Machine Learning Can Speed Diagnosis of Genetic Diseases in Children


2019-04-26 03:26:09 Drugs


A machine learning process and clinical natural language processing can rapidly diagnose rare genetic diseases, according to a study published in the April 24 issue of Science Translational Medicine. Michelle M. Clark, Ph.D., from the Rady Children's Institute for Genomic Medicine in San Diego, and colleagues describe a platform for genetic disease diagnosis using automated phenotyping and interpretation. Bead-based genome libraries were prepared directly from blood samples, and paired 100-nt reads were sequenced in 15.5 hours. Children's deep phenomes were automatically extracted from electronic health records with CNLP. The researchers found that a mean of 4.3 CNLP-extracted phenotypic features matched the expected phenotypic features of 105 genetic diseases in 101 children; using manual interpretation, 0.9 phenotypic features matched. By combining the ranking of the similarity of a patient's CNLP phenome with respect to the expected phenotypic features of all genetic diseases, together with the ranking of the pathogenicity of all the patient's genomic variants, provisional diagnosis was automated. There was good concurrence for automated, retrospective diagnoses with expert manual interpretation (97 percent recall and 99 percent precision in 95 children with 97 genetic diseases). The platform correctly diagnosed three of seven seriously ill children in the intensive care unit, with 100 percent precision and recall, saving 22.19 hours. The diagnosis affected treatment in each case. "By informing timely targeted treatments, rapid genome sequencing can improve the outcomes of seriously ill children with genetic diseases," Clark said in a statement. Several authors disclosed financial ties to the biopharmaceutical industry. One author has filed a study-related patent. Abstract/Full Text (subscription or payment may be required) Posted: April 2019
4月24日发表在《科学转化医学》杂志上的一项研究表明,机器学习过程和临床自然语言处理可以快速诊断罕见的遗传疾病。 圣地亚哥 Rady 儿童基因组医学研究所的 Michelle M . Clark 博士及其同事描述了一个利用自动表型和解释进行遗传疾病诊断的平台。直接从血液样本中提取基于 Bead 的基因组文库,并在15.5小时内对100位基因进行配对。儿童的深层表型被自动从电子健康记录与 CNLP 。 研究人员发现,在101名儿童中,平均4.3个 CNLP 提取的表型特征与105种遗传病的预期表型特征相匹配;使用人工解释,0.9个表型特征相匹配。通过结合患者 CNLP 表型与所有遗传病的预期表型特征的相似性排名,以及所有患者基因变体的致病性排名,临时诊断实现自动化。对于自动的、回顾性的诊断,与专家手工解释有很好的一致性(97%的召回和99%的准确率,95名患有97种遗传病的儿童)。该平台准确诊断了重症监护病房七名重病患儿中的三名,具有100%的准确率和召回率,节省了22.19小时。这一诊断影响了每一个病例的治疗。 克拉克在一份声明中说:“通过及时告知有针对性的治疗,快速的基因组测序可以改善患有遗传病的严重患儿的预后。” 几位作者披露了与生物制药行业的财务关系。一位作者提出了一项与研究相关的专利。 文摘/全文(可能需要订阅或付费) 张贴日期:2019年4月