Maternal Health Risk Assessment using Digital Twin Application

Pregnancy in a life of a woman, is an important time that is connected with both physiological and psychological changes. This paper aims at developing a digital twin application that allows to assess mother's health risk and help to diagnose them. The system presented in this paper includes mo...

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Bibliographic Details
Main Authors: Leszczelowska, Paulina, Mazur-Milecka, Magdalena, Kowalczyk, Natalia, Sobotka, Milena
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
digital twin
gestational diabetes mellitus
health risk assessment
machine learning
maternal
Medical services
Physiology
Predictive models
preeclampsia
Pregnancy
Psychology
Training
Online Access:Request full text
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Summary:Pregnancy in a life of a woman, is an important time that is connected with both physiological and psychological changes. This paper aims at developing a digital twin application that allows to assess mother's health risk and help to diagnose them. The system presented in this paper includes models for three health outcomes: maternal health risk level, diagnosis of gestational diabetes mellitus (GDM), and diagnosis of late onset preeclampsia. The system included an examination of a data generation method. The model destined to assess the risk level achieved an accuracy of 83.5%. GDM model obtained a high precision of 97.2%. The analysis of preeclampsia data generation has shown a great potential for future use. The developed digital twin application serves to exhibit the mentioned models and offer an insight into the future diagnostic tool designed for maternal healthcare in the coming years.
ISSN:2158-2254
DOI:10.1109/HSI61632.2024.10613547