Machine Learning Approach for Prediction of Hematic Parameters in Hemodialysis Patients

被引：15

作者：

Decaro, Cristoforo ^{[1
]}

Montanari, Giovanni Battista ^{[2
]}

Molinari, Riccardo ^{[3
]}

Gilberti, Alessio ^{[2
]}

Bagnoli, Davide ^{[4
]}

Bianconi, Marco ^{[2
,5
]}

Bellanca, Gaetano ^{[1
]}

机构：

[1] Ferrara Univ, Dept Engn, I-44122 Ferrara, Italy

[2] MIST ER, I-40129 Bologna, Italy

[3] Tecnoideal Srl, I-41037 Mirandola, Italy

[4] Medica Spa, I-41036 Medolla, Italy

[5] CNR IMM UOS Bologna, I-40129 Bologna, Italy

来源：

IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE | 2019年 / 7卷

关键词：

Artificial neural network; hematocrit; hemodialisys; machine learning; non-invasive; oxygen saturation; SVM; visible spectroscopy; HEMATOCRIT; SYSTEM; HEART;

D O I：

10.1109/JTEHM.2019.2938951

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

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.

引用

页数：8

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