Scalable EEG interpretation using Deep Learning and Schema Descriptors

使用深度学习和模式描述符的可扩展脑电图解释

• 批准号: 9243724
• 负责人: Sanda Maria Harabagiu
• 金额: $ 37.47万
• 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/9243724
• 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; Descriptor; Development; Diagnosis; Diffuse; Discharge from eye; Electroencephalography; Epilepsy; Evaluation; Event; Exclusion Criteria; Feedback; Frequencies; Functional disorder; Generations; Goals; Graph; Health; 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; Reporting; Research; Research Personnel; Research Support; Resources; Retrieval; Role; Signal Transduction; System; Techniques; Testing; Text; Time; Training; University Hospitals; Validation; base; career; cohort; comparative; comparative effectiveness; data archive; data wrangling; design; effectiveness research; inclusion criteria; information organization; language processing; novel; 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（由申请人提供）：在该国每家医院收集的电子病历（EMR）共同包含了惊人的生物医学知识财富。EMR可以包括非结构化文本、时间上受约束的测量（例如，生命体征），多通道信号数据（例如，EEG）和图像数据（例如，MRI）。这些信息如果得到适当利用，可以带来变革。关于患者医疗问题、治疗和临床过程的信息对于进行比较有效性研究至关重要。本提案的主要目标是揭示能够进行比较研究的临床知识。我们将专注于对天普大学医院12年来收集的临床脑电图（超过25，000次会议和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EEG信号和EEG报告（例如，“接受妥泰治疗的局灶性脑功能障碍年轻患者”）。在目标1中，我们将自动注释有助于诊断的EEG事件。我们将开发自动化技术，使用半监督学习来发现和时间对齐潜在的EEG事件。在目标2中，我们将使用最先进的临床语言处理技术处理EEG报告中的文本。应自动发现临床概念及其类型、极性和模态， 以及空间和时间信息。此外，我们将从EEG报告中提取描述患者临床情况的医学概念。在目标3中，我们将开发一个患者队列检索系统，该系统将对目标1和2中提取的临床知识进行操作。此外，我们将以统一的表示形式组织这些知识：合格的医学知识图（QMKG），它将通过MapReduce使用BigData解决方案构建。QMKG将能够被生物医学研究人员以及执业临床医生搜索。QMKG还将提供医生叙述EEG中事件的方式的特征描述，以及在BigData资源中对这些事件的验证。EMKG是对基础科学的重要贡献。在目标4中，我们将通过收集临床医生和医学生的反馈来验证患者队列识别系统的有效性，他们将参与严格的评估方案。应设计查询的纳入和排除标准，专家将对结果进行相关性判断。对于每个查询，医学专家应检查排名最高的队列的常见精确度错误（假阳性）和排名最低的五个常见召回错误（假阴性）。将使用实时临床数据进行用户确认测试，反馈将提高队列识别系统的质量。EEG的注释大数据存档的存在将大大增加希望研究EEG数据的神经科学，生物工程和医学信息学非专家的可访问性。通过开发高效的自动化数据处理技术来创建此资源，将证明大数据生物工程应用程序的范围更加广泛。