儿童自身免疫疾病中的机器学习和人工智能:应用,挑战,未来的前景.
Parniyan Sadeghi1,2, Hanie Karimi1,3, Atiye Lavafian1,4
1Network of Interdisciplinarity in Neonates and Infants (NINI), Universal Scientific Education and Research Network (USERN), Tehran, Iran.
Expert review of clinical immunology
|May 21, 2024
概括
机器学习有助于诊断和管理儿科自身免疫性疾病,提供更高的精度和个性化治疗. 这项技术有助于识别新的生物标志物和治疗点,以改善患者的治疗结果.
科学领域:
- 儿童自身免疫性疾病
- 医疗信息学医学信息学
- 机器学习应用程序 机器学习应用程序
背景情况:
- 自身免疫性疾病影响4.5%~9.4%的儿童,影响他们的生活质量.
- 由于疾病呈现的变化,诊断和预后具有挑战性.
- 机器学习 (ML) 提供从大型数据集中的模式识别,以改善患者管理.
研究的目的:
- 审查关于儿童自身免疫疾病中ML应用的当前知识.
- 识别ML现有的研究和应用中的差距.
- 探索ML在儿科自身免疫护理中的变革潜力.
主要方法:
- 叙事审查方法.
- 在PubMed,Scopus和Web of Science中进行了广泛的文献搜索.
- 专注于儿童自身免疫和相关疾病中的ML应用.
主要成果:
- 机器学习算法可以提高儿科自身免疫疾病的诊断准确度和速度.
- ML有助于识别新生物标志物和治疗点.
- 可以使用ML驱动的分析开发个性化治疗策略.
结论:
- ML具有显著的潜力,可以彻底改变儿童自身免疫疾病的识别,治疗和管理.
- 医生可以利用ML进行更精确的临床判断和量身定制的患者护理.
- 需要进一步的研究,才能将ML完全纳入儿科自身免疫疾病管理中.
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