Normalization methods in time series of platelet function assays
| Autor: | Ming-Hua Zheng, Mark Roest, Milan Vukicevic, Zhongheng Zhang, Sven Van Poucke, Marcus D. Lancé, Maud Beran, Bart Lauwereins, Abraham Marcus, Yvonne M. C. Henskens |
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| Přispěvatelé: | Medische Microbiologie, MUMC+: DA CDL Algemeen (9), RS: CARIM - R1.04 - Clinical thrombosis and haemostasis, MUMC+: MA Anesthesiologie (9) |
| Rok vydání: | 2016 |
| Předmět: |
Normalization (statistics)
Multivariate statistics Platelet Function Tests thromboelastometry 030218 nuclear medicine & medical imaging Correlation Database normalization 03 medical and health sciences multivariate 0302 clinical medicine Predictive Value of Tests Interquartile range Preoperative Care Quality Improvement Study Humans Medicine Longitudinal Studies Coronary Artery Bypass data space Time point business.industry Pattern recognition General Medicine aggregometry Thromboelastometry high-dimensional normalization Data Interpretation Statistical Predictive value of tests platelets Artificial intelligence business 030217 neurology & neurosurgery Research Article |
| Zdroj: | Medicine Medicine, 95(28). LIPPINCOTT WILLIAMS & WILKINS |
| ISSN: | 0025-7974 |
| DOI: | 10.1097/md.0000000000004188 |
| Popis: | Platelet function can be quantitatively assessed by specific assays such as light-transmission aggregometry, multiple-electrode aggregometry measuring the response to adenosine diphosphate (ADP), arachidonic acid, collagen, and thrombin-receptor activating peptide and viscoelastic tests such as rotational thromboelastometry (ROTEM). The task of extracting meaningful statistical and clinical information from high-dimensional data spaces in temporal multivariate clinical data represented in multivariate time series is complex. Building insightful visualizations for multivariate time series demands adequate usage of normalization techniques. In this article, various methods for data normalization (z-transformation, range transformation, proportion transformation, and interquartile range) are presented and visualized discussing the most suited approach for platelet function data series. Normalization was calculated per assay (test) for all time points and per time point for all tests. Interquartile range, range transformation, and z-transformation demonstrated the correlation as calculated by the Spearman correlation test, when normalized per assay (test) for all time points. When normalizing per time point for all tests, no correlation could be abstracted from the charts as was the case when using all data as 1 dataset for normalization. |
| Databáze: | OpenAIRE |
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