Patients with rare diseases often experience prolonged diagnostic delays. Ordering appropriate genetic tests is crucial yet challenging, especially for general pediatricians without genetic expertise. Recent American College of Medical Genetics (ACMG) guidelines embrace early use of exome sequencing (ES) or genome sequencing (GS) for conditions like congenital anomalies or developmental delays while still recommend gene panels for patients exhibiting strong manifestations of a specific disease. Recognizing the difficulty in navigating these options, we developed a machine learning model trained on 1005 patient records from Columbia University Irving Medical Center to recommend appropriate genetic tests based on the phenotype information. The model achieved a remarkable performance with an AUROC of 0.823 and AUPRC of 0.918, aligning closely with decisions made by genetic specialists, and demonstrated strong generalizability (AUROC:0.77, AUPRC: 0.816) in an external cohort, indicating its potential value for general pediatricians to expedite rare disease diagnosis by enhancing genetic test ordering.

罕见病患者通常会经历较长的诊断延迟。订购适当的基因检测至关重要，但也具有挑战性，尤其是对于没有遗传专业知识的普通儿科医生。最近的美国医学遗传学学会 （ACMG） 指南支持早期使用外显子组测序 （ES） 或基因组测序 （GS） 治疗先天性异常或发育迟缓等疾病，同时仍然建议表现出特定疾病强烈表现的患者使用基因检测。认识到浏览这些选项的困难，我们开发了一个机器学习模型，该模型在哥伦比亚大学欧文医学中心的 1005 份患者记录上进行了训练，以根据表型信息推荐适当的基因测试。该模型取得了显着的性能，AUROC 为 0.823，AUPRC 为 0.918，与遗传专家的决策密切相关，并在外部队列中表现出很强的泛化性 （AUROC：0.77， AUPRC： 0.816），表明其对普通儿科医生通过加强基因检测排序来加快罕见病诊断的潜在价值。