{"title":"基于机器学习构建儿童重症呼吸道合胞病毒肺炎诊断预测模型。","authors":"Yuanwei Liu, Qiong Wu, Lifang Zhou, Yingyuan Tang, Fen Li, Shuangjie Li","doi":"10.1097/SHK.0000000000002472","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Severe respiratory syncytial virus (RSV) pneumonia is a leading cause of hospitalization and morbidity in infants and young children. Early identification of severe RSV pneumonia is crucial for timely and effective treatment by pediatricians. Currently, no prediction model exists for identifying severe RSV pneumonia in children.</p><p><strong>Methods: </strong>This study aimed to construct a diagnostic prediction model for severe RSV pneumonia in children using a machine learning algorithm. We analyzed data from the Gene Expression Omnibus (GEO) Series, including training dataset GSE246622 and testing dataset GSE105450, to identify differential genes between severe and mild-to-moderate RSV pneumonia in children. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on the differential genes, followed by the construction of a protein-protein interaction (PPI) network. An artificial neural network (ANN) algorithm was then used to develop and validate a diagnostic prediction model for severe RSV pneumonia in children.</p><p><strong>Results: </strong>We identified 34 differentially expressed genes between the severe and mild-to-moderate RSV pneumonia groups. Enrichment analysis revealed that these genes were primarily related to pathogenic infection and immune response. From the PPI network, we identified 10 hub genes and, using the random forest algorithm, screened out 20 specific genes. The ANN-based diagnostic prediction model achieved an area under the curve (AUC) value of 0.970 in the training group and 0.833 in the testing group, demonstrating the model's accuracy.</p><p><strong>Conclusions: </strong>This study identified specific biomarkers and developed a diagnostic model for severe RSV pneumonia in children. These findings provide a robust foundation for early identification and treatment of severe RSV pneumonia, offering new insights into its pathogenesis and improving pediatric care.</p>","PeriodicalId":21667,"journal":{"name":"SHOCK","volume":" ","pages":""},"PeriodicalIF":2.7000,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Constructing a diagnostic prediction model to estimate the severe respiratory syncytial virus pneumonia in children based on machine learning.\",\"authors\":\"Yuanwei Liu, Qiong Wu, Lifang Zhou, Yingyuan Tang, Fen Li, Shuangjie Li\",\"doi\":\"10.1097/SHK.0000000000002472\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Severe respiratory syncytial virus (RSV) pneumonia is a leading cause of hospitalization and morbidity in infants and young children. Early identification of severe RSV pneumonia is crucial for timely and effective treatment by pediatricians. Currently, no prediction model exists for identifying severe RSV pneumonia in children.</p><p><strong>Methods: </strong>This study aimed to construct a diagnostic prediction model for severe RSV pneumonia in children using a machine learning algorithm. We analyzed data from the Gene Expression Omnibus (GEO) Series, including training dataset GSE246622 and testing dataset GSE105450, to identify differential genes between severe and mild-to-moderate RSV pneumonia in children. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on the differential genes, followed by the construction of a protein-protein interaction (PPI) network. An artificial neural network (ANN) algorithm was then used to develop and validate a diagnostic prediction model for severe RSV pneumonia in children.</p><p><strong>Results: </strong>We identified 34 differentially expressed genes between the severe and mild-to-moderate RSV pneumonia groups. Enrichment analysis revealed that these genes were primarily related to pathogenic infection and immune response. From the PPI network, we identified 10 hub genes and, using the random forest algorithm, screened out 20 specific genes. The ANN-based diagnostic prediction model achieved an area under the curve (AUC) value of 0.970 in the training group and 0.833 in the testing group, demonstrating the model's accuracy.</p><p><strong>Conclusions: </strong>This study identified specific biomarkers and developed a diagnostic model for severe RSV pneumonia in children. These findings provide a robust foundation for early identification and treatment of severe RSV pneumonia, offering new insights into its pathogenesis and improving pediatric care.</p>\",\"PeriodicalId\":21667,\"journal\":{\"name\":\"SHOCK\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2024-09-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SHOCK\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1097/SHK.0000000000002472\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CRITICAL CARE MEDICINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SHOCK","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/SHK.0000000000002472","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CRITICAL CARE MEDICINE","Score":null,"Total":0}
Constructing a diagnostic prediction model to estimate the severe respiratory syncytial virus pneumonia in children based on machine learning.
Background: Severe respiratory syncytial virus (RSV) pneumonia is a leading cause of hospitalization and morbidity in infants and young children. Early identification of severe RSV pneumonia is crucial for timely and effective treatment by pediatricians. Currently, no prediction model exists for identifying severe RSV pneumonia in children.
Methods: This study aimed to construct a diagnostic prediction model for severe RSV pneumonia in children using a machine learning algorithm. We analyzed data from the Gene Expression Omnibus (GEO) Series, including training dataset GSE246622 and testing dataset GSE105450, to identify differential genes between severe and mild-to-moderate RSV pneumonia in children. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on the differential genes, followed by the construction of a protein-protein interaction (PPI) network. An artificial neural network (ANN) algorithm was then used to develop and validate a diagnostic prediction model for severe RSV pneumonia in children.
Results: We identified 34 differentially expressed genes between the severe and mild-to-moderate RSV pneumonia groups. Enrichment analysis revealed that these genes were primarily related to pathogenic infection and immune response. From the PPI network, we identified 10 hub genes and, using the random forest algorithm, screened out 20 specific genes. The ANN-based diagnostic prediction model achieved an area under the curve (AUC) value of 0.970 in the training group and 0.833 in the testing group, demonstrating the model's accuracy.
Conclusions: This study identified specific biomarkers and developed a diagnostic model for severe RSV pneumonia in children. These findings provide a robust foundation for early identification and treatment of severe RSV pneumonia, offering new insights into its pathogenesis and improving pediatric care.
期刊介绍：
SHOCK®: Injury, Inflammation, and Sepsis: Laboratory and Clinical Approaches includes studies of novel therapeutic approaches, such as immunomodulation, gene therapy, nutrition, and others. The mission of the Journal is to foster and promote multidisciplinary studies, both experimental and clinical in nature, that critically examine the etiology, mechanisms and novel therapeutics of shock-related pathophysiological conditions. Its purpose is to excel as a vehicle for timely publication in the areas of basic and clinical studies of shock, trauma, sepsis, inflammation, ischemia, and related pathobiological states, with particular emphasis on the biologic mechanisms that determine the response to such injury. Making such information available will ultimately facilitate improved care of the traumatized or septic individual.
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