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通过使用电子健康记录数据和机器学习减少急性肝病的诊断延迟:一个多中心开发和验证研究

Balu Bhasuran¹, Katharina Schmolly², Yuvraaj Kapoor³

¹Bakar Computational Health Sciences Institute, San Francisco, CA, 94143.

medRxiv : the preprint server for health sciences

|September 11, 2023

概括

机器学习模型在减少罕见疾病急性肝孔病 (AHP) 的诊断延迟方面表现有希望. 这些模型可以更早地识别潜在的AHP病例,显著缩短诊断时间表.

科学领域:

医疗信息学医学信息学
罕见疾病诊断罕见疾病诊断
机器学习在医疗保健中的应用

背景情况:

急性肝孔病 (AHP) 是一组罕见的,可治疗的疾病,通常会出现显著的诊断延迟,平均为15年.
电子健康记录 (EHR) 和机器学习 (ML) 为早期识别罕见疾病 (如AHP) 提供了潜力.
培训预测模型面临的挑战包括有限的病例数量,非结构化的EHR数据和医疗保健提供偏见.

结论:

机器学习有可能显著减少AHP和其他罕见疾病的诊断延迟.
在临床部署之前,这些ML模型的验证需要强大的招聘策略和多中心合作.
通过ML早期识别可以改善罕见,可治疗的疾病 (如AHP) 的患者结果.

关键词:

预测罕见疾病的预测.严重的肝脏形性形症急性间歇性形性形症急性间歇性形症人工智能的人工智能是人工智能.自动化诊断自动化诊断有关因果关系的模型.数据科学数据科学定向非循环图是指向的非循环图.知识图是知识图.机器学习是机器学习.自然语言处理自然语言处理.这是一个前性研究.

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