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使用数据驱动的方法,在临近期的早产婴儿中划定病率模式
Octavia-Andreea Ciora1, Tanja Seegmüller2, Johannes S Fischer3
1Fraunhofer Institute for Cognitive Systems IKS, Munich, Germany. octavia.ciora@iks.fraunhofer.de.
BMC pediatrics
|April 11, 2024
概括
这项研究揭示了早产婴儿的复杂发病概况,确定了超出对对关联的共同模式和风险因素. 机器学习识别了具有相似并发条件的不同婴儿子组,帮助个性化监测策略.
科学领域:
- 新生儿科学 新生儿科学
- 儿科重症监护 儿科重症监护
- 围产儿医学 围产儿医学
背景情况:
- 过早分娩的生存率与心肺呼吸系统和中枢神经系统的疾病有关.
- 现有的婴儿疾病研究仅限于对联的关联,缺少整体的共同发生模式.
- 了解全面的疾病概况对于非常早产婴儿的长期结果至关重要.
研究的目的:
- 确定和表征在近期的早产婴儿的综合性疾病概况.
- 为了调查包括支气管肺功能失调 (BPD),肺高血压 (PH),心脏缺陷,大脑病理和早产视网膜病变 (ROP) 在内的流行疾病.
- 通过数据驱动方法和机器学习来识别共享的发病率模式和风险概况.
主要方法:
- 对两个独立的前性队列 (AIRR和NEuroSIS) 的分析,共计530名非常早产婴儿.
- 量化对对发病率相关性和评估BPD的歧视力.
- 机器学习的应用,以识别具有相似疾病概况的婴儿子组.
主要成果:
- 支气管肺功能障碍症 (BPD) 和早产视网膜病变 (ROP) 显示了最高的对对相关性,其次是PH和心脏缺陷的BPD.
- 在区分总体发病率发生方面,BPD表现出有限的能力.
- 机器学习识别了具有共同发病率模式的不同患者集群 (在AIRR中6个,在NEuroSIS中8个).
结论:
- 该研究提供了早产婴儿发病概况在出院时的全面描述,与共享的病理生理学相关.
- 识别出患病率模式和患者子组可以为个性化监测策略的开发提供信息.
- 未来的研究应该集中在对早产人口量身定制干预措施的风险概况的完善上.
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