A Topic Model and Visualization for Automatic Summarization of Patient Records

描述（由申请人提供）：初级保健医生（PCP）负责审查和了解广泛的患者病史，以便做出有关护理的明智决定。然而，各种因素阻碍了这一过程，包括：诊断测试和治疗的复杂性和数量不断增加，健康信息交换标准可能会向医疗记录中添加更多信息，以及需要在更短的时间内有效地看到更多患者。这些障碍可能导致患者和提供者之间的对话受到抑制，甚至可能导致医疗错误。需要新的方法来帮助加快医疗保健提供者对患者病史的理解，总结关键信息。使用主题模型来总结大型非结构化数据集合是一个不断发展的研究领域。然而，到目前为止，很少有工作已经做了调整这些模型的临床报告环境。该提案旨在开发一个主题模型和随后的可视化系统，用于自动总结医疗记录，以支持PCP。 两个具体的目标指导拟议的工作：1）创建一个主题模型的自由文本的临床文档，集成上下文的患者和文档级数据，并发现多字的概念;和2）利用拟议的模型来驱动一个Web应用程序，包括概念，源，和时间为导向的视图自动总结病人的记录。所提出的模型的创新之处在于，它是唯一适合于临床记录的纳入 人口统计和离散数据（例如，实验室结果），这会影响文档中主题的发现，并允许适应每个患者的特定病史。作为该项目的测试平台，我们将收集心肌梗死（MI）、乳腺癌或肝硬化编码的医疗记录，因为这些患者将跨越一系列临床复杂性。我们估计本研究将纳入68，539例患者记录。开发的主题模型将被集成到一个基于网络的可视化，显示临床相关的主题随着时间的推移，以及其他相关的临床数据。这种可视化将由PCP进行评价，以衡量其支持病史审查的效用。这个R21提案在临床数据的主题模型的使用方面开辟了新的天地，并将为所提出的模型的新应用提供未来的研究途径。

DESCRIPTION (provided by applicant): Primary care physicians (PCPs) are responsible for reviewing and understanding a wide spectrum of a patient's medical history in order to make informed decisions regarding care. However, a variety of factors impede this process, including: the increasing complexity and number of diagnostic tests and treatments, health information exchange standards that may add more information to the medical record, and the need to efficiently see more patients in less time. These obstructions can lead to an inhibition of dialogue between patients and providers, and possibly even medical errors. New methods are required to help expedite a healthcare provider's understanding of a patient's medical history, summarizing key information. The use of topic models for summarizing large, unstructured data collections is a growing area of research. However, to date little work has been done on adapting these models to the clinical reporting environment. This proposal seeks to develop a topic model and ensuing visualization system for automatically summarizing medical records to support PCPs. Two specific aims guide the proposed work: 1) to create a topic model of free-text clinical documents that integrates contextual patient- and document-level data, and discovers multi-word concepts; and 2) to utilize the proposed model to drive a web application that includes concept-, source-, and time-oriented views for automatically summarizing patient records. The proposed model's innovation is that it is uniquely adapted to clinical records by the incorporation of demographic and discrete data (e.g., lab results), which influences the discovery of topics in documents and allows for adaptation to each patient's specific history. As a test bed for this project, we will gather medical records coded with myocardial infarction (MI), breast cancer, or liver cirrhosis, as these patients will span a spectrum of clinical complexity. We estimate that 68,539 patient records will be included in this study. The developed topic model will be integrated into a web-based visualization that displays clinically pertinent topics over time, as well as other relevant clinical data. This visualization will be evaluated by PCPs to gauge its utility to support the review of medical histories. This R21 proposal breaks new ground in the use of topic models for clinical data, and will provide future avenues of research in new applications of the proposed model.