An ontology-driven, diagnostic modeling system

• Published in: Journal of the American Medical Informatics Association : JAMIA Vol. 20; no. e1; pp. e102 - e110
• Main Authors: Haug, P. J., Ferraro, J. P., Holmen, J., Wu, X., Mynam, K., Ebert, M., Dean, N., Jones, J.
• Format: Journal Article
• Language: English
• Published: England BMJ Publishing Group 01.06.2013
• Subjects: Algorithms; Artificial Intelligence; Decision Support Systems, Clinical; Emergency Service, Hospital; Humans; International Classification of Diseases; Pneumonia - diagnosis; Research and Applications; ROC Curve; Vocabulary, Controlled; Diagnostic System; Data Mining; Pneumonia; Ontology
• ISSN: 1067-5027; 1527-974X; 1527-974X
• DOI: 10.1136/amiajnl-2012-001376

To present a system that uses knowledge stored in a medical ontology to automate the development of diagnostic decision support systems. To illustrate its function through an example focused on the development of a tool for diagnosing pneumonia. We developed a system that automates the creation of diagnostic decision-support applications. It relies on a medical ontology to direct the acquisition of clinic data from a clinical data warehouse and uses an automated analytic system to apply a sequence of machine learning algorithms that create applications for diagnostic screening. We refer to this system as the ontology-driven diagnostic modeling system (ODMS). We tested this system using samples of patient data collected in Salt Lake City emergency rooms and stored in Intermountain Healthcare's enterprise data warehouse. The system was used in the preliminary development steps of a tool to identify patients with pneumonia in the emergency department. This tool was compared with a manually created diagnostic tool derived from a curated dataset. The manually created tool is currently in clinical use. The automatically created tool had an area under the receiver operating characteristic curve of 0.920 (95% CI 0.916 to 0.924), compared with 0.944 (95% CI 0.942 to 0.947) for the manually created tool. Initial testing of the ODMS demonstrates promising accuracy for the highly automated results and illustrates the route to model improvement. The use of medical knowledge, embedded in ontologies, to direct the initial development of diagnostic computing systems appears feasible.