Building and implementing a TBI prognostic model featuring real-time analysis of brain CT images

构建并实施具有脑部 CT 图像实时分析功能的 TBI 预后模型

• 批准号: 10446746
• 负责人: Timothy William Dunn
• 金额: $ 66.23万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10446746
• 关键词: Accident and Emergency department; Admission activity; Affect; American; Brain; Caring; Cause of Death; Clinical; Clinical Trials; Collaborations; Complex; Complication; Country; Data; Data Set; Databases; Decision Making; Diagnosis; Early Diagnosis; Emergency Care; Environment; Epidemic; Future; Goals; Health system; Healthcare; Healthcare Systems; Hemorrhage; Hospitals; Hour; Inflammation; Infrastructure; Injury; Institution; Intervention; Left; Medical History; Methods; Modeling; Modernization; Monitor; Neural Network Simulation; Neurologic; Outcome; Output; Pathway interactions; Patient risk; Patient-Focused Outcomes; Patients; Performance; Procedures; Process; Prognosis; Prospective cohort; Provider; Psyche structure; Resources; Risk; Running; Secure; Sensitivity and Specificity; Ships; Site; TBI treatment; Technology; Testing; Time; Training; Training and Education; Trauma; Traumatic Brain Injury; Triage; United States; Universities; Update; Validation; Withdrawal; Work; X-Ray Computed Tomography; base; clinical care; clinical risk; cohort; combat; computer infrastructure; cost; dashboard; data infrastructure; data warehouse; deep learning model; deep neural network; demographics; design; disability; discrete time; experience; experimental study; improved; machine learning model; mild traumatic brain injury; model building; network architecture; neural network architecture; neuron loss; neurosurgery; physically handicapped; predictive modeling; predictive tools; prognostic model; prospective; readmission rates; real-time images; tool; treatment guidelines; young adult

Scope of Work Duke will complete all work for the machine learning model building and implementation of the model into the Duke clinical workflow. For Aim 1 of the project, this work will include data extraction and cleaning, neural network architecture design, and model optimization and validation. For Aim 2, this work will include establishment of technical infrastructure for real-time image and access and processing, construction of a front-end dashboard in close collaboration with frontline clinicians, and deployment and prospective validation of the model. The latter step will also consist of education and training of hospital users. For Aim 3 of the project, Duke will guide staff at Jefferson through the model implementation and validation process, with the active integration and training performed by staff at Jefferson. In Aim 3 Duke will also run the experiments on multi-site model generalization, using retrospective data at both Duke and Jefferson. Data will be shared between Duke and Jefferson via secure ethernet transfer between Jefferson’s secure data warehouse and Duke’s Protected Analytics and Computing Environment. The end goal of the work will be to provide a sophisticated, high-accuracy, and seamlessly integrated tool for predicting the risk of actionable TBI complications over the course of a TBI patient’s hospital encounter. This method, which will augment decision-making for treating a complex neurological condition, will significantly improve overall TBI outcomes, reduce readmission rates, and minimize the extraordinary costs incurred by inefficient provision of healthcare resources.

工作范围