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)负责审查和了解患者广泛的病史，以便做出有关护理的知情决定。然而，多种因素阻碍了这一进程，包括：诊断测试和治疗的复杂性和数量不断增加，可能会向病历中添加更多信息的健康信息交换标准，以及需要在更短的时间内高效地看更多的患者。这些障碍可能会导致患者和提供者之间的对话受到抑制，甚至可能导致医疗差错。需要新的方法来帮助医疗保健提供者加快对患者病史的了解，总结关键信息。使用主题模型来汇总大型非结构化数据集合是一个不断增长的研究领域。然而，到目前为止，在使这些模型适应临床报告环境方面所做的工作很少。这项建议旨在开发一个主题模型和随后的可视化系统，用于自动汇总病历以支持PCP。 建议的工作有两个具体目标：1)创建自由文本临床文档的主题模型，该主题模型集成了上下文患者和文档级数据，并发现了多词概念；2)利用建议的模型来驱动Web应用程序，该Web应用程序包括用于自动汇总患者记录的面向概念、源和时间的视图。提出的模型的创新之处在于，它通过合并而独特地适应临床记录 人口统计和离散数据(例如，实验室结果)，这影响了文档中主题的发现，并允许适应每个患者的特定病史。作为这个项目的试验台，我们将收集心肌梗死(MI)、乳腺癌或肝硬变的编码医疗记录，因为这些患者将跨越一系列临床复杂性。我们估计这项研究将包括68539份患者记录。开发的主题模型将被集成到基于网络的可视化中，该可视化显示随着时间的推移与临床相关的主题以及其他相关的临床数据。PCP将对这种可视化进行评估，以衡量其对支持病历审查的效用。这项R21提案在临床数据主题模型的使用方面开辟了新的天地，并将为拟议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.