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Machine Learning Approach for Prediction of Hematic Parameters in Hemodialysis Patients

Objective: This paper shows the application of machine learning techniques to predict hematic parameters using blood visible spectra during ex-vivo treatments. Methods: A spectroscopic setup was prepared for acquisition of blood absorbance spectrum and tested in an operational environment. This setu...

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Detalles Bibliográficos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: IEEE 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6788674/
https://www.ncbi.nlm.nih.gov/pubmed/32309060
http://dx.doi.org/10.1109/JTEHM.2019.2938951
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collection PubMed
description Objective: This paper shows the application of machine learning techniques to predict hematic parameters using blood visible spectra during ex-vivo treatments. Methods: A spectroscopic setup was prepared for acquisition of blood absorbance spectrum and tested in an operational environment. This setup is non invasive and can be applied during dialysis sessions. A support vector machine and an artificial neural network, trained with a dataset of spectra, have been implemented for the prediction of hematocrit and oxygen saturation. Results & Conclusion: Results of different machine learning algorithms are compared, showing that support vector machine is the best technique for the prediction of hematocrit and oxygen saturation.
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spelling pubmed-67886742020-04-17 Machine Learning Approach for Prediction of Hematic Parameters in Hemodialysis Patients IEEE J Transl Eng Health Med Article Objective: This paper shows the application of machine learning techniques to predict hematic parameters using blood visible spectra during ex-vivo treatments. Methods: A spectroscopic setup was prepared for acquisition of blood absorbance spectrum and tested in an operational environment. This setup is non invasive and can be applied during dialysis sessions. A support vector machine and an artificial neural network, trained with a dataset of spectra, have been implemented for the prediction of hematocrit and oxygen saturation. Results & Conclusion: Results of different machine learning algorithms are compared, showing that support vector machine is the best technique for the prediction of hematocrit and oxygen saturation. IEEE 2019-10-04 /pmc/articles/PMC6788674/ /pubmed/32309060 http://dx.doi.org/10.1109/JTEHM.2019.2938951 Text en https://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
spellingShingle Article
Machine Learning Approach for Prediction of Hematic Parameters in Hemodialysis Patients
title Machine Learning Approach for Prediction of Hematic Parameters in Hemodialysis Patients
title_full Machine Learning Approach for Prediction of Hematic Parameters in Hemodialysis Patients
title_fullStr Machine Learning Approach for Prediction of Hematic Parameters in Hemodialysis Patients
title_full_unstemmed Machine Learning Approach for Prediction of Hematic Parameters in Hemodialysis Patients
title_short Machine Learning Approach for Prediction of Hematic Parameters in Hemodialysis Patients
title_sort machine learning approach for prediction of hematic parameters in hemodialysis patients
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6788674/
https://www.ncbi.nlm.nih.gov/pubmed/32309060
http://dx.doi.org/10.1109/JTEHM.2019.2938951
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