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Authors:
Emma Frennborn
Khalil Rust
Les Sztandera
Keywords: data mining; data modeling; databases; heavy metals; neonatal health; maternal health
Abstract:
Essential heavy metals, such as zinc, copper, iron, and manganese, play important roles in many biological processes. Other heavy metals, like lead, arsenic, cadmium, and mercury, can displace essential heavy metals and disrupt vital biological processes. Heavy metal exposure can be particularly detrimental to pregnant women and their developing fetuses; however, little is known about the combinatory impact that simultaneous exposure to multiple heavy metals may have on fetal development. Procuring a better understanding of how these metals influence fetal development is a critical first step to addressing this concerning lack of knowledge. There are numerous databases and datasets in existence storing data on heavy metal levels in maternal and fetal blood, and novel studies are being done to expand this collection of data. Data mining techniques present a tool that could be used to close this gap of knowledge by revealing patterns in data not previously discovered. Research at Thomas Jefferson University aims to aid in closing this gap of knowledge. In the study, data such as heavy metal blood concentrations, pregnancy complications, health outcomes, and demographic information will be collected from mothers and newborns. Data mining strategies will then be used to develop models capable of discovering data patterns. If this modeling is successful, such an approach can be utilized by healthcare providers in the future to assess patient risk and provide early intervention for at-risk pregnant patients.
Pages: 7 to 11
Copyright: Copyright (c) IARIA, 2025
Publication date: March 9, 2025
Published in: conference
ISBN: 978-1-68558-247-0
Location: Lisbon, Portugal
Dates: from March 9, 2025 to March 13, 2025