Developing a pneumonia diagnosis ontology from multiple knowledge sources

Abstract

Background: Pneumonia is difficult to differentiate from other pulmonary diseases because it shares many symptoms with these diseases. Diagnosing pneumonia in clinical practice would benefit from having access to a codified representation of clinical knowledge. An ontology represents a well-established paradigm for such codification. Objectives: The goal of this research is to create Pneumonia Diagnosis Ontology (PNADO) that brings together the medical knowledge dispersed among multiple medical knowledge sources. Material and Methods: We used several clinical practice guidelines (CPGs) describing the pneumonia diagnostic process as a starting point in developing PNADO. Preliminary version of PNADO was subsequently expanded to cover a broader range of the concepts by reusing ontologies from Open Biological and Biomedical Ontology (OBO) Foundry and BioPortal. PNADO was evaluated by examining relevant concepts from the pneumonia-specific systematic reviews, using patient data from the MIMIC-III clinical dataset, and by clinical domain experts. Results: PNADO is a comprehensive ontology and has a rich set of classes and properties that cover different types of pneumonia, pathogens, symptoms, clinical signs, laboratory tests and imaging, clinical findings, complications, and diagnoses. Conclusion: PNADO unifies pneumonia diagnostic concepts from multiple knowledge sources. It is available in the BioPortal repository.

Coordinating virus research: The Virus Infectious Disease Ontology

“…Connecting VIDO to CIDO improves semantic interoperability among IDO Core-conformant infectious disease ontologies and, moreover, improves interoperability with other BFO-conformant ontologies, ranging from the OBO Foundry to numerous other ontology projects employing BFO as a top-level architecture. Consequently, our work provides researchers resources for gathering and coordinating life science data while avoiding issues that so frequently undermine automating integration and analyses of the data flood in which we so often find ourselves [124][125][126].…”

Knowledge Representation and Management 2022: Findings in Ontology Development and Applications

“…We obtained 387 papers after the section editors' initial screening. The section editors further reviewed these papers jointly and reached a consensus list of 15 papers, which were nominated as the candidate best papers [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. External reviewers, IMIA Yearbook editors and section editors further evaluated these 15 papers and finally selected two best papers (see Table 1).…”

“…In the candidate paper from Azzi et al [10], the authors have developed a Pneumonia Diagnosis Ontology (PNADO) leveraging clinical practice guidelines and reusing related ontologies from OBO Foundry and BioPortal. The PNADO was the first pneumonia diagnosis ontology to represent different aspects of pneumonia including subtypes, symptoms, and lab tests.…”

Cardiovascular Disease Preliminary Diagnosis Application Using SQL Queries: Filling Diagnostic Gaps in Resource-Constrained Environments

“…A personalized early diagnosis system based on artificial intelligence (AI), ontology, and other medical information processing systems may be a great prevention measure. Ontologies and ontology database models have been applied to the diagnosis of CVD [7,8] and other diseases such as pneumonia [9]. Ontologies are formal representations of knowledge that can be used to define the structure and content of a database, allowing the representation of complex and hierarchical relationships between different concepts [10,11].…”

“…Ontologies allow for encapsulating domain-specific knowledge in human-interpretable and machine-interpretable formats. These formats could conform to the diagnostic guidelines established by healthcare professionals, using a natural language that describes the symptoms of diseases [7][8][9]15,18]. A recently popular alternative is large language models, which are trained on large sets of textual data and generate human-like texts, including diagnostic recommendations; however, they do not demonstrate accurate medical understanding and can introduce the risk of spreading misinformation [19,20].…”