Bioinformatics for post-traumatic stress

创伤后应激的生物信息学

基本信息

批准号：
10412074
负责人：
Erich Kummerfeld
金额：
$ 50.2万
依托单位：
UNIVERSITY OF MINNESOTA
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：
2018-08-10 至 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10412074
关键词：
Bioinformatics Biological Biological Markers Categories Clinical Clinical Data Clinical Research Clinical Trials Clinical Trials Database Complex Data Data Analyses Data Set Databases Development Diagnostic Diagnostic tests Diagnostics Research Dimensions Disease Exposure to Genomics Growth Image Laboratories Linear Models Linear Regressions Logistics Machine Learning Maps Measures Medical History Mental Health Mental disorders Metadata Methods Modeling National Institute of Mental Health Nervous System Trauma Neurocognitive Observational Study Outcome Pathology Patients Pattern Persons Phenotype Population Precision therapeutics Prediction of Response to Therapy Predictive Analytics Principal Component Analysis Psychiatry Psychopathology Recovery Reproducibility Research Research Domain Criteria Research Project Grants Severity of illness Source Statistical Methods Supervision Symptoms Syndrome Techniques Testing Therapeutic Trauma Trauma Research Trauma patient Trauma recovery Traumatic Brain Injury Validation Veterans Work accurate diagnosis analytical tool base biobehavior combat veteran computational platform data archive data complexity data mining data repository data sharing demographics diagnostic criteria diverse data feature selection federated computing guided inquiry hands-on learning heterogenous data in silico indexing innovation insight interest large datasets machine learning method multidimensional data multimodality patient population patient subsets post-traumatic stress post-traumatic symptoms precision medicine predictive modeling predictive test psychologic research and development research study response statistics stress related disorder supervised learning symptomatology tool trauma exposure traumatic event treatment planning treatment responders treatment response unsupervised learning vector

项目摘要

Project Summary/Abstract Maladaptive complications following trauma, including post-traumatic stress (PTS), are highly prevalent in both veterans and civilians, and have been difficult to accurately diagnose, manage and treat. Debate regarding diagnostic criteria and the need to represent the full spectrum of inter-connected features contributing to psychopathology has spawned the development of the Research Domain Criteria (RDoC) by the National Institute of Mental Health (NIMH). RDoC is a developing framework to help guide the discovery and validation of new dimensions of mental health disorders and their relationships to underlying biological mechanisms. NIMH now has a rich federated database that currently houses raw data from RDoC-sponsored clinical research, and clinical trial data from the National Database of Clinical Trials (NDCT) with information that may help to unlock the complex and overlapping relationships between symptoms of PTS and the underlying biomarkers to fuel improvements on diagnostic and therapeutic frameworks for trauma recovery. The proposed project will apply bioinformatics and machine learning analytical tools to these large, heterogeneous datasets to identify and validate new research dimensions of trauma-related psychopathology and treatment response trajectories and their predictors. Aim 1 will develop an in silico trauma patient population by integrating data from diverse sources, including cross-sectional and observational longitudinal clinical studies housed within available data repositories for trauma and other related mental health research. Data will include medical history, demographics, diagnostic tests, clinical outcomes, psychological assessments, genomics, imaging, and other relevant study and meta-data. Aim 2 will identify multiple dimensions of PTS diagnostic criteria, using a combination of unsupervised dimension-reduction statistical methods, internal and external cross-validation, and supervised hypothesis testing of predictive models to understand the heterogeneous subtypes of PTS. Aim 3 will deploy unsupervised machine learning methods, such as topological data analysis and hierarchical clustering, to identify unique clusters of patients based on symptomatology to develop clustering methods for precision mapping of PTS patients based on disease severity. Aim 4 will use supervised machine learning techniques for targeted predictive analytics focused on identifying treatment responders from the NDCT, and identification of latent variables that predict treatment response. The results of the proposed research project will greatly enrich the field of computational psychiatry research to identify conserved dimensions associated with the complex relationships of psychopathology and precision treatment planning following exposure to traumatic events.

项目概要/摘要创伤后的适应不良并发症，包括创伤后应激障碍（PTS），在这两种疾病中都非常普遍。退伍军人和平民，并且很难准确诊断、管理和治疗。关于的辩论诊断标准以及代表所有相互关联的特征的需要精神病理学催生了国家研究领域标准（RDoC）的发展心理健康研究所（NIMH）。 RDoC 是一个帮助指导发现和验证的开发框架精神健康障碍的新维度及其与潜在生物机制的关系。 NIMH 现在拥有丰富的联合数据库，其中包含来自 RDoC 赞助的临床的原始数据来自国家临床试验数据库 (NDCT) 的研究和临床试验数据，其中包含可能的信息有助于解开 PTS 症状与潜在症状之间复杂且重叠的关系生物标记物可促进创伤康复诊断和治疗框架的改进。这拟议的项目将应用生物信息学和机器学习分析工具来分析这些大型的、异构的用于识别和验证创伤相关精神病理学和治疗的新研究维度的数据集响应轨迹及其预测因子。目标 1 将通过以下方式开发计算机模拟创伤患者群体：整合不同来源的数据，包括横断面和观察性纵向临床研究存放在用于创伤和其他相关心理健康研究的可用数据存储库中。数据将包括病史、人口统计、诊断测试、临床结果、心理评估、基因组学、成像以及其他相关研究和元数据。目标 2 将确定 PTS 诊断的多个维度标准，使用无监督降维统计方法、内部和外部的组合交叉验证和预测模型的监督假设检验以了解异质性 PTS 的亚型。目标3将部署无监督机器学习方法，例如拓扑数据分析和层次聚类，根据症状学识别独特的患者群体，以开发根据疾病严重程度对 PTS 患者进行精确绘图的聚类方法。目标 4 将使用监督用于有针对性的预测分析的机器学习技术，重点是识别治疗反应者 NDCT 以及预测治疗反应的潜在变量的识别。拟议的结果研究项目将极大地丰富计算精神病学研究领域，以识别保守的与精神病理学和精确治疗计划的复杂关系相关的维度经历创伤事件后。

项目成果

期刊论文数量（2）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Causal discovery replicates symptomatic and functional interrelations of posttraumatic stress across five patient populations.

因果发现复制了五个患者群体中创伤后应激的症状和功能相互关系。

DOI：
发表时间：
2022
期刊：
影响因子：
0
作者：
Pierce, Benjamin;Kirsh, Thomas;Ferguson, Adam R;Neylan, Thomas C;Ma, Sisi;Kummerfeld, Erich;Cohen, Beth E;Nielson, Jessica L
通讯作者：
Nielson, Jessica L

Increased suicidal ideation and suicide attempts in COVID-19 patients in the United States: Statistics from a large national insurance billing database.

美国 COVID-19 患者的自杀意念和自杀企图增加：来自大型国家保险计费数据库的统计数据。

DOI：
发表时间：
2023-05
期刊：
影响因子：
11.3
作者：
Reinke, Michael;Falke, Chloe;Cohen, Ken;Anderson, David;Cullen, Kathryn R;Nielson, Jessica L
通讯作者：
Nielson, Jessica L