Using clinical reasoning ontologies to make smarter clinical decision support systems: a systematic review and data synthesis

Abstract

Objective The study sought to describe the literature describing clinical reasoning ontology (CRO)–based clinical decision support systems (CDSSs) and identify and classify the medical knowledge and reasoning concepts and their properties within these ontologies to guide future research. Methods MEDLINE, Scopus, and Google Scholar were searched through January 30, 2019, for studies describing CRO-based CDSSs. Articles that explored the development or application of CROs or terminology were selected. Eligible articles were assessed for quality features of both CDSSs and CROs to determine the current practices. We then compiled concepts and properties used within the articles. Results We included 38 CRO-based CDSSs for the analysis. Diversity of the purpose and scope of their ontologies was seen, with a variety of knowledge sources were used for ontology development. We found 126 unique medical knowledge concepts, 38 unique reasoning concepts, and 240 unique properties (137 relationships and 103 attributes). Although there is a great diversity among the terms used across CROs, there is a significant overlap based on their descriptions. Only 5 studies described high quality assessment. Conclusion We identified current practices used in CRO development and provided lists of medical knowledge concepts, reasoning concepts, and properties (relationships and attributes) used by CRO-based CDSSs. CRO developers reason that the inclusion of concepts used by clinicians’ during medical decision making has the potential to improve CDSS performance. However, at present, few CROs have been used for CDSSs, and high-quality studies describing CROs are sparse. Further research is required in developing high-quality CDSSs based on CROs.

Formal representation of patients’ care context data: the path to improving the electronic health record

“…We conducted a multi-method study including the following steps: (1) identify an initial set of relationships from our prior work; 17 , 18 (2) annotate inpatient consult notes; (3) annotate outpatient visit notes; (4) annotate spoken communications during outpatient visits and verify whether the tuples from this annotation contain information communicated in the respective notes of these visits; and (5) compare the tuples used across the multiple data sources. These steps are described in detail in the subsequent sections.…”

“…The 3 authors independently reviewed the 163 relationships from our previous studies, 17 , 18 then met in person to reach consensus about an initial sample of relationships that seemed most likely, in our experience, to be useful for representing patients’ care context data as reported in clinical notes. We also reached consensus on the class (ie, the classification of a concept as finding, condition, intervention, etc.)…”

“… 16 We then identified an initial sample of relationships from clinical cases published in the literature. 17 Next, we conducted another systematic review to identify ontology properties (eg, concepts, relationships) from CDS systems that incorporate a formal ontology in their logic, 18 and found that although most published ontologies contain relationships useful for CDS, they do not always provide relationships suitable for representing patients’ care context. In this study, we expand our sample of relationships 17 , 18 by manually annotating patients’ care context data as represented in clinical notes and spoken communications during outpatient visits.…”

Finding Misstatement Accounts in Financial Statements through Ontology Reasoning

“…For a long time, ontology-based clinical support has always been an unavoidable topic [4]. Generally speaking, ontology-based disease diagnosis mainly refers to providing decision support for the disease diagnosis and treatment with ontology model [17].…”

“…In disease treatment, ontology-based decision support mainly focuses on cancer treatment [21] and antibiotic treatment [17,22,19]. Notably, Shen, Colloc et al (2018) [21] proposed a decision support system called DSS (Decision Support System).…”