Annals of the New York Academy of Sciences
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Ann. N. Y. Acad. Sci. · Jan 2017
ReviewResistance diagnosis and the changing economics of antibiotic discovery.
Point-of-care diagnostics that can determine an infection's antibiotic sensitivity increase the profitability of new antibiotics that enjoy patent protection, even when such diagnostics reduce the quantity of antibiotics sold. Advances in the science and technology underpinning rapid resistance diagnostics can therefore be expected to spur efforts to discover and develop new antibiotics, especially those with a narrow spectrum of activity that would otherwise fail to find a market.
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Ann. N. Y. Acad. Sci. · Jan 2017
Comparative StudyMachine learning approaches to personalize early prediction of asthma exacerbations.
Patient telemonitoring results in an aggregation of significant amounts of information about patient disease trajectory. However, the potential use of this information for early prediction of exacerbations in adult asthma patients has not been systematically evaluated. The aim of this study was to explore the utility of telemonitoring data for building machine learning algorithms that predict asthma exacerbations before they occur. ⋯ Our study demonstrated that machine learning techniques have significant potential in developing personalized decision support for chronic disease telemonitoring systems. Future studies may benefit from a comprehensive predictive framework that combines telemonitoring data with other factors affecting the likelihood of developing acute exacerbation. Approaches implemented for advanced asthma exacerbation prediction may be extended to prediction of exacerbations in patients with other chronic health conditions.
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Ann. N. Y. Acad. Sci. · Jan 2017
ReviewA brief primer on genomic epidemiology: lessons learned from Mycobacterium tuberculosis.
Genomics is now firmly established as a technique for the investigation and reconstruction of communicable disease outbreaks, with many genomic epidemiology studies focusing on revealing transmission routes of Mycobacterium tuberculosis. In this primer, we introduce the basic techniques underlying transmission inference from genomic data, using illustrative examples from M. tuberculosis and other pathogens routinely sequenced by public health agencies. We describe the laboratory and epidemiological scenarios under which genomics may or may not be used, provide an introduction to sequencing technologies and bioinformatics approaches to identifying transmission-informative variation and resistance-associated mutations, and discuss how variation must be considered in the light of available clinical and epidemiological information to infer transmission.
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Ann. N. Y. Acad. Sci. · Jan 2017
Data science, learning, and applications to biomedical and health sciences.
The last decade has seen an unprecedented increase in the volume and variety of electronic data related to research and development, health records, and patient self-tracking, collectively referred to as Big Data. Properly harnessed, Big Data can provide insights and drive discovery that will accelerate biomedical advances, improve patient outcomes, and reduce costs. However, the considerable potential of Big Data remains unrealized owing to obstacles including a limited ability to standardize and consolidate data and challenges in sharing data, among a variety of sources, providers, and facilities. Here, we discuss some of these challenges and potential solutions, as well as initiatives that are already underway to take advantage of Big Data.