OBJECTIVES: 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. MATERIALS AND METHODS: 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. RESULTS: 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. DISCUSSION: Initial testing of the ODMS demonstrates promising accuracy for the highly automated results and illustrates the route to model improvement. CONCLUSIONS: The use of medical knowledge, embedded in ontologies, to direct the initial development of diagnostic computing systems appears feasible.
OBJECTIVES: 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. MATERIALS AND METHODS: 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. RESULTS: 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. DISCUSSION: Initial testing of the ODMS demonstrates promising accuracy for the highly automated results and illustrates the route to model improvement. CONCLUSIONS: The use of medical knowledge, embedded in ontologies, to direct the initial development of diagnostic computing systems appears feasible.
Entities:
Keywords:
Data Mining; Diagnostic System; Ontology; Pneumonia
Authors: Nathan C Dean; Barbara E Jones; Jeffrey P Ferraro; Caroline G Vines; Peter J Haug Journal: JAMA Intern Med Date: 2013-04-22 Impact factor: 21.873