机器学习在新生儿呼吸窘迫综合征早期预警中的研究现状与展望
Research Status and Prospects of Machine Learning in Early Warning of Neonatal Respiratory Distress Syndrome
摘要: 新生儿呼吸窘迫综合征(NRDS)是威胁早产儿生命健康的重大疾病，传统诊断方法存在明显局限性。随着医疗信息化的发展，电子病历系统积累了大量的临床数据，机器学习技术为NRDS的早期预警提供了新的解决方案。本文系统分析了机器学习在NRDS早期预警中的研究价值及临床应用，探讨了其在推动精准医学发展和探究发病机制方面的潜力。同时，深入剖析了当前研究面临的数据质量、模型可解释性、泛化能力及临床整合等关键问题，并为该领域的未来研究方向提供了建设性展望。
Abstract: Neonatal Respiratory Distress Syndrome (NRDS) is a major life-threatening condition in preterm infants, with traditional diagnostic methods exhibiting significant limitations. With the advancement of medical informatization, electronic health record systems have accumulated vast amounts of clinical data, enabling machine learning technologies to offer novel solutions for early warning of NRDS. This paper systematically analyzes the research value and clinical applications of machine learning in NRDS early warning, exploring its potential in advancing precision medicine and elucidating disease mechanisms. It also delves into critical challenges facing current research, including data quality, model interpretability, generalization capability, and clinical integration, while offering constructive perspectives for future research directions in this field.
文章引用：杨兰, 郝莉霞. 机器学习在新生儿呼吸窘迫综合征早期预警中的研究现状与展望[J]. 临床医学进展, 2025, 15(11): 2226-2231. https://doi.org/10.12677/acm.2025.15113340

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