SCH: Heterogenous, dynamic synthetic data: From algorithms to clinical applications

SCH：异构动态合成数据：从算法到临床应用

• 批准号: 10559690
• 负责人: Jason Yeates Adams
• 金额: $ 29.86万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-01 至 2025-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10559690
• 关键词: Accident and Emergency department; Acute Respiratory Distress Syndrome; Acute respiratory failure; Address; Admission activity; Advanced Development; Affect; Algorithms; American; Architecture; Area; Artificial Intelligence; Benchmarking; Caring; Cessation of life; Clinical; Critical Care; Critical Illness; Data; Data Protection; Data Set; Detection; Development; Diagnosis; Discrimination; Effectiveness; Electronic Health Record; Ensure; Evaluation; Evolution; Failure; Functional disorder; Generations; Goals; Health; Healthcare; Hospitals; Housing; Image; Instruction; Interdisciplinary Study; Intubation; Laboratories; Laws; Lung; Machine Learning; Manuals; Mathematics; Mechanical ventilation; Medicine; Methodology; Methods; Modality; Modeling; Morbidity - disease rate; Operating Rooms; Patients; Perception; Play; Privacy; Property; Research; Respiratory Failure; Risk; Societies; Statistical Methods; Syndrome; Techniques; Testing; Time; Transportation; Treatment outcome; Validation; algorithm development; care costs; clinical application; clinical decision support; clinical development; clinical practice; clinically relevant; cohort; complex data; cost; cost efficient; data framework; data privacy; data quality; experience; health data; high risk; improved; innovation; mortality; multimodal data; multimodality; novel; privacy preservation; psychologic; respiratory; theories; tool

Gaining access to health data is the major barrier in developing and validating new AI methods for clinical applications, since health data are protected by strict privacy laws. A significant obstacle in current data provisioning is that existing methods to access and deidentifying health data are increasingly being challenged for their effectiveness, with a common perception that it is generally impossible to fully deidentify any health data set and still retain utility for research purposes. Synthetic data is a promising concept for solving this conundrum, by reconciling data innovation with data privacy. The goal of synthetic data is to create an as-realistic-as-possible dataset generated from existing data - one that maintains the statistical properties of the original dataset, but does so without risk of exposing sensitive information. While synthetic data is not new in health care, so far it was limited to simple, single-modality, static datasets, which severely affected its impact. The aim of this interdisciplinary research effort is the development of an algorithmic framework for the faithful and privacy-preserving generation of heterogeneous, dynamic synthetic datasets to boost the development of clinical decision support applications. In the US, critical illness effects a significant number of Americans per year with an estimated 4 million admission and 500,000 deaths per year. A sizable proportion of the patients suffer respiratory failure requiring intubation. To increase the utility of algorithms in clinical applications, like in the ICU, strategies are needed to address barriers to use of complex data. Thus, the ICU is a prototypical setting where high-quality synthetic data would be tremendously helpful to break through this data bottleneck, while respecting health data privacy laws. However, ascertaining data to test and validate the algorithms is difficult to obtain. As such, this project proposes to use a type of severe respiratory (lung) failure, acute respiratory distress syndrome (ARDS) to study the use of synthetic data for the development of artificial intelligence-based algorithms. Patients with ARDS experience substantial morbidity and mortality, prolonged mechanical ventilation high hospital-associated costs, and long-term physical and psychological dysfunction. Using ARDS as an archetypical model to guide this research effort will a ensure successful transition from theory to clinical practice. RELEVANCE (See instructions): The results of this project will play a key role in advancing AI research in health, especially in areas of high-risk, high-cost care such as the emergency department, operating room, and ICU. On a specific level, the project will improve detection and treatment of the acute respiratory distress syndrome. On a broader level, this effort will contribute to more cost-efficient health care while enabling improved patient treatment outcomes.

获得健康数据是开发和验证新的AI方法的主要障碍 应用程序，因为健康数据受严格的隐私法保护。当前数据的重要障碍 提供的是，现有的访问和去识别健康数据的方法越来越多 他们的有效性受到挑战，人们普遍认为通常不可能完全 去识别任何健康数据集，并且仍然保留用于研究目的的实用程序。 合成数据是解决此难题的一个有前途的概念，通过将数据创新与 数据隐私。综合数据的目的是创建一个从 现有数据 - 维护原始数据集的统计属性的数据，但没有风险 暴露敏感信息。虽然合成数据在医疗保健中并不新鲜，但到目前为止， 简单，单模式，静态数据集，严重影响了其影响。 这项跨学科研究工作的目的是开发算法框架 忠实而保护隐私的生成异质，动态合成数据集，以提高 开发临床决策支持应用程序。 在美国，危害疾病每年影响大量美国人，估计有400万 每年入院和500,000人死亡。相当一部分患者遭受呼吸衰竭 需要插管。为了增加算法在临床应用中的效用，例如在ICU中，策略 需要解决复杂数据的使用障碍。因此，ICU是一个典型的环境 高质量的合成数据对突破此数据瓶颈非常有帮助，而 尊重健康数据隐私法。但是，确定数据以测试和验证算法是 难以获得。因此，该项目建议使用一种严重的呼吸道（肺）失败，急性 呼吸窘迫综合征（ARDS）研究合成数据用于人工的发展 基于智能的算法。 ARDS患者的发病率和死亡率很大， 长时间的机械通气高医院相关的成本以及长期的身体和心理 功能障碍。将ARDS作为原型模型指导这项研究工作将确保成功 从理论到临床实践的过渡。 相关性（请参阅说明）： 该项目的结果将在推进健康研究方面的关键作用，尤其是在 高风险，高成本护理，例如急诊室，手术室和ICU。在特定级别 该项目将改善急性呼吸窘迫综合征的检测和治疗。更广泛 级别，这项工作将有助于更具成本效益的医疗保健，同时可以改善患者治疗 结果。