Deep Learning for Pulmonary Embolism Imaging Decision Support: A Multi-institutional Collaboration

肺栓塞成像决策支持的深度学习：多机构合作

• 批准号: 9926311
• 负责人: MATTHEW LUNGREN
• 金额: $ 34.86万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-11 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9926311
• 关键词: Academic Medical Centers; Acute; Adoption; Affect; Age; Big Data; Biometry; Caring; Cigarette; Clinical; Clinical Data; Clinical Informatics; Clinical Medicine; Clinical Trials; Collaborations; Communities; Comparative Effectiveness Research; Computerized Medical Record; Computing Methodologies; Data; Databases; Decision Making; Decision Support Model; Diagnostic Imaging; Engineering; Environment; Epidemiology; Evidence based practice; Exposure to; Future; Generations; Gold; Growth; Guidelines; Health Care Costs; Health Expenditures; Healthcare Industry; Healthcare Systems; Image; Image Enhancement; Imaging Techniques; Imaging technology; Immune System Diseases; Incidental Findings; Informatics; Institution; Insurance Carriers; Lead; Learning; Life; Machine Learning; Medical; Medical Imaging; Medical center; Medicare; Mentors; Methodology; Modeling; Obesity; Observational Study; Outcome; Patients; Phenotype; Physicians; Policy Maker; Population; Precision Health; Pregnancy; Principal Investigator; Process; Pulmonary Embolism; Radiation exposure; Radiology Specialty; Randomized; Recommendation; Reporting; Research Personnel; Retrospective Studies; Risk; Role; Scanning; Services; Societies; Source; Spottings; Testing; Time; Unnecessary Procedures; Work; X-Ray Computed Tomography; aged; base; biomedical informatics; chemotherapy; clinical data warehouse; clinical decision support; clinical imaging; cohort; cost; deep learning; diagnosis standard; flexibility; imaging study; improved; inclusion criteria; informatics tool; innovation; insight; learning strategy; lung imaging; model building; mortality; new technology; outcome prediction; patient oriented; payment; personalized risk prediction; point of care; precision medicine; predictive modeling; pressure; radiologist; support tools; tool; unnecessary treatment

Project Summary Diagnostic imaging costs $100 billion annually. These healthcare costs are expected to increase in the coming decade as the national population ages and the pool of insured patients increases. The size and growth of these costs concern policy makers, payers, and society alike. The use of advanced imaging for PE has increased 27 fold in recent years, and this sharp escalation has the potential to expose patients to unnecessary procedures, tests, and risks due to incidental findings. Although radiologists do not order most radiology exams, these physicians are the target of criticism about the rising costs and possible overuse of radiology services. The healthcare industry has called upon radiologists to manage the potential overuse of advanced imaging and to take the lead on investigating best practices for the optimal use of advanced imaging. The ideal sources of information for imaging utilization guidelines are randomized, controlled imaging clinical trials. However, these trials are cost and time intensive, exceedingly difficult to conduct, and typically use narrow patient-inclusion criteria, making it challenging to generalize the results to broader clinical situations. Alternative sources of reliable evidence, such as observational or retrospective studies, have been lacking. The widespread adoption of electronic medical records (EMRs) and the increasing availability of computational methods to process vast amounts of unstructured information now make it possible to learn directly from practice-based evidence. We propose that “big data” clinical repositories, including radiology reports, can lend themselves to a treasure trove of point-of-care, relevant, actionable data that can be used in an innovative and cost-sensitive approach to evaluate the appropriate use of medical imaging. We aim to create a predictive model that leverages real-time EMR clinical data from top national medical centers to arrive at a patient-specific imaging outcome prediction. We recognize that clinicians have to make on-the-spot medical imaging-ordering decisions and they generally do not comply with existing clinical decision support rules. Our study aims to provide clinicians with a tool that can leverage aggregate patient data for medical imaging decision making at the point of care. The overarching approach of this study is to utilize scalable methodology that can be widely applied to leverage EMR data to predict the outcome of a several other high-cost, low-yield imaging tests. This proposal has the potential to better inform advanced imaging in the learning healthcare system of the future and reduce unnecessary imaging examinations and healthcare costs.

项目摘要 诊断成像每年花费1000亿美元。预计这些医疗保健费用将在未来增加 随着全国人口老龄化和参保患者人数的增加，它们的大小和生长 成本涉及决策者、支付者和社会。PE的高级成像使用增加了27 折叠近年来，这种急剧升级有可能使患者暴露于不必要的程序， 测试和风险，由于偶然的发现。尽管放射科医生不会订购大多数放射学检查，但这些 医生是批评的目标，因为放射科服务的费用不断上涨，而且可能过度使用。的 医疗保健行业呼吁放射科医生管理先进成像的潜在过度使用， 带头调查最佳做法，以优化高级成像的使用。 影像学应用指南的理想信息来源是随机、对照的影像学临床 审判然而，这些试验是成本和时间密集型的，极难进行，并且通常使用狭窄的通道。 患者入选标准，使其具有挑战性的结果推广到更广泛的临床情况。替代 缺乏可靠的证据来源，如观察性或回顾性研究。广泛 电子病历（EMR）的采用和计算方法的日益可用性， 处理大量的非结构化信息，现在可以直接从实践中学习 证据我们建议，“大数据”临床知识库，包括放射学报告，可以借给自己一个 护理点、相关、可操作数据的宝库，可用于创新和成本敏感的 方法来评价适当使用医学成像。我们的目标是创建一个预测模型， 利用来自顶级国家医疗中心的实时EMR临床数据， 影像学结果预测。我们认识到，临床医生必须在现场进行医学成像排序 因此，它们通常不符合现有的临床决策支持规则。我们的研究旨在提供 临床医生使用一种工具，可以利用汇总的患者数据进行医学成像决策， 照顾。 本研究的总体方法是利用可扩展的方法，可广泛应用于 利用EMR数据预测其他几种高成本、低产量成像测试的结果。这 该提案有可能更好地为未来学习医疗保健系统中的先进成像提供信息， 减少不必要的成像检查和医疗保健费用。