SCH: EXP: Discovering Patterns to Improve Health to Overcome Health Disparities

在住院期间使用多学科科学循证实践(EBP)指南可以帮助低收入和少数族裔人口恢复和维持健康，从而减少再次住院。然而，EBP指南可能并不是对所有人群都同样有效。国家要求所有卫生专业人员在2015年前实施可互操作的电子健康记录(EHR)，这为EHR数据的再利用提供了机会，以解决新的研究问题，这些问题探索患者特征和资源的模式、EBP干预(卫生专业人员在治疗患者时的行动)以及健康改善，例如，住院和后续治疗期间多学科EBP的有效性。这个探索性项目关注的是以下患者群体：(1)有相同的特定情况，例如严重败血症和感染性休克或糖尿病或糖尿病并发症患者，(2)因该情况或相关并发症而住院，(3)在随后的一段时间内作为门诊患者接受治疗，以及(4)有可识别的结果，例如再次住院、急诊室(ER)就诊、与该情况相关的死亡，或无需再住院而得到控制的情况。将对这些患者进行分析，以了解具有相同病情但结果不同的患者之间的差异，目标是(1)评估EBP指南是否起到了作用，(2)发现导致改善结果的干预措施，可能需要添加到EBP指南中。实现这些目标需要开发新的分析技术，以便从电子健康记录数据中获得对健康结果的洞察。该项目开发的算法和方法将推动健康信息学的发展，使研究人员能够从电子病历中相对原始和无组织的海量数据中提取患者健康和治疗随时间的演变的更高层次的视图，并使用这些信息来分析健康结果有利和不利的患者之间的差异。更具体地说，将开发新的技术和工具，以(1)创建总结患者、他们的环境和他们的治疗的重要特征的患者和干预简档，(2)在这些简档中找到组(簇)和模式，以及(3)使用简档、簇和模式来分析具有相同健康状况的患者之间的结果差异。实现这些目标带来了巨大的挑战。例如，出于研究和分析的目的，原始形式的电子病历数据大多是相对无组织和低级别的格式，例如，主要包含护理文档的流程图具有代表患者评估和结果以及实验室和其他诊断测试的多行数据。这就需要提取和总结与任务有关的信息。由于时间在这些数据中扮演着如此重要的角色，因此跨时间从数据中提取有用的特征是至关重要的。然而，所涉及的时间序列往往是不规则的。更广泛地说，并不是所有的患者都有相同的信息集，而且信息不是定期提供的。此外，可能需要在多个时间分辨率下查看数据，例如，血压突然升高，而不是几年后逐渐但有噪音的升高。人口子结构、特征类型的差异、结合特征之间先前依赖关系的知识以及不完整和缺失的数据引起了额外的复杂性。该项目将应对这些挑战。这些努力的成功将推动分类、聚类和模式挖掘领域的数据挖掘，以及各种类型的时态数据分析，包括趋势、变化点和异常检测。为该项目提出的新的模式挖掘方法将有助于产生生物医学研究人员在理解一些严重的健康问题和避免不良结果方面取得进展所需的洞察力。这种进展可能会推进个性化医疗保健，从而有可能改善人类健康并降低医疗保健成本。除了健康应用，这项工作还广泛而直接地应用于任何复杂系统，对于这些复杂系统，至少在不久的将来，为复杂实体建立全面的预测模型往往是不现实的，最好的希望是确定特定的模式，这些模式提供对实体或系统相对于某些特定感兴趣条件的当前或未来状态的洞察。例子包括交通和能源系统、企业和政府组织、生态系统、尖端机械和计算机/网络系统。拟议框架和算法的创建还将直接培训一些研究生和本科生在数据挖掘及其在分析健康数据方面的使用。该项目的成果将在计算机科学以及与目标应用相关的领域的各种会议和期刊上公布。

The use of multidisciplinary scientific evidence based practice (EBP) guidelines during hospitalization can assist low income and minority populations to regain and maintain health, thus reducing rehospitalization. However, EBP guidelines may not be equally effective across all populations. The national mandate for all health professionals to implement interoperable electronic health records (EHRs) by 2015 provides an opportunity for reuse of EHR data to address new research questions that explore patterns of patient characteristics and resources, EBP interventions (actions of health professionals in treatment of the patient), and improvement in health, e.g., the effectiveness of multi-disciplinary EBP during hospitalization and follow-up. This exploratory project is interested in groups of patients that (1) share a particular condition, e.g., severe sepsis and septic shock or patients with diabetes or diabetic complications, (2) were hospitalized for this condition or related complications, (3) were treated as an outpatient in a clinic during a succeeding period of time, and (4) have an identifiable outcome, e.g., rehospitalization, emergency room (ER) visits, death related to the condition, or condition under control without rehospitalization. These patients will be analyzed to understand the differences between patients with the same condition but different outcomes, with the goals of (1) evaluating whether EBP guidelines made a difference and (2) discovering interventions which lead to improvement in outcomes that may need to be added to EBP guidelines. Achievement of these goals requires the development of new analysis techniques for deriving insights into health outcomes from EHR data. The algorithms and approaches developed in this project will advance health informatics by enabling researchers to extract, from the relatively raw and unorganized mass of data in an EHR, a higher level view of the evolution of the patient's health and treatment over time and use that information to analyze the differences between patients with favorable and unfavorable health outcomes. More specifically, new techniques and tools will be developed to (1) create patient and intervention profiles that summarize important characteristics of the patient, their environment, and their treatment, (2) find groups (clusters) and patterns in these profiles, and (3) use the profiles, clusters, and patterns to analyze the differences in outcomes between patients with a common health condition. Achievement of these goals poses significant challenges. For instance, EHR data in its original form is, for research and analysis purposes, mostly in a relatively unorganized and low-level format, e.g., flowsheets, which contain primarily nursing documentation, have numerous rows of data representing patient assessments and results as well as laboratory and other diagnostic tests. This necessitates the extraction and summarization of information relevant for the task. Because time plays such an important role in this data, extracting useful features from the data across time is critical. However, the time series involved are often irregular. More generally, not all patients have the same set of information and information is not available at regular intervals. Furthermore, data may need to be viewed at multiple temporal resolutions, e.g., sudden increase in blood pressure versus gradual, but noisy increase over several years. Additional complexities arise from population substructure, differences in the types of features, incorporating knowledge of prior dependencies among features, and incomplete and missing data. This project will address these challenges. Success in these efforts will advance data mining in the areas of classification, clustering and pattern mining, as well as various types of temporal data analysis, including trend, change point, and anomaly detection. The novel pattern mining approaches proposed for this project will help generate the insights biomedical researchers need to make progress in understanding a number of serious health problems and avoiding poor outcomes. Such progress is likely to advance personalized health care and thus has the potential to improve human health and reduce health care costs. Beyond health applications, this work has broad and immediate applications to any complex system for which creating a comprehensive predictive model for complex entities is often unrealistic, at least in the near future, and the best that can be hoped for is to identify specific patterns that provide insight into the current or future state of an entity or system with respect to certain specific conditions of interest. Examples include transportation and energy systems, business and government organizations, ecosystems, sophisticated machinery, and computer / network systems. The creation of the proposed frameworks and algorithms will also directly train a number of graduate and undergraduate students in the areas of data mining and its use in analyzing health data. The results of this project will be presented in various conferences and journals in computer science, as well as those in domains related to the target applications.