Informatics Core

信息学核心

基本信息

批准号：
9898138
负责人：
Dexter D Hadley
金额：
$ 144.62万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-25 至 2023-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9898138
关键词：
Accounting Alzheimer disease detection Artificial Intelligence Biochemical Markers California Caring Chronic low back pain Clinic Clinical Clinical Data Collaborations Complex Computer Vision Systems Computing Methodologies Coupling Data Delirium Development Disease Disease Pathway Electronic Health Record Goals Human Hyperglycemia Hyperlipidemia Hypertension Image Informatics Infrastructure Intelligence Intervention Laboratories Machine Learning Manuals Medical Medical Imaging Methodology Methods Modality Modeling Modernization Operative Surgical Procedures Outcome Pain Pain management Pathogenesis Pathway interactions Patient-Focused Outcomes Patients Performance Pharmaceutical Preparations Phenotype Physical therapy Physiological Positioning Attribute Procedures Provider Recommendation Records Replacement Arthroplasty Research Research Project Grants Risk Factors Skin Cancer Spinal Fusion Statistical Factor Analysis Symbiosis System Techniques Time Translating Treatment outcome Universities Work base clinical application clinical imaging clinical predictors clinical risk clinically relevant common treatment convolutional neural network deep learning digital disability experience hospital readmission improved improved outcome innovation insight learning strategy malignant breast neoplasm mindfulness molecular marker multimodal data novel pain outcome pain patient pathology imaging patient oriented patient subsets personalized medicine programs psychosocial radiological imaging statistics synergism translational model treatment planning treatment response

项目摘要

The Informatics Core (IC) will translate patient data on chronic lower back pain (cLBP) collected by UCSF REACH into translational models of clinical intelligence that improve cLBP outcomes. By coupling UCSF's extensive clinical experience with this second-to-none infrastructure to perform AI research based on EHR data, the IC has the potential to produce innovative collaborative research that significantly improves cLBP outcomes for patients. The overarching goal of IC is to work in symbiosis with other UCSF REACH Research and Clinical cores to develop novel AI methodologies for interpreting imaging and other Electronic Health Record (EHR) data to significantly improve cLBP care. In order to enable the full utilization of data collected by UCSF REACH, IC will aid in the development and clinical application of statistical and machine learning methods with the following specific aims. In in Aim 1, to identify patient subgroups based on cLBP phenotypes, we will work with the Reall other cores to use the existing curated EHR and newly collected data from cLBP patients to better understand cLBP disease pathways. To analyze trade-offs and synergies between treatment objectives, IC will apply canonical correlation analysis to discover clinical insights potential synergies and trade-offs between various treatment outcomes (pain, physical and psychosocial disability metrics) to develop machine learning methods for predicting patient-specific treatment response. Finally, IC will apply state-of-the art convolutional neural network techniques in predicting clinically relevant outcomes from medical imaging to optimize this clinical insight and personalize treatment plans for patients.

Informatics Core（IC）将翻译有关UCSF收集的慢性下背部疼痛（CLBP）的患者数据进入临床智能的转化模型，以改善CLBP结果。通过耦合UCSF的耦合基于EHR数据进行AI研究的首次进行AI研究，广泛的临床经验， IC有可能产生创新的协作研究，从而大大改善CLBP的结果适用于患者。 IC的总体目标是与其他UCSF进行共生研究和临床核心开发用于解释成像和其他电子健康记录（EHR）数据的新型AI方法论显着改善CLBP护理。为了使UCSF收集的数据完全利用，IC 将有助于开发和临床应用统计和机器学习方法的应用具体目标。在AIM 1中，为了确定基于CLBP表型的患者亚组，我们将与使用其他核心使用现有的策划EHR和来自CLBP患者的新收集的数据以更好了解CLBP疾病途径。为了分析治疗目标之间的权衡和协同作用，将应用规范相关分析，以发现临床见解潜在的协同和权衡各种治疗结果（疼痛，身体和社会心理残疾指标）开发机器学习预测患者特异性治疗反应的方法。最后，IC将应用最先进的卷积神经网络技术在预测医学成像的临床相关结果以优化这一点临床洞察力和个性化患者的治疗计划。