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019 ▼a 962325041 ▼a 970389366 ▼a 970613635
020 ▼a 1849199795 ▼q (electronic bk.)
020 ▼a 9781849199797 ▼q (electronic bk.)
020 ▼z 1849199787
020 ▼z 9781849199780
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08204 ▼a 610.3 ▼2 23
1001 ▼a Clifton, David A.
24510 ▼a Machine Learning for Healthcare Technologies.
260 ▼b The Institution of Engineering and Technology, ▼c 2016.
300 ▼a 1 online resource (296 pages).
336 ▼a text ▼b txt ▼2 rdacontent
337 ▼a computer ▼b c ▼2 rdamedia
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4901 ▼a Healthcare technologies; ▼v 2
5050 ▼a 1. Machine learning for healthcare technologies -- an introduction / David A. Clifton; 2. Detecting artifactual events in vital signs monitoring data / Partha Lal, Christopher K.I. Williams, Konstantinos Georgatzis, Christopher Hawthorne, Paul McMonagle, Ian Piper and Martin Shaw; 3. Signal processing and feature selection preprocessing for classification in noisy healthcare data / Qiao Li, Chengyu Liu, Julien Oster and Gari D. Clifford; 4. ECG model-based Bayesian filtering / Julien Oster.
5058 ▼a 5. The power of tensor decompositions in biomedical applications / Borb?la Hunyadi, Sabine Van Huffel and Maarten De Vos6. Patient physiological monitoring with machine learning / Marco A.F. Pimentel and David A. Clifton; 7. A Bayesian model for fusing biomedical labels / Tingting Zhu, Gari D. Clifford and David A. Clifton; 8. Incorporating end-user preferences in predictive models / Suchi Saria and Daniel P. Robinson; 9. Variational Bayesian non-parametric inference for infectious disease models / James Hensman and Theodore Kypraios.
5058 ▼a 10. Predicting antibiotic resistance from genomic data / Yang Yang, Katherine E. Niehaus and David A. Clifton11. Machine learning for chronic disease / Katherine E. Niehaus and David A. Clifton; 12. Big data and optimisation of treatment strategies / Shamim Nemati and Mohammad M. Ghassemi; 13. Decision support systems for home monitoring applications: Classification of activities of daily living and epileptic seizures / Stijn Luca, Lode Vuegen, Hugo Van hamme, Peter Karsmakers and Bart Vanrumste; Index.
520 ▼a This book brings together chapters on the state-of-the-art in machine learning (ML) as it applies to the development of patient-centred technologies, with a special emphasis on 'big data' and mobile data.
5880 ▼a Print version record.
590 ▼a eBooks on EBSCOhost ▼b All EBSCO eBooks
650 0 ▼a Medical innovations.
650 0 ▼a Medical informatics.
650 0 ▼a Machine learning.
650 0 ▼a Medicine ▼x Data processing.
650 7 ▼a HEALTH & FITNESS ▼x Holism. ▼2 bisacsh
650 7 ▼a HEALTH & FITNESS ▼x Reference. ▼2 bisacsh
650 7 ▼a MEDICAL ▼x Alternative Medicine. ▼2 bisacsh
650 7 ▼a MEDICAL ▼x Atlases. ▼2 bisacsh
650 7 ▼a MEDICAL ▼x Essays. ▼2 bisacsh
650 7 ▼a MEDICAL ▼x Family & General Practice. ▼2 bisacsh
650 7 ▼a MEDICAL ▼x Holistic Medicine. ▼2 bisacsh
650 7 ▼a MEDICAL ▼x Osteopathy. ▼2 bisacsh
650 7 ▼a Machine learning. ▼2 fast ▼0 (OCoLC)fst01004795
650 7 ▼a Medical informatics. ▼2 fast ▼0 (OCoLC)fst01014175
650 7 ▼a Medical innovations. ▼2 fast ▼0 (OCoLC)fst01014181
650 7 ▼a Medicine ▼x Data processing. ▼2 fast ▼0 (OCoLC)fst01014924
650 7 ▼a Bayes methods. ▼2 inspect
650 7 ▼a health care. ▼2 inspect
650 7 ▼a learning (artificial intelligence) ▼2 inspect
650 7 ▼a patient monitoring. ▼2 inspect
650 7 ▼a physiological models. ▼2 inspect
650 2 ▼a Biomedical Technology.
655 4 ▼a Electronic books.
77608 ▼i Print version: ▼a Clifton, David A. ▼t Machine Learning for Healthcare Technologies. ▼d The Institution of Engineering and Technology, 2016 ▼z 9781849199780 ▼z 1849199787 ▼w (OCoLC)950950855
830 0 ▼a Healthcare technologies series ; ▼v 2.
85640 ▼u http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1413233
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