Improving Neonatal Care with AI: Class Weight Optimization for Respiratory Distress Syndrome Prediction in Very Low Birth Weight Infants

Woocheol Jang; Jinseok Lee

doi:10.1109/EMBC53108.2024.10782854

Improving Neonatal Care with AI: Class Weight Optimization for Respiratory Distress Syndrome Prediction in Very Low Birth Weight Infants

Woocheol Jang
, Jinseok Lee

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

In this study, we developed an AI model to predict Respiratory Distress Syndrome (RDS) in premature infants, aiming to reduce unnecessary treatment with artificial pulmonary surfactant. We analyzed data from 13,120 infants in 76 hospitals, considering various factors including infant information, maternity details, birth process, family background, resuscitation, and lab results. seven machine learning algorithms were compared, with Support Vector Machine (SVM) showing the highest accuracy. We further improved prediction performance with a 5-layer Deep Neural Network (DNN) using selected features from SVM-based analysis. To address imbalanced data, we employed ensemble methods and class weight optimization. The final model achieved exceptional results on an independent test dataset, with a specificity of 87.36%, sensitivity of 90.65%, balanced accuracy of 89.01%, and an AUC of 0.9612, surpassing other models.

Original language	English
Title of host publication	46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350371499
DOIs	https://doi.org/10.1109/EMBC53108.2024.10782854
Publication status	Published - 2024
Event	46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024 - Orlando, United States Duration: 15 Jul 2024 → 19 Jul 2024

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISSN (Print)	1557-170X

Conference

Conference	46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024
Country/Territory	United States
City	Orlando
Period	15/07/24 → 19/07/24

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

Keywords

artificial intelligence
class weight
deep neural network
ensemble
machine learning
premature infants
respiratory distress syndrome

Access to Document

10.1109/EMBC53108.2024.10782854

Cite this

Jang, W., & Lee, J. (2024). Improving Neonatal Care with AI: Class Weight Optimization for Respiratory Distress Syndrome Prediction in Very Low Birth Weight Infants. In 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024 - Proceedings (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC53108.2024.10782854

Jang, Woocheol ; Lee, Jinseok. / Improving Neonatal Care with AI : Class Weight Optimization for Respiratory Distress Syndrome Prediction in Very Low Birth Weight Infants. 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2024. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

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title = "Improving Neonatal Care with AI: Class Weight Optimization for Respiratory Distress Syndrome Prediction in Very Low Birth Weight Infants",

abstract = "In this study, we developed an AI model to predict Respiratory Distress Syndrome (RDS) in premature infants, aiming to reduce unnecessary treatment with artificial pulmonary surfactant. We analyzed data from 13,120 infants in 76 hospitals, considering various factors including infant information, maternity details, birth process, family background, resuscitation, and lab results. seven machine learning algorithms were compared, with Support Vector Machine (SVM) showing the highest accuracy. We further improved prediction performance with a 5-layer Deep Neural Network (DNN) using selected features from SVM-based analysis. To address imbalanced data, we employed ensemble methods and class weight optimization. The final model achieved exceptional results on an independent test dataset, with a specificity of 87.36\%, sensitivity of 90.65\%, balanced accuracy of 89.01\%, and an AUC of 0.9612, surpassing other models.",

keywords = "artificial intelligence, class weight, deep neural network, ensemble, machine learning, premature infants, respiratory distress syndrome",

author = "Woocheol Jang and Jinseok Lee",

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year = "2024",

doi = "10.1109/EMBC53108.2024.10782854",

language = "English",

series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",

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Jang, W & Lee, J 2024, Improving Neonatal Care with AI: Class Weight Optimization for Respiratory Distress Syndrome Prediction in Very Low Birth Weight Infants. in 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024 - Proceedings. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, Institute of Electrical and Electronics Engineers Inc., 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024, Orlando, United States, 15/07/24. https://doi.org/10.1109/EMBC53108.2024.10782854

Improving Neonatal Care with AI: Class Weight Optimization for Respiratory Distress Syndrome Prediction in Very Low Birth Weight Infants. / Jang, Woocheol; Lee, Jinseok.
46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2024. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Improving Neonatal Care with AI

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AU - Jang, Woocheol

AU - Lee, Jinseok

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N2 - In this study, we developed an AI model to predict Respiratory Distress Syndrome (RDS) in premature infants, aiming to reduce unnecessary treatment with artificial pulmonary surfactant. We analyzed data from 13,120 infants in 76 hospitals, considering various factors including infant information, maternity details, birth process, family background, resuscitation, and lab results. seven machine learning algorithms were compared, with Support Vector Machine (SVM) showing the highest accuracy. We further improved prediction performance with a 5-layer Deep Neural Network (DNN) using selected features from SVM-based analysis. To address imbalanced data, we employed ensemble methods and class weight optimization. The final model achieved exceptional results on an independent test dataset, with a specificity of 87.36%, sensitivity of 90.65%, balanced accuracy of 89.01%, and an AUC of 0.9612, surpassing other models.

AB - In this study, we developed an AI model to predict Respiratory Distress Syndrome (RDS) in premature infants, aiming to reduce unnecessary treatment with artificial pulmonary surfactant. We analyzed data from 13,120 infants in 76 hospitals, considering various factors including infant information, maternity details, birth process, family background, resuscitation, and lab results. seven machine learning algorithms were compared, with Support Vector Machine (SVM) showing the highest accuracy. We further improved prediction performance with a 5-layer Deep Neural Network (DNN) using selected features from SVM-based analysis. To address imbalanced data, we employed ensemble methods and class weight optimization. The final model achieved exceptional results on an independent test dataset, with a specificity of 87.36%, sensitivity of 90.65%, balanced accuracy of 89.01%, and an AUC of 0.9612, surpassing other models.

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KW - class weight

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KW - ensemble

KW - machine learning

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KW - respiratory distress syndrome

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Jang W, Lee J. Improving Neonatal Care with AI: Class Weight Optimization for Respiratory Distress Syndrome Prediction in Very Low Birth Weight Infants. In 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2024. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC53108.2024.10782854

Improving Neonatal Care with AI: Class Weight Optimization for Respiratory Distress Syndrome Prediction in Very Low Birth Weight Infants

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