Terminological Systems for Representing Patient Status

描述(由申请人提供)： 医疗是一个深刻的信息密集型行业，存在于 基于计算机的开发和部署环境 支持信息处理的技术正在以一种 前所未有的速度。不幸的是，缺乏一致性和可比性 术语数据仍然是充分实现 这些新技术的潜力。基于概念的术语术语 系统提供解决方案，强调基于概念的术语方法 而不是基于词汇的方法。 本提案的总体重点是基本的概念系统， 的术语数据的机器处理所需 “患者状态”的主题字段。我们使用术语患者状态来指代 身体、社会和认知功能的维度 健康、疾病和生活环境的背景。这个 该项目的具体目标是：1)确定和评估条款和 与患者状态相关的术语系统，2)描述术语 特定于患者状态识别和评估主题领域的系统， 以及3)将正式表示患者状态的术语系统与 参考术语和信息模型。为了实现这一目标，我们将 利用在梅奥诊所近十年的工作经验，这是强调的概念 支持临床自动标引的基于术语系统 记录，以及针对索引领域所做的大量试点工作 病人状态。 与强调纪律和纪律的术语发展不同 业务视角在建模计划中，我们采用了“自下而上”的方法 视角，强调患者地位的主体领域视角 通过汇编丰富的文本语料库。在研究结束时，我们 打算为该领域提供可扩展的资源。这项工作做了 不重复术语和信息建模工作中的任何现有工作， 相当广泛地建立在现有工作的基础上。

DESCRIPTION (provided by applicant): Health care is a profoundly information intensive industry, existing in an environment where the development and deployment of computer based technologies to support information processing is proceeding at an unprecedented pace. Unfortunately, the lack of consistent and comparable terminological data remains a significant barrier to fully realizing the potential of these new technologies. Concept based terminologies terminology systems offer solutions, emphasizing concept based approaches to terminology rather than lexical based approaches. The overall focus of this proposal is on the underlying concept systems that are required for the machine processing of terminological data related to the subject field of "patient status". We use the term patient status to refer to the dimensions of physical, social, and cognitive functioning within the context of health, disease, and the environment in which one lives. The specific aims of this project are to 1) identify and evaluate terms and terminology systems relevant to patient status, 2) describe a terminology system specific to the subject field of patient status identify and evaluate, and 3) compare a terminology system to formally represent patient status to reference terminology and information models. In accomplishing this, we will leverage nearly a decade of work at the Mayo Clinic that is emphasized concept based terminology systems in support of automated indexing of clinical records, and considerable pilot work specific to indexing the domain of patient status. In contrast to terminology developments that have emphasized disciplinary and business perspectives in modeling initiatives, we are employing a "bottom-up" perspective, emphasizing the subject field perspective of patient status through the assembly of rich textual corpora. At the end of this study, we intend to make available a scalable resource for this field. This work does not duplicate any existing work in terminology and information modeling work, rather extensively builds on existing work.