Petra Grđan Stevanović, Nina Barišić, Iva Šunić, Ann-Marie Malby Schoos, Branka Bunoza, Ruža Grizelj, Ana Bogdanić, Ivan Jovanović, Mario Lovrić
{"title":"机器学习识别贝利结果的关键预测因子:早产儿队列研究","authors":"Petra Grđan Stevanović, Nina Barišić, Iva Šunić, Ann-Marie Malby Schoos, Branka Bunoza, Ruža Grizelj, Ana Bogdanić, Ivan Jovanović, Mario Lovrić","doi":"10.3390/jpm14090922","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>The aim of this study was to understand how neurological development of preterm infants can be predicted at earlier stages and explore the possibility of applying personalized approaches.</p><p><strong>Methods: </strong>Our study included a cohort of 64 preterm infants, between 24 and 34 weeks of gestation. Linear and nonlinear models were used to evaluate feature predictability to Bayley outcomes at the corrected age of 2 years. The outcomes were classified into motor, language, cognitive, and socio-emotional categories. Pediatricians' opinions about the predictability of the same features were compared with machine learning.</p><p><strong>Results: </strong>According to our linear analysis sepsis, brain MRI findings and Apgar score at 5th minute were predictive for cognitive, Amiel-Tison neurological assessment at 12 months of corrected age for motor, while sepsis was predictive for socio-emotional outcome. None of the features were predictive for language outcome. Based on the machine learning analysis, sepsis was the key predictor for cognitive and motor outcome. For language outcome, gestational age, duration of hospitalization, and Apgar score at 5th minute were predictive, while for socio-emotional, gestational age, sepsis, and duration of hospitalization were predictive. Pediatricians' opinions were that cardiopulmonary resuscitation is the key predictor for cognitive, motor, and socio-emotional, but gestational age for language outcome.</p><p><strong>Conclusions: </strong>The application of machine learning in predicting neurodevelopmental outcomes of preterm infants represents a significant advancement in neonatal care. The integration of machine learning models with clinical workflows requires ongoing education and collaboration between data scientists and healthcare professionals to ensure the models' practical applicability and interpretability.</p>","PeriodicalId":16722,"journal":{"name":"Journal of Personalized Medicine","volume":null,"pages":null},"PeriodicalIF":3.0000,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11433372/pdf/","citationCount":"0","resultStr":"{\"title\":\"Machine Learning for the Identification of Key Predictors to Bayley Outcomes: A Preterm Cohort Study.\",\"authors\":\"Petra Grđan Stevanović, Nina Barišić, Iva Šunić, Ann-Marie Malby Schoos, Branka Bunoza, Ruža Grizelj, Ana Bogdanić, Ivan Jovanović, Mario Lovrić\",\"doi\":\"10.3390/jpm14090922\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>The aim of this study was to understand how neurological development of preterm infants can be predicted at earlier stages and explore the possibility of applying personalized approaches.</p><p><strong>Methods: </strong>Our study included a cohort of 64 preterm infants, between 24 and 34 weeks of gestation. Linear and nonlinear models were used to evaluate feature predictability to Bayley outcomes at the corrected age of 2 years. The outcomes were classified into motor, language, cognitive, and socio-emotional categories. Pediatricians' opinions about the predictability of the same features were compared with machine learning.</p><p><strong>Results: </strong>According to our linear analysis sepsis, brain MRI findings and Apgar score at 5th minute were predictive for cognitive, Amiel-Tison neurological assessment at 12 months of corrected age for motor, while sepsis was predictive for socio-emotional outcome. None of the features were predictive for language outcome. Based on the machine learning analysis, sepsis was the key predictor for cognitive and motor outcome. For language outcome, gestational age, duration of hospitalization, and Apgar score at 5th minute were predictive, while for socio-emotional, gestational age, sepsis, and duration of hospitalization were predictive. Pediatricians' opinions were that cardiopulmonary resuscitation is the key predictor for cognitive, motor, and socio-emotional, but gestational age for language outcome.</p><p><strong>Conclusions: </strong>The application of machine learning in predicting neurodevelopmental outcomes of preterm infants represents a significant advancement in neonatal care. The integration of machine learning models with clinical workflows requires ongoing education and collaboration between data scientists and healthcare professionals to ensure the models' practical applicability and interpretability.</p>\",\"PeriodicalId\":16722,\"journal\":{\"name\":\"Journal of Personalized Medicine\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.0000,\"publicationDate\":\"2024-08-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11433372/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Personalized Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.3390/jpm14090922\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"HEALTH CARE SCIENCES & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Personalized Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3390/jpm14090922","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
Machine Learning for the Identification of Key Predictors to Bayley Outcomes: A Preterm Cohort Study.
Background: The aim of this study was to understand how neurological development of preterm infants can be predicted at earlier stages and explore the possibility of applying personalized approaches.
Methods: Our study included a cohort of 64 preterm infants, between 24 and 34 weeks of gestation. Linear and nonlinear models were used to evaluate feature predictability to Bayley outcomes at the corrected age of 2 years. The outcomes were classified into motor, language, cognitive, and socio-emotional categories. Pediatricians' opinions about the predictability of the same features were compared with machine learning.
Results: According to our linear analysis sepsis, brain MRI findings and Apgar score at 5th minute were predictive for cognitive, Amiel-Tison neurological assessment at 12 months of corrected age for motor, while sepsis was predictive for socio-emotional outcome. None of the features were predictive for language outcome. Based on the machine learning analysis, sepsis was the key predictor for cognitive and motor outcome. For language outcome, gestational age, duration of hospitalization, and Apgar score at 5th minute were predictive, while for socio-emotional, gestational age, sepsis, and duration of hospitalization were predictive. Pediatricians' opinions were that cardiopulmonary resuscitation is the key predictor for cognitive, motor, and socio-emotional, but gestational age for language outcome.
Conclusions: The application of machine learning in predicting neurodevelopmental outcomes of preterm infants represents a significant advancement in neonatal care. The integration of machine learning models with clinical workflows requires ongoing education and collaboration between data scientists and healthcare professionals to ensure the models' practical applicability and interpretability.
期刊介绍:
Journal of Personalized Medicine (JPM; ISSN 2075-4426) is an international, open access journal aimed at bringing all aspects of personalized medicine to one platform. JPM publishes cutting edge, innovative preclinical and translational scientific research and technologies related to personalized medicine (e.g., pharmacogenomics/proteomics, systems biology). JPM recognizes that personalized medicine—the assessment of genetic, environmental and host factors that cause variability of individuals—is a challenging, transdisciplinary topic that requires discussions from a range of experts. For a comprehensive perspective of personalized medicine, JPM aims to integrate expertise from the molecular and translational sciences, therapeutics and diagnostics, as well as discussions of regulatory, social, ethical and policy aspects. We provide a forum to bring together academic and clinical researchers, biotechnology, diagnostic and pharmaceutical companies, health professionals, regulatory and ethical experts, and government and regulatory authorities.