Project Abstract

Understanding the use of medications during pregnancy is essential to ensure the health and well-being of both the mother and the baby. With growing concerns about medication safety during pregnancy, there is an urgent need for robust data science tools to analyze and interpret valuable insights into medication patterns, potential risks, and outcomes associated with medication use during pregnancy. Addressing this need can significantly impact public health by guiding healthcare providers, policymakers, and expectant mothers in making informed decisions about medication use during pregnancy.This research offers a unique perspective by focusing on data science tools specifically tailored for analyzing the use of medications during pregnancy. The main aim of this study is to answer the research question: “What are the outcomes of specific medications on mothers and children? What are the advantages and disadvantages of these medications? What are the consequences for children after birth, and what occurs in different stages following birth?”The basic design of the study involves analyzing data from the SAIL DATA BANK. Machine learning algorithms and statistical methods are employed as the primary approach to analyze the data and identify patterns, correlations, and potential risks related to medication use during pregnancy.Key findings from the analysis reveal significant patterns in medication use during pregnancy, identifying commonly prescribed medications, potential drug interactions, and associated health outcomes. Insights gained from the data science tools include understanding the frequency of medication use, potential risks to fetal development, and recommendations for safer medication alternatives during pregnancy.This research contributes to the existing body of knowledge by providing a novel approach to analyzing the use of medications during pregnancy using data science tools. This will enhance our understanding of medication safety during pregnancy, empower healthcare decision-making, and ultimately improve maternal and child health outcomes.

Keywords: Data Science, Machine Learning Algorithm, Pregnancy and Medicine

 Conference Details

Session: Presentation Stream 19 at Presentation Slot 10

Location: GH018 at Wednesday 8th 09:00 – 12:30

Markers: Betsy Dayana Marcela Chaparro Rico, Jens Blanck

Course: ROA Micro-credentials Undergraduate Professional Learner PART TIME, Masters PG

Future Plans: I’m looking for an industry placement