For this original study, Hot Potatoes was used as one of a suite of language-centred teaching approaches, so its full value has not, thus far, been individually assessed. Anecdotally, Hot Potatoes was a great tool to revise genetic language from previous lectures, and was appreciated by motivated students who wished to explore extra voluntary online exercises, or use the Hot Potatoes exercises as study tools. This study in Statistics will focus primarily on Hot Potatoes and assess it as a tool through which to teach statistical language

Myalgic encephalomyelitis/chronic fatigue syndrome: A comprehensive review

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating chronic disease of unknown aetiology that is recognized by the World Health Organization (WHO) and the United States Center for Disease Control and Prevention (US CDC) as a disorder of the brain. The disease predominantly affects adults, with a peak age of onset of between 20 and 45 years with a female to male ratio of 3:1. Although the clinical features of the disease have been well established within diagnostic criteria, the diagnosis of ME/CFS is still of exclusion, meaning that other medical conditions must be ruled out. The pathophysiological mechanisms are unclear but the neuro-immuno-endocrinological pattern of CFS patients gleaned from various studies indicates that these three pillars may be the key point to understand the complexity of the disease. At the moment, there are no specific pharmacological therapies to treat the disease, but several studies’ aims and therapeutic approaches have been described in order to benefit patients’ prognosis, symptomatology relief, and the recovery of pre-existing function. This review presents a pathophysiological approach to understanding the essential concepts of ME/CFS, with an emphasis on the population, clinical, and genetic concepts associated with ME/CFS. © 2019 by the authors

Language difficulties in first year Science - an interim report

A key goal of the study entitled ‘A cross-disciplinary approach to language support for first year students in the science disciplines’, funded by the Carrick Institute for Learning and Teaching in Higher Education, is to examine the role of language in the learning of science by first-year university students. The disciplines involved are Physics, Chemistry and Biology. This national project also aims to transfer active learning skills, which are widely used in language teaching, to the teaching of science in first year. The paper discusses the background to the study, reports on some of the preliminary results on the language difficulties faced by first year student cohorts in science from data collected in 2008, and describes the framework we have established for the organization and delivery of first year science courses to be implemented in semester one 2009