PDF(Pubmed)
Abstract：
Bronchopulmonary dysplasia (BPD) is a complex, multifactorial lung disease affecting preterm neonates that can result in long-term pulmonary and non-pulmonary complications. Current therapies mainly focus on symptom management after the development of BPD, indicating a need for innovative approaches to predict and identify neonates who would benefit most from targeted or earlier interventions. Clinical informatics, a subfield of biomedical informatics, is transforming healthcare by integrating computational methods with patient data to improve patient outcomes. The application of clinical informatics to develop and enhance clinical therapies for BPD presents opportunities by leveraging electronic health record data, applying machine learning algorithms, and implementing clinical decision support systems. This review highlights the current barriers and the future potential of clinical informatics in identifying clinically relevant BPD phenotypes and developing clinical decision support tools to improve the management of extremely preterm neonates developing or with established BPD. However, the full potential of clinical informatics in advancing our understanding of BPD with the goal of improving patient outcomes cannot be achieved unless we address current challenges such as data collection, storage, privacy, and inherent data bias.
摘要：
支气管肺发育不良（BPD）是一个复杂的，影响早产新生儿的多因素肺部疾病，可导致长期肺部和非肺部并发症。目前的治疗主要集中在BPD发展后的症状管理，这表明需要创新的方法来预测和确定从有针对性或早期干预措施中获益最大的新生儿。临床信息学,生物医学信息学的一个子领域，正在通过将计算方法与患者数据集成来改善患者预后，从而改变医疗保健。通过利用电子健康记录数据，临床信息学在开发和增强BPD临床治疗方面的应用提供了机会。应用机器学习算法，实施临床决策支持系统。这篇综述强调了临床信息学在识别临床相关BPD表型和开发临床决策支持工具以改善发育或已建立BPD的极度早产新生儿的管理方面的当前障碍和未来潜力。然而,除非我们解决当前的挑战，如数据收集，storage,隐私,和固有的数据偏差。
