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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...
Formato: | Online Artículo Texto |
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Lenguaje: | English |
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IEEE
2019
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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. |
format | Online Article Text |
id | pubmed-6788674 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | IEEE |
record_format | MEDLINE/PubMed |
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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