Automatic discovery and processing of EEG cohorts from clinical records

 描述(由申请者提供)：美国每家医院收集的电子病历(EMR)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、时间约束的测量(例如，生命体征)、多通道信号数据(例如，脑电)和图像数据(例如，磁共振)。如果利用得当，这些信息可能具有变革性。有关患者的医疗问题、治疗和临床病程的信息对于进行比较有效性研究是必不可少的。发现能够进行比较研究的临床知识是这项提案的主要目标。我们将专注于对坦普尔大学医院12年来收集的临床脑电的自动解释(超过25,000次会议和15,000名患者)。临床医生将能够恢复 使用标准查询的相关EEG信号和EEG报告(例如“使用妥泰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EEG事件。我们将开发自动化技术，使用半监督学习来发现潜在的EEG事件并对其进行时间调整。在目标2中，我们将使用最先进的临床语言处理技术来处理EEG报告中的文本。临床概念及其类型、极性和形态应自动发现， 以及空间和时间信息。此外，我们将从脑电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，该系统将在AIMS 1和2中提取的临床知识上运行。此外，我们将在一个统一的表示中组织这些知识：合格的医学知识图(QMKG)，它将通过MapReduce使用BigData解决方案来构建。QMKG将能够被生物医学研究人员以及执业临床医生搜索。QMKG还将提供医生描述EEG中事件的方式的特征，并通过BigData资源对这些方式进行验证。EMKG代表着对基础科学的重要贡献。在目标4中，我们将通过收集将参与严格评估方案的临床医生和医科学生的反馈来验证患者队列识别系统的有用性。应设计查询的纳入和排除标准，专家将为结果提供相关性判断。对于每个查询，医学专家应检查排名最靠前的队列中常见的精确度错误(假阳性)和排名后五位的常见回忆错误(假阴性)。将使用现场临床数据进行用户验证测试，反馈将提高队列识别系统的质量。带有注释的脑电BigData档案的存在将极大地增加神经科学、生物工程和医学信息学方面的非专家对脑电数据的研究。通过开发高效的自动化数据争论技术创建这一资源将表明，BigData生物工程应用的范围更广，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.