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ORIGINAL RESEARCH article

Front. Med.
Sec. Pulmonary Medicine
Volume 11 - 2024 | doi: 10.3389/fmed.2024.1481830
This article is part of the Research Topic Advances in Lung Ultrasound: From Child to Adulthood Diseases - Volume II View all articles

A study of machine learning to predict NRDS severity based on lung ultrasound score and clinical indicators

Provisionally accepted
Chunyan Huang Chunyan Huang 1,2Xiaoming Ha Xiaoming Ha 1Yanfang Cui Yanfang Cui 1Hongxia Zhang Hongxia Zhang 1*
  • 1 Yantaishan Hospital, Yantai, China
  • 2 Binzhou Medical University, Binzhou, Shandong Province, China

The final, formatted version of the article will be published soon.

    Objective: To develop predictive models for neonatal respiratory distress syndrome (NRDS) using machine learning algorithms to improve the accuracy of severity predictions. Methods: This double-blind cohort study included 230 neonates admitted to the neonatal intensive care unit (NICU) of Yantaishan Hospital between December 2020 and June 2023. Of these, 119 neonates were diagnosed with NRDS and placed in the NRDS group, while 111 neonates with other conditions formed the non-NRDS (N-NRDS) group. All neonates underwent lung ultrasound and various clinical assessments, with data collected on the oxygenation index (OI), sequential organ failure assessment (SOFA), respiratory index (RI), and lung ultrasound score (LUS). An independent sample test was used to compare the groups' LUS, OI, RI, SOFA scores, and clinical data. Use Least Absolute Shrinkage and Selection Operator (LASSO) regression to identify predictor variables, and construct a model for predicting NRDS severity using logistic regression (LR), random forest (RF), artificial neural network (NN), and support vector machine (SVM) algorithms. The importance of predictive variables and performance metrics was evaluated for each model. Results: The NRDS group showed significantly higher LUS, SOFA, and RI scores and lower OI values than the N-NRDS group (P<0.01). LUS, SOFA, and RI scores were significantly higher in the severe NRDS group compared to the mild and moderate groups, while OI was markedly lower (P<0.01). LUS, OI, RI, and SOFA scores were the most impactful variables for the predictive efficacy of the models. The RF model performed best of the four models, with an AUC of 0.894, accuracy of 0.808, and sensitivity of 0.706. In contrast, the LR, NN, and SVM models have lower AUC values than the RF model with 0.841, 0.828, and 0.726, respectively. Conclusion: Four predictive models based on machine learning can accurately assess the severity of NRDS. Among them, the RF model exhibits the best predictive performance, offering more effective support for the treatment and care of neonates.

    Keywords: NRDS, machine learning, risk factor, Clinical indicator, Prediction model

    Received: 16 Aug 2024; Accepted: 18 Oct 2024.

    Copyright: © 2024 Huang, Ha, Cui and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Hongxia Zhang, Yantaishan Hospital, Yantai, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.