Automatic discovery and processing of EEG cohorts from clinical records

 描述（由申请人提供）：该国每家医院收集的电子病历 (EMR) 总共包含惊人的生物医学知识财富。 EMR 可以包括非结构化文本、时间受限测量（例如生命体征）、多通道信号数据（例如 EEG）和图像数据（例如 MRI）。如果利用得当，这些信息可能会带来变革。有关患者医疗问题、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。该提案的主要目标是发现能够进行比较研究的临床知识。我们将重点关注天普大学医院 12 年来收集的临床脑电图的自动解释（超过 25,000 次治疗和 15,000 名患者）。临床医生将能够检索 使用标准查询的相关脑电图信号和脑电图报告（例如“接受 Topamax 治疗的局灶性脑功能障碍的年轻患者”）。在目标 1 中，我们将自动注释有助于诊断的脑电图事件。我们将开发自动化技术，使用半监督学习来发现和时间调整潜在的脑电图事件。在目标 2 中，我们将使用最先进的临床语言处理技术处理脑电图报告中的文本。临床概念、它们的类型、极性和形态将被自动发现， 以及空间和时间信息。此外，我们将从脑电图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方式组织这些知识：合格的医学知识图（QMKG），该系统将通过 MapReduce 使用大数据解决方案构建。生物医学研究人员和执业临床医生将能够搜索 QMKG。 QMKG 还将提供医生描述脑电图事件的方式的特征，并通过大数据资源验证这些事件。 EMKG 代表了对基础科学的重要贡献。在目标 4 中，我们将通过收集参与严格评估方案的临床医生和医学生的反馈来验证患者队列识别系统的有用性。设计查询的纳入和排除标准，专家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靠前的队列的常见精度错误（假阳性）和排名靠后的五个常见召回错误（假阴性）。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。带注释的脑电图大数据档案的存在将大大增加想要研究脑电图数据的神经科学、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整理技术来创建该资源将证明更广泛的大数据生物工程应用程序现在是易于处理的。

 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.