Doing-Harris Kristina, Bray Bruce E, Thackeray Anne, Shah Rashmee U, Shao Yijun, Cheng Yan, Zeng-Treitler Qing, Garvin Jennifer H, Weir Charlene
Nuance Communications, Burlington, MA, USA.
Division of Cardiovascular Medicine, University of Utah, Salt Lake City, UT, USA.
J Biomed Semantics. 2019 Jan 18;10(1):3. doi: 10.1186/s13326-019-0195-3.
A Cardiac-centered Frailty Ontology can be an important foundation for using NLP to assess patient frailty. Frailty is an important consideration when making patient treatment decisions, particularly in older adults, those with a cardiac diagnosis, or when major surgery is a consideration. Clinicians often report patient's frailty in progress notes and other documentation. Frailty is recorded in many different ways in patient records and many different validated frailty-measuring instruments are available, with little consistency across instruments. We specifically explored concepts relevant to decisions regarding cardiac interventions. We based our work on text found in a large corpus of clinical notes from the Department of Veterans Affairs (VA) national Electronic Health Record (EHR) database.
The full ontology has 156 concepts, with 246 terms. It includes 86 concepts we expect to find in clinical documents, with 12 qualifier values. The remaining 58 concepts represent hierarchical groups (e.g., physical function findings). Our top-level class is clinical finding, which has children clinical history finding, instrument finding, and physical examination finding, reflecting the OGMS definition of clinical finding. Instrument finding is any score found for the existing frailty instruments. Within our ontology, we used SNOMED-CT concepts where possible. Some of the 86 concepts we expect to find in clinical documents are associated with the properties like ability interpretation. The concept ability to walk can either be able, assisted or unable. Each concept-property level pairing gets a different frailty score. Each scored concept received three scores: a frailty score, a relevance to cardiac decisions score, and a likelihood of resolving after the recommended intervention score. The ontology includes the relationship between scores from ten frailty instruments and frailty as assessed using ontology concepts. It also included rules for mapping ontology elements to instrument items for three common frailty assessment instruments. Ontology elements are used in two clinical NLP systems.
We developed and validated a Cardiac-centered Frailty Ontology, which is a machine-interoperable description of frailty that reflects all the areas that clinicians consider when deciding which cardiac intervention will best serve the patient as well as frailty indications generally relevant to medical decisions. The ontology owl file is available on Bioportal at http://bioportal.bioontology.org/ontologies/CCFO .
以心脏为中心的衰弱本体论可以成为使用自然语言处理（NLP）评估患者衰弱程度的重要基础。在做出患者治疗决策时，尤其是在老年人、患有心脏疾病的患者或考虑进行大手术的情况下，衰弱是一个重要的考量因素。临床医生经常在病程记录和其他文档中报告患者的衰弱情况。在患者记录中，衰弱以多种不同方式记录，并且有许多不同的经过验证的衰弱测量工具，但各工具之间缺乏一致性。我们专门探讨了与心脏干预决策相关的概念。我们的工作基于从退伍军人事务部（VA）国家电子健康记录（EHR）数据库中的大量临床记录文本中找到的内容。
完整的本体论有156个概念，246个术语。它包括我们预期在临床文档中会出现的86个概念，以及12个限定值。其余58个概念代表层次分组（例如，身体功能发现）。我们的顶级类别是临床发现，它有子类临床病史发现、仪器检查发现和体格检查发现，这反映了开放式医学语义模型（OGMS）对临床发现的定义。仪器检查发现是指现有衰弱测量工具得出的任何分数。在我们的本体论中，我们尽可能使用了系统化医学命名法-临床术语（SNOMED-CT）概念。我们预期在临床文档中出现的86个概念中的一些与诸如能力解释等属性相关。行走能力的概念可以是能够、需协助或不能。每个概念-属性级别配对都有不同的衰弱分数。每个有分数的概念都获得三个分数：一个衰弱分数、一个与心脏决策的相关性分数以及一个在推荐干预后解决可能性的分数。该本体论包括十种衰弱测量工具的分数与使用本体论概念评估的衰弱之间的关系。它还包括将本体论元素映射到三种常见衰弱评估工具的工具项目的规则。本体论元素被用于两个临床NLP系统。
我们开发并验证了一个以心脏为中心的衰弱本体论，它是对衰弱的机器可互操作描述，反映了临床医生在决定哪种心脏干预最适合患者时所考虑的所有领域，以及通常与医疗决策相关的衰弱指标。该本体论的owl文件可在生物门户（Bioportal）上获取，网址为http://bioportal.bioontology.org/ontologies/CCFO 。
