Development of a cardiac-centered frailty ontology

Abstract

BackgroundA 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.ResultsThe 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.ConclusionsWe 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.Electronic supplementary materialThe online version of this article (10.1186/s13326-019-0195-3) contains supplementary material, which is available to authorized users.

Design and Use of Semantic Resources: Findings from the Section on Knowledge Representation and Management of the 2020 International Medical Informatics Association Yearbook

“…Section editors achieved a first selection of 148 papers based on titles and abstracts. After a second review of this set of papers, including full text reviews, a selection of 15 candidate best papers was established 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 . Five reviewers reviewed these pre-selected papers to best four best papers 4 5 6 7 .…”

“…Several articles describe ontology designs [9,10,18]. First, Brenas et al, [9] describe the ins and outs of the Adverse Childhood Experiences (ACEs) Ontology for Mental Health Surveillance.…”

“…This ontology was created to be used by major actors in the ACEs community with different applications, from the diagnosis of individuals (and the prediction of potential negative outcomes), to the prevention of ACEs in a population (and the design of interventions and policies). Doing-Harris et al, [10] describe the development of a cardiac-centered frailty ontology. This ontology is designed to cover the portions of reality relevant to assess the patient frailty, with a focus on cardiac care decisions.…”

Feasibility of Extracting Meaningful Patient Centered Outcomes From the Electronic Health Record Following Critical Illness in the Elderly

“…This approach could be further developed into an ontology for free text data mining with automated Natural Language Processing (NLP). Such ontologies have been previously built to capture frailty in cardiac patients and functional status in patients with colorectal cancer ( 57 , 58 ). NLP and machine learning has also been extensively used to in studies for mental health ( 59 , 60 ).…”

Developing Cardiac Electrophysiology Ontology: Moving Towards Data Harmonization and Integration

“…Cardiac-centered Frailty Ontology has been developed as a machine-interoperable description to allow making decisions on patient treatment. The full hierarchy has three top classes (clinical history finding, instrument finding, and physical examination finding), 12 qualifiers, 156 concepts, with 246 terms [42]. Heart Rate Turbulence (HRT) ontology was developed by Ruiz et al based on SNOMED-CT codes for semantic interoperability's in her [43].…”