Advanced Computational Framework for Decision Support in Critically Ill Children

危重儿童决策支持的高级计算框架

• 批准号: 7836596
• 负责人: RANDALL C WETZEL
• 金额: $ 49.28万
• 依托单位: CHILDREN'S HOSPITAL OF LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-30 至 2011-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7836596
• 关键词: Address; Advanced Development; Algorithms; Architecture; Area; Artificial Intelligence; Biological Neural Networks; Businesses; Caring; Categories; Child; Clinical; Clinical Data; Communication; Complex; Computational Technique; Computer Analysis; Computing Methodologies; Critical Illness; Critically ill children; Data; Data Analyses; Data Quality; Data Sources; Databases; Decision Making; Detection; Deterioration; Development; Diagnosis; Diagnostic; Disease; Electronics; Goals; Health Insurance Portability and Accountability Act; Healthcare; Hospital Units; Human; Image; Individual; Industry; Information Technology; Institution; Intelligence; Intensive Care; Knowledge; Laboratories; Lead; Learning; Legal; Machine Learning; Maintenance; Mathematics; Medical; Medical Electronics; Medical Records; Medicine; Memory; Metadata; Methodology; Metric; Mining; Modeling; Monitor; Organ; Outcome; Patients; Pharmaceutical Preparations; Physiological; Population; Privacy; Process; Protocols documentation; Quality Control; Records; Safety; Security; Semantics; Severity of illness; Source; Speed; Stream; Structure; System; Techniques; Technology; Therapeutic; Therapeutic Intervention; Time; Translations; Treatment Protocols; Treatment outcome; Ventilator; Work; Writing; abstracting; analog; base; comparative; computer framework; computer infrastructure; computer science; data integrity; data mining; data modeling; database structure; design; digital; direct patient care; encryption; experience; frailty; heuristics; indexing; medical information system; medical specialties; novel; outcome forecast; patient population; physical science; prognostic; repository; statistics; therapy outcome; tool; trend

DESCRIPTION (provided by applicant): Advanced Computational Framework for Decision Support in Critically Ill Children This application addresses broad Challenge Area (10): Increasing Technology for Processing Health Care Data and specific Challenge Topic 10-LM-102: Advanced decision support for complex clinical decisions. Abstract: Artificial Intelligence (AI) and advanced computational techniques, applied to complex, multidimensional, streaming, clinical data (historic, physiologic, laboratory, imaging, etc) from disparate health care digital data sources, can be used to produce integrated, higher level representations of critically ill patients for knowledge discovery and decision support for the 'next' patient. Vast amounts of digital health care data are available for real time computational analysis using artificial intelligence and statistical approaches such as data clustering and neural networks to ascertain relationships, determine 'diagnostic clusters' and trend outcomes of therapy in individual and groups of patients. While such algorithms have successful applications in business, industry, physical sciences and in parts of health care, several barriers exist for their broader application to intensive care medicine and other health care domains. Using AI and automated computational methodology we will develop algorithms for data mining raw medical data from disparate clinical data sources. This will enable understanding and application of the most recent experiential information from large numbers of critically ill patients to find similarities between a current individual patient and historical, similar populations with known treatment outcomes. This will iteratively lead to a refined representation of the individual patient and guide patient management. This project is not merely the development of a database, although this is an essential precursor for the application of the advanced analytic techniques we will develop. It is ultimately about developing a framework to support the extraction, manipulation and construction of new views, relationships and information of and from observational clinical data from multiple sources to enable meaningful analysis and application at the patient's bedside. There are many open questions not sufficiently addressed about the translation of raw clinical data into an analytic computational infrastructure capable of providing bedside decision support. We will integrate expertise in medicine, computational mathematics and computer science to construct suitable data structures and develop AI computational techniques for analysis and presentation. Our goal is to advance the development of explicit methodology to bring analyzed comparative data to the bedside of critically ill children in real time to support clinical decision-making. Such a process will involve: 1.) transformation of patient-centric data across disparate platforms into a large relational database; 2.) construction of an integrated data model, assuring data integrity, structure and security; and finally, 3.) data analysis and presentation as meaningful comparative metrics that adequately characterize the time-based continuum of critical illness. Analytics can then be developed and algorithms refined that can be applied to severity of illness, prognostic, therapeutic, and anomaly detection in patients, hospital units, and national populations, thus painting a holistic picture of patients and populations. Our goal is to provide an integrated high-level view of a patient compared to and in the context of previous critically ill patients. We will use advanced computational techniques and artificial intelligence to detect categories within raw medical data from disparate data sources allowing the most recent experiential information about large numbers of critically ill children to be mined to find similarities between the index case and historical cases with known outcomes. This will enable decision support for diagnosis, management, therapy and outcomes serving the functions of disease detection, direct patient care, quality, and safety.

描述（由申请人提供）： 危重儿童中的决策支持的高级计算框架该申请解决了广泛的挑战领域（10）：增加处理医疗保健数据和特定挑战主题的技术10-LM-102：对复杂临床决策的高级决策支持。摘要：来自不同医疗保健数字数据源的人工智能（AI）和先进的计算技术，用于复杂，多维，流媒体，临床数据（历史，生理，实验室，成像等）针对“下一个”患者的知识发现和决策支持的重症患者的表示。使用人工智能和统计方法（例如数据聚类和神经网络）来确定关系，确定“诊断簇”以及个人和患者组的治疗趋势结果，可用于实时计算数据进行实时计算数据。尽管这种算法在商业，行业，物理科学和部分医疗保健中都有成功的应用，但在重症监护医学和其他医疗保健领域中，存在一些障碍。使用AI和自动化计算方法，我们将开发来自不同临床数据源的数据挖掘原始医疗数据的算法。这将使了解和应用来自大量重病患者的最新体验信息，以发现当前的患者与具有已知治疗结果的相似人群之间的相似之处。这将迭代地导致单个患者的精致表示并指导患者管理。该项目不仅是数据库的开发，尽管这是我们将要开发的高级分析技术的必要先驱。最终，它是开发一个框架来支持从多个来源的观察性临床数据的提取，操纵和构建，并从多个来源的观察性临床数据和信息中提供有意义的分析和应用。关于将原始临床数据转换为能够提供床边决策支持的分析计算基础架构，有许多未充分解决的问题。我们将整合医学，计算数学和计算机科学方面的专业知识，以构建合适的数据结构并开发用于分析和演示的AI计算技术。我们的目标是推进显式方法的发展，以实时将经过分析的比较数据带到危重儿童的床边，以支持临床决策。这样的过程将涉及：1。）跨不同平台以患者为中心的数据转换为大型关系数据库； 2.）构建集成数据模型，确保数据完整性，结构和安全性；最后，3。）数据分析和呈现是有意义的比较指标，可以充分表征基于时间的重症疾病的连续性。然后可以开发分析，并精制算法，可用于患者，医院单位和国家人口的疾病，预后，治疗和异常检测的严重程度，从而对患者和人群进行整体景象。我们的目标是与以前的重症患者相比，提供患者的综合高级视野。我们将使用先进的计算技术和人工智能来检测来自不同数据源的原始医疗数据中的类别，从而允许开采有关大量重症儿童的最新经验信息，以发现索引案例与具有已知结果的历史案例之间的相似之处。这将为诊断，管理，治疗和成果提供决策支持，以服务于疾病检测，直接患者护理，质量和安全的功能。