Background: Many thousands of patients with a suspected Mendelian disease have their exomes/genomes sequenced every year, but only about 30% receive a definitive diagnosis. Since a novel Mendelian gene-disease association is published on average every business day, thousands of undiagnosed patient cases could receive a diagnosis each year if their genomes were regularly compared to the latest literature. With millions of genomes expected to be sequenced for rare disease analysis by 2025, and considering the current publication rate of 1.1 million new articles per annum in PubMed, manually reanalyzing the growing cases of undiagnosed patients is not sustainable. Methods: We describe a fully automated reanalysis framework for patients with suspected, but undiagnosed, Mendelian disorders. The presented framework was tested by automatically parsing all ~100,000 newly published peer reviewed papers every month and matching them on genotype and phenotype with all stored undiagnosed patients. If a new article contains a possible diagnosis for an undiagnosed patient, the system provides notification. We test the accuracy of the automatic reanalysis system on 110 patients, including 61 with available trio data. Results: Even when trained only on older data, our system identifies 80% of reanalysis diagnoses, while sending only 0.5-1 alerts per patient per year, a 100-1,000-fold efficiency gain over manual literature surveillance of equivalent yield. Conclusion: We show that automatic reanalysis of patients with suspected Mendelian disease is feasible and has the potential to greatly streamline diagnosis. Our system is not intended to replace clinical judgment. Rather, clinical diagnostic services could greatly benefit from a modest re-allocation of time from manual literature exploration to review of automated reanalysis alerts. Our system additionally supports a new paradigm for medical IT systems: proactive, continuously learning and consequently able to autonomously identify valuable insights as they emerge in digital health records. We have launched automated patient reanalysis, trained on the latest data, with user accounts and daily literature updates at https://AMELIE.stanford.edu.

中文翻译：

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AMELIE 3：全自动孟德尔患者再分析，每位患者每年不足1次警报

背景：每年有成千上万名怀疑患有孟德尔病的患者对其外显子组/基因组进行测序，但只有约30％的患者得到了明确的诊断。由于平均每个工作日都会发布一种新颖的孟德尔基因-疾病关联，因此，如果定期将其基因组与最新文献进行比较，则每年成千上万未经诊断的患者病例都可以得到诊断。到2025年，预计将对成千上万的基因组进行测序，以进行罕见疾病分析，并考虑到PubMed中每年每年有110万新文章的发表率，手动重新分析不断增长的未确诊患者的病例是不可持续的。方法：我们为患有可疑但未诊断的孟德尔疾病的患者描述了一个全自动的重新分析框架。通过每月自动解析约100,000篇新发表的同行评审论文，并将它们的基因型和表型与所有未诊断的患者进行匹配，对提出的框架进行了测试。如果新文章包含对未诊断患者的可能诊断，则系统会提供通知。我们在110名患者中测试了自动再分析系统的准确性，其中包括61名具有可用三重数据的患者。结果：即使仅对较旧的数据进行培训，我们的系统也可以识别80％的重新分析诊断，而每位患者每年仅发送0.5-1条警报，与同等产量的人工文献监视相比，效率提高了100-1,000倍。结论：我们表明，对怀疑的孟德尔病患者进行自动重新分析是可行的，并且可能大大简化诊断。我们的系统无意取代临床判断。相反，临床诊断服务可以从适时的重新分配时间（从手动文献探索到自动重新分析警报的审核）中受益匪浅。我们的系统还支持用于医疗IT系统的新范例：主动，不断学习，因此能够自动识别数字健康记录中出现的有价值的见解。我们已经启动了自动的患者再分析功能，接受了最新数据的培训，并通过https://AMELIE.stanford.edu提供了用户帐户和每日文献更新。我们的系统还支持医疗IT系统的新范例：主动，不断学习，因此能够自动识别数字健康记录中出现的有价值的见解。我们已经启动了自动的患者重新分析，接受了最新数据的培训，并通过https://AMELIE.stanford.edu提供了用户帐户和每日文献更新。我们的系统还支持医疗IT系统的新范例：主动，不断学习，并因此能够在数字健康记录中出现时自动识别出有价值的见解。我们已经启动了自动的患者重新分析，接受了最新数据的培训，并通过https://AMELIE.stanford.edu提供了用户帐户和每日文献更新。