Abstract
Fetal growth curves are considered a critical instrument in prenatal medicine for an appropriate fetal well-being evaluation. Many factors affect fetal growth including physiological and pathological variables, therefore each particular population should have its own reference charts in order to provide the most accurate fetal assessment. In literature a large variety of reference charts are described but they’re up to five decades old and consider hospital-based samples, so they’re not suitable for the current population and furthermore they don’t address the ethnicity, which is an important aspect to take into account. Starting from a detailed analysis of the limitations that characterize the current adopted reference charts, the paper presents a new method, based on multidimensional analysis for creating dynamic and customized fetal growth curves. A preliminary implementation based on open source software, shows why Big Data techniques are essential to solve the problem.
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Bochicchio, M.A., Longo, A., Vaira, L., Ramazzina, S. (2013). Online Data Analysis of Fetal Growth Curves. In: Aversa, R., Kołodziej, J., Zhang, J., Amato, F., Fortino, G. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2013. Lecture Notes in Computer Science, vol 8286. Springer, Cham. https://doi.org/10.1007/978-3-319-03889-6_17
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DOI: https://doi.org/10.1007/978-3-319-03889-6_17
Publisher Name: Springer, Cham
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