Abstract
A major public health problem is the chronically respiratory ill patients. To create a more preventive and anticipatory system for these patients we can use artificial intelligence techniques. This work tackle the problem of developing a model for automatic classification of patients with risk of having a respiratory crisis on the biggest paediatric Public Hospital in Santiago, Chile. We present a benchmark of different approaches to create a model. The models were developed with history of biomedical signals for 45 patients from 0 months to 15 years old. We are able to identify to approaches which have a remarkable performance.
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Acknowledgments
This work was supported by CONICYT programmes: IDeA FONDEF project code: CA13i-10300, and FONDECYT project code: 11130252. Also, authors would like to thank the continuous support of “Instituto Sistemas Complejos de Ingeniería” (ICM: P-05-004- F, CONICYT: FBO16).
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Ríos, S.A., Tenorio, F.G., Jimenez-Molina, A. (2016). A Benchmark on Artificial Intelligence Techniques for Automatic Chronic Respiratory Diseases Risk Classification. In: Chen, YW., Torro, C., Tanaka, S., Howlett, R., C. Jain, L. (eds) Innovation in Medicine and Healthcare 2015. Smart Innovation, Systems and Technologies, vol 45. Springer, Cham. https://doi.org/10.1007/978-3-319-23024-5_43
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DOI: https://doi.org/10.1007/978-3-319-23024-5_43
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