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Machine Learning Methods for Pregnancy and Childbirth Risk Management
Machine learning methods enable medical systems to automatically generate data-driven decision support models using real-world data inputs, eliminating the need for explicit rule design. In this research, we investigated the application of machine learning methods in healthcare, specifically focusin...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10303735/ https://www.ncbi.nlm.nih.gov/pubmed/37373964 http://dx.doi.org/10.3390/jpm13060975 |
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author | Kopanitsa, Georgy Metsker, Oleg Kovalchuk, Sergey |
author_facet | Kopanitsa, Georgy Metsker, Oleg Kovalchuk, Sergey |
author_sort | Kopanitsa, Georgy |
collection | PubMed |
description | Machine learning methods enable medical systems to automatically generate data-driven decision support models using real-world data inputs, eliminating the need for explicit rule design. In this research, we investigated the application of machine learning methods in healthcare, specifically focusing on pregnancy and childbirth risks. The timely identification of risk factors during early pregnancy, along with risk management, mitigation, prevention, and adherence management, can significantly reduce adverse perinatal outcomes and complications for both mother and child. Given the existing burden on medical professionals, clinical decision support systems (CDSSs) can play a role in risk management. However, these systems require high-quality decision support models based on validated medical data that are also clinically interpretable. To develop models for predicting childbirth risks and due dates, we conducted a retrospective analysis of electronic health records from the perinatal Center of the Almazov Specialized Medical Center in Saint-Petersburg, Russia. The dataset, which was exported from the medical information system, consisted of structured and semi-structured data, encompassing a total of 73,115 lines for 12,989 female patients. Our proposed approach, which includes a detailed analysis of predictive model performance and interpretability, offers numerous opportunities for decision support in perinatal care provision. The high predictive performance achieved by our models ensures precise support for both individual patient care and overall health organization management. |
format | Online Article Text |
id | pubmed-10303735 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103037352023-06-29 Machine Learning Methods for Pregnancy and Childbirth Risk Management Kopanitsa, Georgy Metsker, Oleg Kovalchuk, Sergey J Pers Med Article Machine learning methods enable medical systems to automatically generate data-driven decision support models using real-world data inputs, eliminating the need for explicit rule design. In this research, we investigated the application of machine learning methods in healthcare, specifically focusing on pregnancy and childbirth risks. The timely identification of risk factors during early pregnancy, along with risk management, mitigation, prevention, and adherence management, can significantly reduce adverse perinatal outcomes and complications for both mother and child. Given the existing burden on medical professionals, clinical decision support systems (CDSSs) can play a role in risk management. However, these systems require high-quality decision support models based on validated medical data that are also clinically interpretable. To develop models for predicting childbirth risks and due dates, we conducted a retrospective analysis of electronic health records from the perinatal Center of the Almazov Specialized Medical Center in Saint-Petersburg, Russia. The dataset, which was exported from the medical information system, consisted of structured and semi-structured data, encompassing a total of 73,115 lines for 12,989 female patients. Our proposed approach, which includes a detailed analysis of predictive model performance and interpretability, offers numerous opportunities for decision support in perinatal care provision. The high predictive performance achieved by our models ensures precise support for both individual patient care and overall health organization management. MDPI 2023-06-10 /pmc/articles/PMC10303735/ /pubmed/37373964 http://dx.doi.org/10.3390/jpm13060975 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Kopanitsa, Georgy Metsker, Oleg Kovalchuk, Sergey Machine Learning Methods for Pregnancy and Childbirth Risk Management |
title | Machine Learning Methods for Pregnancy and Childbirth Risk Management |
title_full | Machine Learning Methods for Pregnancy and Childbirth Risk Management |
title_fullStr | Machine Learning Methods for Pregnancy and Childbirth Risk Management |
title_full_unstemmed | Machine Learning Methods for Pregnancy and Childbirth Risk Management |
title_short | Machine Learning Methods for Pregnancy and Childbirth Risk Management |
title_sort | machine learning methods for pregnancy and childbirth risk management |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10303735/ https://www.ncbi.nlm.nih.gov/pubmed/37373964 http://dx.doi.org/10.3390/jpm13060975 |
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