信息学核心-猫眼课题宝

信息学核心

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

项目状态：

已结题

起止时间：

2019-09-25 至 2023-08-31

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

中文摘要

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英文摘要

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.

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