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Machine learning to optimize cerebrospinal fluid dilution for analysis of MRZH reaction by Ana Turčić; Andrija Štajduhar; Željka Vogrinc; Ljiljana Zaninović; Dunja Rogić is a Medicine article available to read on EtoBox.

What is Machine learning to optimize cerebrospinal fluid dilution for analysis of MRZH reaction about?

## Abstract ## Objectives To create a supervised machine learning algorithm aimed at predicting an optimal cerebrospinal fluid (CSF) dilution when determining virus specific antibody indices to reduce the need for repeated tests. ## Methods The CatBoost model was trained, optimized, and tested on a dataset with five input variables: albumin quotient, immunoglobulin G (IgG) in CSF, IgG quotient (QIgG), intrathecal synthesis (ITS) and limes quotient (LIM IgG). Albumin and IgG concentrations in CSF and serum were performed by immunonephelometry on Atellica NEPH 630 (Siemens Healthineers, Erlangen, Germany) and ITS and LIM IgG were calculated according to Reiber. Concentrations of IgG antibodies to measles, rubella, varicella zoster and herpes simplex 1/2 viruses were analysed in CSF and serum by ELISA (Euroimmun, Lübeck, Germany). Optimal CSF dilution was defined for each virus and used as a classification variable while the standard operating procedure was set to start at 2×-dilution of CSF. ## Results The dataset included 571 samples with the imbalanced distribution of the optimal CSF dilutions: 2× dilution n=440, 3× dilution n=109, 4× dilution n=22. The optimized CatBoost model ach

Who reads Machine learning to optimize cerebrospinal fluid dilution for analysis of MRZH reaction?

Author

Ana Turčić; Andrija Štajduhar; Željka Vogrinc; Ljiljana Zaninović; Dunja Rogić

Publisher

Walter de Gruyter GmbH

Published

2023

Field

Medicine (Health Sciences)