Machine learning (ML) is a burgeoning field of medicine with huge resources being applied to fuse computer science and statistics to medical problems. Proponents of ML extol its ability to deal with large, complex and disparate data, often found within medicine and feel that ML is the future for biomedical research, personalized medicine, computer-aided diagnosis to significantly advance global health care. However, the concepts of ML are unfamiliar to many medical professionals and there is untapped potential in the use of ML as a research tool. In this article, we provide an overview of the theory behind ML, explore the common ML algorithms used in medicine including their pitfalls and discuss the potential future of ML in medicine.

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Abstract.Text messaging through the Internet or cellular phones has become a major medium of personal and commercial communication. In the same time, flames (such as rants, taunts, and squalid phrases) are offensive/abusive phrases which might attack or offend the users for a variety of reasons. An automatic discriminative software with a sensitivity parameter for flame or abusive language detection would be a useful tool. Although a human could recognize these sorts of useless annoying texts among the useful ones, it is not an easy task for computer programs. In this paper, we describe an automatic flame detection method which extracts features at different conceptual levels and applies multilevel classification for flame detection. While the system is taking advantage of a variety of statistical models and rule-based patterns, there is an auxiliary weighted pattern repository which improves accuracy by matching the text to its graded entries.

Abstract. The purpose of this work is to reduce the workload of human experts in building systematic reviews from published articles, used in evidence-based medicine. We propose to use a committee of classifiers to rank biomedical abstracts based on the predicted relevance to the topic under review. In our approach, we identify two subsets of abstracts: one that represents the top, and another that represents the bottom of the ranked list. These subsets, identified using machine learning (ML) techniques, are considered zones where abstracts are labeled with high confidence as relevant or irrelevant to the topic of the review. Early experiments with this approach using different classifiers and different representation techniques show significant workload reduction.

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