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

基本信息

批准号：
10164857
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
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-17 至 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10164857
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 implementation facilitation improved information organization innovation interoperability next generation novel open source patient health information phenotypic data pituitary fossa portability precision medicine success

项目摘要

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.

项目概要该项目的总体目标是建立基因组医学学习系统，以加速基因组医学学习电子健康记录（EHR）中的知识发现和应用我们将整合深层特征。从 EHR 中提取的表型以及不断发展的基因型-表型关联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，以及通过构建动态基因型-表型知识网络加速因果基因的发现。关于表型-基因关系和患者表型信息的先验知识可以促进在临床基因组背景下识别数千种遗传变异中的致病突变然而，如何最好地从患者的 EHR 记录中提取表型信息？诊断或评估单基因疾病，标准化可计算表示表型，并将其用于基因组解释仍不清楚。通过跨疾病的表型来发现人类遗传学的新知识仍然是一个很大程度上尚未开发的领域为了应对这些挑战，我们将开发和验证可扩展和可移植的开源软件。用于自动准确提取特征表型的自然语言处理 (NLP) 方法然后，我们将开发一个来自 EHR 叙述的概念（例如，“j 形蝶鞍”和“身材矮小”）。表型驱动的评分系统（称为 EHR-Phenolyzer）可预测可能的候选遗传变异与基因组测序患者的表型相关，并且单基因的可能性很高在此基础上，我们将开发一个概率疾病诊断和知识发现系统。丰富而深入的 EHR 表型，并使用大数据评估这些用于基因组诊断和发现的方法最终，这些方法将支持高效、有效和可扩展的临床外显子组测序数据。基因组诊断，促进基因组引导的精准医学在临床实践中的实施。