A. Minard, K. Kaiser:
"Supporting Computer-interpretable Guidelines´ Modeling by Automatically Classifying Clinical Actions";
in: "Process Support and Knowledge Representation in Health Care", D. Riano, R. Lenz, S. Miksch, M. Peleg, M. Reichert, A. ten Teije (Hrg.); Springer Verlag, LNAI 8268, 2013, ISBN: 978-3-319-03915-2, S. 39 - 52.

Kurzfassung englisch:
Modeling computer-interpretable clinical practice guidelines is a complex and tedious task that has been of interest for several attempts to automate parts of this process. When modeling guidelines one of the tasks is to specify common actions in everyday´s practical medicine (e.g., drug prescription, observation) in order to link them with clinical information systems (e.g., an order-entry system). In this paper we compare a rule-based and a machine-learning method to classify activities according to the Clinical Actions Palette used in the Hybrid-Asbru ontology. We use syntactic and semantic features, such as the Semantic Types of the UMLS to classify the activities. Furthermore, we extend our methods by using 2-step classification and combining machine learning and rule-based approaches. Results show that machine learning performs better than the rule-based method on the classification task. They also show that the 2-step classification method improves the categorization of activities.

Clinical Practice Guidelines, Hybrid-Asbru, Common Clinical Actions, Natural Language Processing, Classification

"Offizielle" elektronische Version der Publikation (entsprechend ihrem Digital Object Identifier - DOI)

Elektronische Version der Publikation:

Zugeordnete Projekte:
Projektleitung Katharina Kaiser:
Unterstützung für Entwicklung und Transformation von medizinischen Leitlinien und Protokollen

Projektleitung Silvia Miksch:
MobiGuide: Führen von PatientInnen immer & überall

Erstellt aus der Publikationsdatenbank der Technischen Universität Wien.