Automatic discovery and processing of EEG cohorts from clinical records

从临床记录中自动发现和处理脑电图队列

• 批准号: 8876239
• 负责人: Sanda Maria Harabagiu
• 金额: $ 45.99万
• 依托单位: TEMPLE UNIV OF THE COMMONWEALTH
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-06-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8876239
• 关键词: Accident and Emergency department; Archives; Area; Basic Science; Big Data; Bilateral; Biomedical Engineering; Blinking; Cerebrum; Clinical; Clinical Data; Clinical Research; Clinical Treatment; Code; Comparative Study; Computer software; Computerized Medical Record; Country; Data; Development; Diagnosis; Diffuse; Discharge from eye; Electroencephalography; Epilepsy; Evaluation; Event; Exclusion Criteria; Feedback; Frequencies; Functional disorder; Generations; Goals; Graph; Hospitals; Image; Judgment; Knowledge; Language; Learning; Life; Link; Measurement; Medical; Medical Informatics; Medical Records; Medical Students; Mining; Modality; Modeling; Morphologic artifacts; Multimedia; Nature; Neurosciences; Outcome; Patients; Pattern; Physicians; Process; Protocols documentation; Qualifying; Records; Reporting; Research; Research Personnel; Research Support; Resources; Retrieval; Role; Signal Transduction; Solutions; System; Techniques; Testing; Text; Time; Training; University Hospitals; Validation; base; career; cohort; comparative; comparative effectiveness; design; effectiveness research; inclusion criteria; information organization; language processing; novel; public health relevance; repository

 DESCRIPTION (provided by applicant): Electronic medical records (EMRs) collected at every hospital in the country collectively contain a staggering wealth of biomedical knowledge. EMRs can include unstructured text, temporally constrained measurements (e.g., vital signs), multichannel signal data (e.g., EEGs), and image data (e.g., MRIs). This information could be transformative if properly harnessed. Information about patient medical problems, treatments, and clinical course is essential for conducting comparative effectiveness research. Uncovering clinical knowledge that enables comparative research is the primary goal of this proposal. We will focus on the automatic interpretation of clinical EEGs collected over 12 years at Temple University Hospital (over 25,000 sessions and 15,000 patients). Clinicians will be able to retrieve relevant EEG signals and EEG reports using standard queries (e.g. "Young patients with focal cerebral dysfunction who were treated with Topamax"). In Aim 1 we will automatically annotate EEG events that contribute to a diagnosis. We will develop automated techniques to discover and time-align the underlying EEG events using semi-supervised learning. In Aim 2 we will process the text from the EEG reports using state-of-the-art clinical language processing techniques. Clinical concepts, their type, polarity and modality shall be discovered automatically, as well as spatial and temporal information. In addition, we shall extract the medical concepts describing the clinical picture of patients from the EEG reports. In Aim 3, we will develop a patient cohort retrieval system that will operate on the clinical knowledge extracted in Aims 1 and 2. In addition we shall organize this knowledge in a unified representation: the Qualified Medical Knowledge Graph (QMKG), which will be built using BigData solutions through MapReduce. The QMKG will be able to be searched by biomedical researchers as well as practicing clinicians. The QMKG will also provide a characterization of the way in which events in an EEG are narrated by physicians and the validation of these across a BigData resource. The EMKG represents an important contribution to basic science. In Aim 4 we will validate the usefulness of the patient cohort identification system by collecting feedback from clinicians and medical students who will participate in a rigorous evaluation protocol. Inclusion and exclusion criteria for the queries shall be designed and experts will provide relevance judgments for the results. For each query, medical experts shall examine the top-ranked cohorts for common precision errors (false positives) and the bottom five ranked common recall errors (false negatives). User validation testing will be performed using live clinical data and the feedback wil enhance the quality of the cohort identification system. The existence of an annotated BigData archive of EEGs will greatly increase accessibility for non- experts in neuroscience, bioengineering and medical informatics who would like to study EEG data. The creation of this resource through the development of efficient automated data wrangling techniques will demonstrate that a much wider range of BigData bioengineering applications are now tractable.

 描述（由适用提供）：在该国每家医院收集的电子病历（EMR）统称具有惊人的生物医学知识。 EMR可以包括非结构化文本，暂时约束的测量值（例如生命体征），多通道信号数据（例如，EEGS）和图像数据（例如MRIS）。如果正确利用，此信息可能会具有变革性。有关患者医疗问题，治疗和临床课程的信息对于进行比较有效性研究至关重要。探索能够进行比较研究的临床知识是该提议的主要目标。我们将重点介绍在坦普尔大学医院（Temple University Hospital）12年内收集的临床脑电图（超过25,000次和15,000名患者）的自动解释。临床医生将能够检索 使用标准查询的相关脑电图和脑电图报告（例如，“接受过托帕马克斯治疗的局灶性脑功能障碍的年轻患者”）。在AIM 1中，我们将自动注释有助于诊断的EEG事件。我们将开发自动化的技术，以使用半监督的学习来发现和时间一致。在AIM 2中，我们将使用最先进的临床语言处理技术从脑电图报告中处理文本。临床概念，它们的类型，极性和方式应自动发现 以及空间和临时信息。此外，我们还将提取描述脑电图报告中患者的临床情况的医学概念。在AIM 3中，我们将开发一个患者队列检索系统，该系统将根据AIMS 1和2中提取的临床知识运行。此外，我们还将以统一的代表形式来组织此知识：合格的医学知识图（QMKG），该图将通过MapReduce使用BigData Solutions构建。生物医学研究人员和执业临床医生将能够搜索QMKG。 QMKG还将提供由医生讲述脑电图中事件的方式以及在Bigdata资源中对这些事件的验证的特征。 EMKG代表了对基础科学的重要贡献。在AIM 4中，我们将通过收集将参加严格评估协议的临床医生和医学生的反馈来验证患者队列识别系统的实用性。应设计疑问的包含和排除标准，专家将为结果提供相关法官。对于每个查询，医学专家应检查排名最高的队列是否存在常见的精确错误（误报）和排名第五的常见召回错误（假否定性）。用户验证测试将使用实时临床数据进行，反馈将提高队列识别系统的质量。脑电图的带注释的BigData档案的存在将大大提高想要研究脑电图数据的神经科学，生物工程和医学信息的非专家的可访问性。通过开发有效的自动数据包装技术来创建此资源，将表明，现在可以处理大量更广泛的BigData生物工程应用程序。