Discovering and Applying Knowledge in Clinical Databases

我们正在进行的项目“发现和应用临床数据库中的知识”的长期目标是从电子健康记录（EHR）中的数据中学习，并将这些知识应用于相关问题。越来越多的采用EHR有望为临床研究和信息学研究提供数据，但数据的二次使用受到限制。挑战包括记录的复杂性、不完整性和不准确性。我们建议从信息论的角度来研究EHR，将EHR作为一个值得研究的自然对象，并应用非线性时间序列分析的方法。用一个更好的 为了更好地理解记录，我们希望衡量和说明数据的完整性，并改进对数据的解释和使用。我们假设，我们可以使用一个正式的信息理论框架来描述电子健康记录，并且测量的属性可以帮助回答信息学和临床问题。 我们的目标是：（1）开发一个信息理论框架来表征电子健康记录，（2）使用信息理论框架来研究EHR和抽样问题，（3）使用框架和传统的数据挖掘来回答临床和信息学问题。我们将把EHR作为一个复杂的时间序列来处理，并使用单变量顺序互信息（一个变量的观测值预测未来观测值的程度）和所有变量（离散和连续）之间的成对互信息网络来描述记录中的信息。其结果将是记录的可预测性和临床特征之间的一组关联的度量。我们将使用可预测性结果来研究患者记录的完整性、临床医生采样率的适当性、离群数据点和患者紧急程度的变化。我们将使用可预测性和关联性将叙述性抽象与其主要数据联系起来，解释叙述性修饰语，聚类术语，寻找关联（在全表型关联研究的背景下），并进行定义表型特征和基于相互信息的监测的探索性分析。

The long term goal of our ongoing project, "Discovering and applying knowledge in clinical databases," is to learn from data in the electronic health record (EHR) and to apply that knowledge to relevant problems. The increasing adoption of the EHR promises to provide data for clinical research and informatics research, but secondary use of the data has been limited. Challenges include the complexity, incompleteness, and inaccuracy of the record. We propose to study the EHR from an information theoretic point of view, treating the EHR as a natural object worthy of study, and applying methods from non-linear time series analysis. Armed with a better understanding of the record, we hope to measure and account for data completeness and to improve interpretation and use of the data. We hypothesize that we can characterize an electronic health record using a formal information theoretic framework, and that the measured properties can help answer informatics and clinical questions. Our aims are to (1) develop an information theoretic framework for characterizing the electronic health record, (2) use the information theoretic framework to study EHR and sampling issues, and (3) use the framework and traditional data mining to answer clinical and informatics questions. We will approach the EHR as a complex time series and characterize the information in the record using univariate sequential mutual information (the degree to which observations of a variable predict future observations) and a network of pair-wise mutual information among all variables, discrete and continuous. The result will be a measure of the predictability of the record and a set of associations among clinical features. We will use the predictability results to study the completeness of a patient's record, the appropriateness of a clinician's sampling rate, outlier data points, and changes in patient acuity. We will use predictability and associations to link narrative abstractions with their primary data, to interpret narrative modifiers, to cluster terms, to find associations (in the context of phenome-wide association studies), and to carry out exploratory analyses of defining phenotype profiles and of mutual information-based surveillance.