Abstract
Proton Magnetic Resonance Spectroscopy (1H MRS) has proven its diagnostic potential in a variety of conditions. However, MRS is not yet widely used in clinical routine because of the lack of experts on its diagnostic interpretation. Although data-based decision support systems exist to aid diagnosis, they often take for granted that the data is of good quality, which is not always the case in a real application context. Systems based on models built with bad quality data are likely to underperform in their decision support tasks. In this study, we propose a system to filter out such bad quality data. It is based on convex Non-Negative Matrix Factorization models, used as a dimensionality reduction procedure, and on the use of several classifiers to discriminate between good and bad quality data.
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Acknowledgements
This work was funded by the European Union’s Seventh Framework Programme (FP7/2007-2013) under grant agreement n° ITN-GA-2012-316679 – TRANSACT. This work was also partially funded by CIBER-BBN, which is an initiative of the VI National R&D&i Plan 2008–2011, CIBER Actions and financed by the Instituto de Salud Carlos III with assistance from the European Regional Development Fund.
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Mocioiu, V., Kyathanahally, S.P., Arús, C., Vellido, A., Julià-Sapé, M. (2016). Automated Quality Control for Proton Magnetic Resonance Spectroscopy Data Using Convex Non-negative Matrix Factorization. In: Ortuño, F., Rojas, I. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2016. Lecture Notes in Computer Science(), vol 9656. Springer, Cham. https://doi.org/10.1007/978-3-319-31744-1_62
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DOI: https://doi.org/10.1007/978-3-319-31744-1_62
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