Using preference learning for detecting inconsistencies in clinical practice guidelines: Methods and application to antibiotherapy |
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| Affiliation: | 1. LIMICS, INSERM UMRS 1142, Université Paris 13, Sorbonne Université, F-75006 Paris, France;2. AP-HP, Paris, France;1. Center for Communication and Information Technology, University Hospital Erlangen, Glückstraße 11, 91054 Erlangen, Germany;2. Department of Medical Informatics, Biometrics and Epidemiology, Chair of Medical Informatics, Friedrich-Alexander-University Erlangen-Nuremberg, Wetterkreuz 13, 91058 Erlangen-Tennenlohe, Germany;3. Department of Anaesthesiology, University Hospital Erlangen, Krankenhausstraße 12, 91054 Erlangen, Germany;4. Department of Computer Science, Chair of Computer Science 6 (Data Management), Martensstraße 3, 91058 Erlangen, Germany;1. Faculty of Computer Science, Dalhousie University, 6050 University Ave, Halifax, NS, B3H 1W5, Canada;2. Faculty of Medicine, Dalhousie University, 1459 Oxford Street, Halifax, NS B3H 4R2, Canada;1. Department of Physics, Duke University, Durham, NC 27708-0305, USA;2. Frankfurt Institute for Advanced Studies (FIAS), Ruth-Moufang-Str. 1, 60438 Frankfurt am Main, Germany;3. SUBATECH, UMR 6457, Université de Nantes, Ecole des Mines de Nantes, IN2P3/CNRS, 4 rue Alfred Kastler, 44307 Nantes cedex 3, France;1. School of Electrical, Computer, and Telecommunication Engineering, Faculty of Engineering and Information Scinces, University of Wollongong, North Avenue, Wollongong, NSW, Australia;2. School of Medicine, Faculty of Scince, Medicine and Health, University of Wollongong, North Avenue, Wollongong, NSW, Australia;3. Department of Gastroenterology, The Hospital of Wollongong, Wollongong, NSW, Australia;1. University Panthéon-Sorbonne, 90 Rue de Tolbiac, 75634 Paris, France;2. University of Lorraine, Cité Universitaire, 57000 Metz, France;3. University of Ljubljana, Večna pot 113, 1000 Ljubljana, Slovenia;1. Section for Artificial Intelligence and Decision Support, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, A-1090 Vienna, Austria;2. Medexter Healthcare GmbH, Borschkegasse 7/5, A-1090 Vienna, Austria;1. Homer Warner Research Center, Intermountain Healthcare, 5171 South Cottonwood Street, Murray, UT 84107, USA;2. Department of Biomedical Informatics, University of Utah, 421 Wakara Way, Salt Lake City, UT 84108, USA;1. Department of Medicine & Clinical and Translational Science Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA;2. Center for Biomedical Informatics, Charles Drew University, 1748 E 118th Street, LSRNE N238, Los Angeles, CA 90059, USA |
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| Abstract: | Clinical practice guidelines provide evidence-based recommendations. However, many problems are reported, such as contradictions and inconsistencies. For example, guidelines recommend sulfamethoxazole/trimethoprim in child sinusitis, but they also state that there is a high bacteria resistance in this context. In this paper, we propose a method for the semi-automatic detection of inconsistencies in guidelines using preference learning, and we apply this method to antibiotherapy in primary care. The preference model was learned from the recommendations and from a knowledge base describing the domain.We successfully built a generic model suitable for all infectious diseases and patient profiles. This model includes both preferences and necessary features. It allowed the detection of 106 candidate inconsistencies which were analyzed by a medical expert. 55 inconsistencies were validated. We showed that therapeutic strategies of guidelines in antibiotherapy can be formalized by a preference model. In conclusion, we proposed an original approach, based on preferences, for modeling clinical guidelines. This model could be used in future clinical decision support systems for helping physicians to prescribe antibiotics. |
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| Keywords: | Preference learning Antibiotherapy Clinical practice guidelines Inconsistencies in guidelines |
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