Personalized risk assessment in Neurofibromatosis Type 1

1 型神经纤维瘤病的个性化风险评估

基本信息

批准号：
10621489
负责人：
Aditi Gupta
金额：
$ 59.67万
依托单位：
WASHINGTON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-04-01 至 2027-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10621489
关键词：
Adult Affect Algorithms Artificial Intelligence Attention deficit hyperactivity disorder Automobile Driving Behavioral Benign Biological Markers Biological Models Birth Caring Central Nervous System Characteristics Child Clinical Clinical Data Clinical Markers Collaborations Complex Data Data Set Development Diagnosis Disease Disease Management Disease Surveillance Electronic Health Record Environment Ethnic Origin Family Family member Genetic Diseases Germ-Line Mutation Glioma Healthcare Systems Individual Informatics Inherited Institution Knowledge Longevity Machine Learning Malignant - descriptor Manuals Measurable Measures Methods Modeling Morbidity - disease rate NF1 gene Neurofibromatoses Neurofibromatosis 1 Optics Outcome Pathway interactions Patients Pattern Pediatric Hospitals Performance Peripheral Nervous System Neoplasms Phenotype Physicians Population Predisposition Preventive measure Prognostic Marker Psychometrics Quality of life Race Registries Reproducibility Research Risk Risk Assessment Safety Site Structure Symptoms Syndrome Techniques Time Universities Washington artificial intelligence method autosome behavioral phenotyping body system burden of illness care outcomes care providers clinical database clinical decision support clinical decision-making clinical heterogeneity clinical phenotype clinical research site clinically actionable comparative cost data harmonization deep learning disease phenotype disease prognosis disorder risk electronic structure gene interaction improved individual patient insight inter-individual variation interest machine learning model multiple data sources multiscale data novel phenotyping algorithm point of care precision medicine predictive modeling predictive tools prognostic response risk stratification scoliosis sex structured data success support tools text searching tool tumor verification and validation

Adult Affect Algorithms Artificial Intelligence Attention deficit hyperactivity disorder Automobile Driving Behavioral Benign Biological Markers Biological Models Birth Caring Central Nervous System Characteristics Child Clinical Clinical Data Clinical Markers Collaborations Complex Data Data Set Development Diagnosis Disease Disease Management Disease Surveillance Electronic Health Record Environment Ethnic Origin Family Family member Genetic Diseases Germ-Line Mutation Glioma Healthcare Systems Individual Informatics Inherited Institution Knowledge Longevity Machine Learning Malignant - descriptor Manuals Measurable Measures Methods Modeling Morbidity - disease rate NF1 gene Neurofibromatoses Neurofibromatosis 1 Optics Outcome Pathway interactions Patients Pattern Pediatric Hospitals Performance Peripheral Nervous System Neoplasms Phenotype Physicians Population Predisposition Preventive measure Prognostic Marker Psychometrics Quality of life Race Registries Reproducibility Research Risk Risk Assessment Safety Site Structure Symptoms Syndrome Techniques Time Universities Washington artificial intelligence method autosome behavioral phenotyping body system burden of illness care outcomes care providers clinical database clinical decision support clinical decision-making clinical heterogeneity clinical phenotype clinical research site clinically actionable comparative cost data harmonization deep learning disease phenotype disease prognosis disorder risk electronic structure gene interaction improved individual patient insight inter-individual variation interest machine learning model multiple data sources multiscale data novel phenotyping algorithm point of care precision medicine predictive modeling predictive tools prognostic response risk stratification scoliosis sex structured data success support tools text searching tool tumor verification and validation

