Discovering and Applying Knowledge in Clinical Databases

描述（由申请人提供）： 随着改进的临床信息系统产品（例如，流动系统，订单输入系统），改进的数据输入技术（例如，语音识别，文本处理技术），以及数据交换标准的进一步采用，使得更多的机构开始生成电子病历，未来这些病历将在广度，深度和编码程度上得到扩展。这些记录主要用于个别病人的护理，但利用这些记录进行临床研究和质量职能的工作已经落后。主要挑战包括各种复杂数据以及缺失和不准确的数据。 我们建议继续开发和测试挖掘临床数据库的方法。一个特别的重点将是利用储存库中的大量信息（潜在的关联和知识），并使用计算机密集型技术和数据表示和操作方面的进步，以更好地解释数据库中的内容，并克服复杂，缺失和不准确的数据的挑战。我们假设，数据挖掘技术可以应用到一个存储库，以产生准确的临床解释。我们进一步假设，潜在的临床丰富的知识库中的协会可以用来改善该知识库中的病例分类。 我们的目标是开发方法来准备数据挖掘;在临床数据存储库中的信息的特点;开发基于自然语言处理器输出和信息检索技术的操作相似性措施;应用最近邻技术和基于案例的推理来改善分类;开发基于统计的方法来改善不完整或不准确数据的案例分类;并将我们的方法应用于真实的临床研究问题，并进行额外的数据挖掘研究。 鉴于该团队的经验，哥伦比亚大学医学信息学系的研究人员在开展这项研究方面具有独特的优势（数据挖掘，统计，健康数据组织，健康知识表示，自然语言处理），200万患者13年数据库的可用性，以及一个名为MedLEE的自然语言处理器的可用性，它可以将数百万份叙述性报告转换为编码丰富的临床数据。

DESCRIPTION (provided by applicant): With the advent of improved clinical information system products (e.g., ambulatory systems, order entry systems), improved data entry technologies (e.g., speech recognition, text processing techniques), and further adoption of data interchange standards, more institutions are generating electronic medical records, and these records will expand in breadth, depth, and degree of coding in the future. The records are used mainly for individual patient care, but exploiting the records for clinical research and quality functions has lagged behind. Major challenges include the wide range of complex data and missing and inaccurate data. We propose to continue our work to develop and test methods to mine a clinical data repository. A special emphasis will be to exploit the vast amount of information in the repository (latent associations and knowledge) and to use computer intensive techniques and advances in data representation and manipulation to better interpret what is in the database and to overcome the challenges of complex, missing, and inaccurate data. We hypothesize that data mining techniques can be applied to a repository to generate accurate clinical interpretations. We further hypothesize that associations latent in a clinically rich repository can be used to improve the classification of cases in that repository. We aim to develop methods to prepare data for mining; to characterize the information in the clinical data repository; to develop similarity measures based on manipulation of natural language processor output and on information retrieval techniques; to apply nearest neighbor technique and case-based reasoning to improve classification; to develop a statistically based method to improve classification of cases with incomplete or inaccurate data; and to apply our methods to real clinical research questions and carry out additional data mining research. The researchers in the Department of Medical Informatics at Columbia University are uniquely positioned to carry out this research, given the experience of the team (data mining, statistics, health data organization, health knowledge representation, natural language processing), the availability of a repository of 13 years of data on 2 million patients, and the availability of a natural language processor called MedLEE to convert millions of narrative reports into richly coded clinical data.