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通过使用健康记录数据和机器学习,减少急性肝脏病的诊断延迟

Balu Bhasuran1, Katharina Schmolly2, Yuvraaj Kapoor3

  • 1Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA 94143, United States.

Journal of the American Medical Informatics Association : JAMIA
|July 1, 2024
PubMed
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,改善患者的治疗结果,并可能在诊断过程中节省大量时间.

关键词:
急性肝脏形性形症电子健康记录数据 电子健康记录数据知识图是知识图.机器学习是机器学习.罕见疾病预测 罕见疾病预测

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