展开

项目摘要

Project Summary/Abstract Neurofibromatosis (NF) encompasses a set of complex genetic disorders that affect almost every organ system and increase risk for the development of benign and malignant central and peripheral nervous system tumors. Of the three types of NF, Neurofibromatosis Type 1 (NF1) is the most prevalent occurring in approximately 1 in every 3,000 births without predilection for race, sex, or ethnicity. While NF1 is inherited in a fully penetrant autosomal dominant manner, there is wide inter-individual variability with respect to clinical features and their impact on patient morbidity. Clinical heterogeneity is a pervasive challenge for clinicians and families, as the management of children and adults with NF1 remains largely reactive, without reliable biomarkers or predictive models for early risk stratification and/or prognostic assessment at the time of diagnosis. Traditional approaches, which focus on identifying a single clinical or biological marker that can be measured and used to assess disease risk or trajectory in NF1, have achieved limited success and have hindered progress in the development of precision medicine for NF1-affected individuals. In response to these challenges, and with the opportunity to improve the care of individuals with NF1, we aim to verify and validate an alternative and generalizable approach for developing artificial intelligence (AI)-based clinical decision support tools for NF1 sub-phenotypes, implemented and evaluated in a comparative manner across two clinical sites. Our proposed project will first generate a multi-scale data set using a text-mining based clinical phenotyping algorithm to integrate and harmonize data from multiple sources such as clinical databases, structured electronic health records, and unstructured clinical notes. Secondly, we will develop AI-based pipelines capable of generating predictive models and tools to identify disease risk for three critical NF1 sub- phenotypes (OPGs, scoliosis, and ADHD). We will then evaluate the models for quantitative accuracy and clinical actionability at the point of care with the help of NF1 clinicians. Finally, we will validate these methods and models across multiple sites, so that we can better understand the challenges to generalizing and transporting such predictive models based across different healthcare systems, environments, and populations. We anticipate that the use of artificial intelligence techniques in order to study NF1-specific sub-phenotypes at two different sites will yield novel and potentially clinically-actionable and generalizable insights concerning the precision diagnosis and care of individuals with NF1, with broader applicability across a spectrum of similarly complex disease-states.

项目摘要/摘要神经纤维瘤病（NF）包括一组影响几乎每个器官系统的复杂遗传疾病并增加良性和恶性中心和周围神经系统肿瘤发展的风险。的 NF的三种类型的神经纤维瘤病1型（NF1）是最普遍的发生在大约1中每3,000个出生而没有种族，性别或种族的偏爱。而NF1则以完全渗透率继承常染色体显性方式，相对于临床特征及其它们的个体间差异很大对患者发病率的影响。临床异质性是临床医生和家庭的普遍挑战，因为 NF1的儿童和成人的管理基本上是反应性的，没有可靠的生物标志物或诊断时的早期风险分层和/或预后评估的预测模型。传统方法，重点是识别可以测量的单个临床或生物标记并用于评估NF1中的疾病风险或轨迹，取得了有限的成功，并阻碍了进步在为受NF1影响的个体的精确医学开发中。应对这些挑战以及我们有机会改善NF1个人的护理，我们旨在验证和验证替代方案开发人工智能（AI）基于NF1的临床决策支持工具（AI）的方法在两个临床部位以比较方式实施和评估的子表型。我们提出的项目将首先使用基于文本挖掘的临床表型生成多尺度数据集算法集成和协调来自多个来源的数据，例如临床数据库，结构化电子健康记录和非结构化临床注释。其次，我们将开发基于AI的管道能够生成预测模型和工具，以确定三个关键NF1子的疾病风险表型（OPG，脊柱侧弯和ADHD）。然后，我们将评估模型的定量准确性和在NF1临床医生的帮助下，在护理上的临床可行性。最后，我们将验证这些方法以及跨多个站点的模型，以便我们可以更好地了解概括和概括的挑战在不同的医疗保健系统，环境和人群中运输此类预测模型。我们预计使用人工智能技术来研究NF1特异性亚表格型两种不同的地点将产生有关有关该的新颖且潜在的临床和可推广的见解精确诊断和护理NF1的个体，在类似范围内具有更广泛的适用性复杂的疾病状态。