Author : Yang Ren
Advisor : Dr. Dezhi Wu, IIT Dept. & Dr. Yan Tong, CSE Dept
Date : Aug 26th
Time:  9:00 am – 11: 00 am
Place : Teams

Link: https://teams.microsoft.com/dl/launcher/launcher.html?url=%2F_%23%2Fl%2…

Dial in by phone

+1 803-400-6044,,897438708# United States, Columbia
Find a local number
Phone conference ID: 897 438 708#

Abstract

          Low Birthweight (LBW) is a major public health issue resulting in increased neonatal mortality and long-term health complications. Traditional LBW analysis methods, focusing on incidence rates and risk factors through statistical models, often struggle with complex unseen data, and thus their effectiveness is limited in early prevention of LBW. As such, more advanced LBW prediction models are needed, so this dissertation delves into this important research area through proposing and examining novel machine learning (ML) and deep learning (DL) algorithms to more accurately predict LBW during the early stage of birthing individuals’ pregnancy period. This dissertation consists of three studies, which covers the following three major research topics.  

     The first topic focuses on the examination of the effectiveness and impact of various data rebalancing techniques for LBW prediction to solve extremely imbalanced data issues. Through this investigation, we established a foundational pipeline for LBW prediction, paving the way for further development and refinement in subsequent studies. This first study also included an extensive feature importance analysis to identify key factors in LBW classification, crucial to guiding targeted interventions to improve birth outcomes.
     The second topic aims to develop a novel longitudinal transformer-based LBW prediction framework, which integrates prenatal mothers’ historical health records and current pre-delivery data, making it possible to provide more comprehensive and relevant input features for LBW prediction. This framework’s ability to effectively process and analyze these diverse data inputs marks a more significant advancement than previous approaches that primarily focus on immediate pre-delivery factors. As a result, this enhanced model is proved to improve the accuracy of LBW predictions, and thus offering a more robust tool for more effective early intervention strategies.
     The third topic is to propose and examine a pioneering fusion framework that combines structured medical records with rich text-based data. This large language model (LLM)-based approach aims to explore and optimize the strengths of both quantitative and qualitative data sources, for enhancing the predictive accuracy and explainability of the LBW prediction models. By integrating diverse data types, this proposed method is expected to offer in-depth insights into the myriad factors contributing to LBW, potentially unveiling previously unrecognized and more granular risk factors to refine the prediction models further.
     In summary, this dissertation presents a comprehensive exploration of using advanced ML and DL algorithms in the prediction of LBW through a series of three studies. From establishing LBW prediction pipeline with rebalancing strategies (Study 1), developing a transformer-based approach (Study 2) to introducing a tabular-text fusion framework (Study 3), this research will contribute to a substantial advancement in prenatal care. By enabling earlier and more accurate identification of LBW risks, this work has the potential to transform prenatal intervention strategies, leading to improved health outcomes for both mothers and their infants.