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

Looking for Shadows: The Cultural Myths of the Computer in the Classroom

This paper will draw its findings from a recent study (Lloyd, 2003) which sought to identify the cultural myths of the computer in the classroom through a case study of computer education in Queensland state schools from 1983 to 1997. This was a period marked by its consecutive, discrete, high-profile and politically-motivated projects to put computers in classrooms. The emergent myths were categorised within their source metanarratives and were also positioned within a critical cultural framework. The term "computer education" is given to mean any curricular or classroom-based use of computers. This study addressed a hitherto neglected area of educational research by looking beyond the rhetoric and highlighting where policy decisions have been made on the basis of mythic assumptions. The identification of the cultural myth(s) in this study was essentially a process of looking for shadows. Finding the twenty-seven pervasive myths which initiated and sustained the systemic policies, infrastructure programs and curricular decisions of the period under review involved rigorous processes of deconstruction, reconstruction, analysis and synthesis. The data sources were contemporary policy documents, Hansard entries, press releases and media statements, correspondence and interviews with stakeholders while the methodology employed was an adaptation of Descriptive Interpretational Analysis (Tesch, 1990)

Unsupervised two-class and multi-class support vector machines for abnormal traffic characterization

Although measurement-based real-time traffic classification has received considerable research attention, the timing constraints imposed by the high accuracy requirements and the learning phase of the algorithms employed still remain a challenge. In this paper we propose a measurement-based classification framework that exploits unsupervised learning to accurately categorise network anomalies to specific classes. We introduce the combinatorial use of two-class and multi-class unsupervised Support Vector Machines (SVM)s to first distinguish normal from anomalous traffic and to further classify the latter category to individual groups depending on the nature of the anomaly

Myths of the High Medical Cost of Old Age and Dying

The rising costs of medical care in the United States are often erroneously linked to the growing population of older adults. Despite public perception, health care costs associated with aging are limited. Part of the ILC-USA's project on Ageism In America with generous support from the Open Society Institute, this report identifies and dispels seven myths about caring for older people at the end of life

The Faculty Notebook, September 2016

The Faculty Notebook is published periodically by the Office of the Provost at Gettysburg College to bring to the attention of the campus community accomplishments and activities of academic interest. Faculty are encouraged to submit materials for consideration for publication to the Associate Provost for Faculty Development. Copies of this publication are available at the Office of the Provost

Truth Is a Lie: Crowd Truth and the Seven Myths of Human Annotation

Big data is having a disruptive impact across the sciences. Human annotation of semantic interpretation tasks is a critical part of big data semantics, but it is based on an antiquated ideal of a single correct truth that needs to be similarly disrupted. We expose seven myths about human annotation, most of which derive from that antiquated ideal of truth, and dispell these myths with examples from our research. We propose a new theory of truth, crowd truth, that is based on the intuition that human interpretation is subjective, and that measuring annotations on the same objects of interpretation (in our examples, sentences) across a crowd will provide a useful representation of their subjectivity and the range of reasonable interpretations

Unmasking Medical Fake News Using Machine Learning Techniques

Fake news has always been a critical and challenging problem in the informationenvironment. The propagation of false news is a serious concern, especially in medical information, which can have dangerous and potentially deadly consequences. With the tsunami of online misinformation, it is crucial to fight fake medical news. In this study, we use machine learning techniques to help detect fake news related to diseases, including COVID-19, Ebola, Zika, SARS, Cancer, and Polio. To facilitate research in this space, we create a new medical dataset named MedHub. MedHub has records from two publicly available datasets on COVID and manually curated facts and myths about the other diseases. In addition, we build several different machine learning models trained on MedHub, including KNN, Na¨ıve Bayes, SVM, Logistic regression, and MLP classifier, and present a proof-of-concept web application that uses these models to detect fake medical news. Our best-performing model, which we call Disease Myth Buster, is based on BERT and achieves an accuracy of 99%. In addition, we perform experiments to demonstrate that 1) our models perform well at identifying misinformation related to any disease even if it is not represented in the dataset, and 2) they are well optimized to identify COVID-19 specific misinformation, and 3) Disease Myth Buster can be extended for general fake news classification using Transfer learning. We create two new manually curated test datasets for the first two experiments. The first test dataset has 164 records related to Diabetes and the second test dataset has 13459 records of COVID-19 myths. We open-source all our datasets and models for future research