Dominic Girardi1, Sandra Wartner1, Gerhard Halmerbauer2, Margit Ehrenmüller2, Hilda Kosorus3, Stephan Dreiseitl4. 1. RISC Software GmbH, Research Unit Medical Informatics, Johannes Kepler University Linz/Hagenberg, Austria. 2. Department of Process Management in Health Care, University of Applied Sciences Upper Austria, Steyr, Austria. 3. Institute for Application Oriented Knowledge Processing, Johannes Kepler University Linz, Austria. 4. Department of Software Engineering, University of Applied Sciences Upper Austria, Hagenberg, Austria. Electronic address: stephan.dreiseitl@fh-hagenberg.at.
Abstract
OBJECTIVE: We introduce a new distance measure that is better suited than traditional methods at detecting similarities in patient records by referring to a concept hierarchy. MATERIALS AND METHODS: The new distance measure improves on distance measures for categorical values by taking the path distance between concepts in a hierarchy into account. We evaluate and compare the new measure on a data set of 836 patients. RESULTS: The new measure shows marked improvements over the standard measures, both qualitatively and quantitatively. Using the new measure for clustering patient data reveals structure that is otherwise not visible. Statistical comparisons of distances within patient groups with similar diagnoses shows that the new measure is significantly better at detecting these similarities than the standard measures. CONCLUSION: The new distance measure is an improvement over the current standard whenever a hierarchical arrangement of categorical values is available.
OBJECTIVE: We introduce a new distance measure that is better suited than traditional methods at detecting similarities in patient records by referring to a concept hierarchy. MATERIALS AND METHODS: The new distance measure improves on distance measures for categorical values by taking the path distance between concepts in a hierarchy into account. We evaluate and compare the new measure on a data set of 836 patients. RESULTS: The new measure shows marked improvements over the standard measures, both qualitatively and quantitatively. Using the new measure for clustering patient data reveals structure that is otherwise not visible. Statistical comparisons of distances within patient groups with similar diagnoses shows that the new measure is significantly better at detecting these similarities than the standard measures. CONCLUSION: The new distance measure is an improvement over the current standard whenever a hierarchical arrangement of categorical values is available.
Authors: Wolf E Hautz; Moritz M Kündig; Roger Tschanz; Tanja Birrenbach; Alexander Schuster; Thomas Bürkle; Stefanie C Hautz; Thomas C Sauter; Gert Krummrey Journal: Diagnosis (Berl) Date: 2021-10-21