Modeling the temporal succession of phenotypes & environnnental cues

对表型的时间连续进行建模

• 批准号: 8382712
• 负责人: ANDREY RZHETSKY
• 金额: $ 22.03万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-22 至 2016-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8382712
• 关键词: Autistic Disorder; Biological Markers; Categories; Characteristics; Clinical; Clinical Data; Collaborations; Complex; Cues; Data; Databases; Diagnostic; Disease; Electronic Health Record; Environmental Risk Factor; Genetic; Genomics; Health; Healthcare Systems; Individual; Life; Mental Depression; Modeling; Natural Language Processing; Neurodevelopmental Disorder; Patients; Phenotype; Process; Sampling; Schizophrenia; Statistical Models; Subgroup; System; Testing; Therapeutic; Time; Validation; Variant; care delivery; comorbidity; disease phenotype; neuropsychiatry; time use

This project two serves as the bookends of a hypothesis-test-verify cycle to define the shared and distinct components of neurodevelopmental diseases. For the first bookend: In the context of a large healthcare system, we use the processed electronic health records (through Natural Language Processing¿NLP) to determine the temporal course of multiple health/clinical attributes observed and/or obtained during the course of care delivery. These then are used, in collaboration with Project 4 to define temporal co-morbidity trajectories for each of these diseases and to determine the extent to which there is shared phenomenology particularly in time. For the second bookend: those genomic variants/sequences that are predicted/hypothesized to define subgroups previously characterized by clinical trajectories, or predict conventional biomarkers/clinical findings and/or (most preferably but most challenging) predict differential therapeutic responsiveness.

这个项目两个是假设检验验证循环的书架，以定义共同且独特的 神经发育疾病的成分。对于第一个书挡：在大型医疗保健的背景下 系统，我们使用已处理的电子健康记录（通过自然语言处理€nlp） 确定观察到的多个健康/临床属性的临时过程和/或 提供护理过程。然后将这些与项目4合作使用来定义临时合并症 这些疾病的轨迹轨迹，并确定有共同现象学的程度 特别是及时。对于第二本书：那些基因组变体/序列 预测/假设以定义先前以临床轨迹为特征的亚组，或预测 常规生物标志物/临床发现和/或（最优先但最挑战）预测差异 治疗反应能力。