Deep phenotyping in Electronic Health Records for Genomic Medicine

基因组医学电子健康记录中的深度表型分析

• 批准号: 9925808
• 负责人: CHUNHUA WENG
• 金额: $ 80万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-17 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9925808
• 关键词: Address; Adopted; Age; Area; Benchmarking; Candidate Disease Gene; Characteristics; ClinVar; Clinical; Clinical Research; Clinical effectiveness; Computer software; Data; Data Science; Data Set; Data Sources; Diagnosis; Diagnostic; Disease; Effectiveness; Electronic Health Record; Event; Genes; Genetic Diseases; Genome; Genomic medicine; Genomics; Genotype; Goals; Human; Human Genetics; Informatics; Knowledge; Knowledge Discovery; Learning; Link; Literature; Measures; Methods; Natural Language Processing; Online Mendelian Inheritance In Man; Ontology; Patients; Phenotype; Probability; Research; Resources; Software Tools; Standardization; Statistical Models; System; Terminology; Testing; Text; Translating; Universities; Variant; abstracting; base; causal variant; clinical decision support; clinical diagnostics; clinical practice; clinical sequencing; cost effectiveness; data modeling; data standards; data warehouse; design; disease diagnosis; disease phenotype; disease-causing mutation; disorder prevention; ethnic diversity; exome; exome sequencing; experience; genetic disorder diagnosis; genetic variant; health record; human disease; improved; information organization; innovation; interoperability; next generation; novel; open source; patient health information; phenotypic data; pituitary fossa; portability; precision medicine; success

PROJECT SUMMARY The overarching goal of the project is to establish a genomic medicine learning system to accelerate genomic knowledge discovery and application in electronic health records (EHRs). We will integrate deep characteristic phenotypes extracted from EHRs and evolving knowledge of genotype-phenotype associations to optimize the accuracy of variant interpretation and the cost-effectiveness of clinical genome/exome sequencing, and to accelerate the discovery of causal genes by constructing a dynamic genotype-phenotype knowledge network. Prior knowledge on phenotype-gene relationships and phenotypic information about patients can facilitate the identification of disease-causing mutations from thousands of genetic variants in the context of clinical genomic sequencing; however, how best to abstract phenotype information from notes in the EHRs of patients who are diagnosed with or evaluated for monogenetic disorders, standardize the computable representation of phenotypes, and utilize it in genomic interpretation remains unclear. Additionally, how to systematically compare phenotypes across diseases to discover new knowledge in human genetics remains a largely untapped area with great promise. To address these challenges, we will develop and validate scalable and portable open-source natural language processing (NLP) methods for automated and accurate abstraction of characteristic phenotype concepts (e.g., “j-shaped sella turcica” and “short stature”) from EHR narratives. We will then develop a phenotype-driven scoring system called EHR-Phenolyzer to predict the likely candidate genetic variants associated with the phenotypes for patients with genomic sequencing and a high probability of a monogenic condition. On this basis, we will develop a probabilistic disease diagnosis and knowledge discovery system using rich and deep EHR phenotypes, and evaluate these methods for genomic diagnosis and discovery using large- scale clinical exome sequencing data. Ultimately, these methods will support efficient, effective, and scalable genomic diagnostics, and facilitate the implementation of genome-guided precision medicine in clinical practice.

项目摘要 该项目的总体目标是建立一个基因组医学学习系统， 知识发现和电子健康记录（EHRs）中的应用。我们将深度融合特色 从EHR中提取表型，并不断发展基因型-表型关联的知识，以优化 变异解释的准确性和临床基因组/外显子组测序的成本效益，以及 通过构建动态的基因型-表型知识网络，加速致病基因的发现。 关于表型-基因关系的先验知识和患者的表型信息可以促进 在临床基因组学背景下从数千种遗传变异中鉴定致病突变 然而，如何最好地从患者的EHR中提取表型信息， 诊断或评估单基因疾病，标准化的可计算表示 表型，并利用它在基因组的解释仍然不清楚。如何系统地比较 发现人类遗传学的新知识仍然是一个基本上尚未开发的领域 很有前途为了应对这些挑战，我们将开发和验证可扩展和可移植的开源 自然语言处理（NLP）方法，用于自动和准确地提取特征表型 概念（例如，“J形蝶鞍”和“身材矮小”）。然后我们将开发一个 表型驱动的评分系统，称为EHR-Phenolyzer，用于预测可能的候选遗传变异 与基因组测序患者的表型相关， 条件在此基础上，我们将开发一个概率疾病诊断和知识发现系统， 丰富和深入的EHR表型，并评估这些方法的基因组诊断和发现，使用大的- 规模临床外显子组测序数据。最终，这些方法将支持高效、有效和可扩展的 基因组诊断，并促进在临床实践中实施基因组指导的精准医学。