基于表型和内型的学前喘息治疗
Sormeh Salehian1,2, Louise Fleming1,2, Sejal Saglani1,2
1National Heart and Lung Institute, Imperial College London, London, UK.
Expert review of respiratory medicine
|October 24, 2023
概括
学前喘息 (PSW) 是一个复杂的疾病,其潜在机制各不相同. 机器学习可以识别不同的表型,以指导个性化治疗,并改善持续喘息的儿童的治疗结果.
科学领域:
- 儿科呼吸系统医学 儿科呼吸系统医学
- 计算生物学 计算生物学
- 精准医学是一门精准的医学.
背景情况:
- 学前喘 (PSW) 带来了重大的公共卫生挑战,包括频繁的急诊室访问和严重的恶化.
- 目前对PSW的治疗方法是普遍的,往往不反映这种异质性疾病的各种潜在的病理生物学机制.
- 对PSW的基于症状的标签经常无法捕捉不同的疾病途径,限制了针对性的治疗方法.
研究的目的:
- 审查PSW表型和潜在内型的可观察特征和病理生物学证据.
- 探索机器学习 (ML) 在PSW中识别疾病模式中的作用.
- 将已识别的表型与治疗选择和未来研究方向联系起来.
主要方法:
- 对临床特征和与学前喘息表型相关的病理生物学数据的审查.
- 在复杂的儿科呼吸系统数据中探索用于模式识别的机器学习方法.
- 在已识别的PSW表型的背景下分析现有治疗策略.
主要成果:
- 严重PSW的不同集群 (表型) 呈现出独特和共享的潜在病理生物学机制.
- 对临床,生物标志物和环境数据应用的ML可以区分持久性与解决PSW.
- 识别驱动喘息持续和解决的机制是通过ML促进的.
结论:
- 基于ML的方法可以帮助区分PSW表型,可能揭示新的治疗点.
- 未来临床试验中的患者分层可以通过ML识别的疾病亚型来告知.
- 了解不同的PSW内型对于开发个性化治疗策略和改善长期结果至关重要.
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