Using preference learning for detecting inconsistencies in clinical practice guidelines: Methods and application to antibiotherapy

Autor: Jean-Baptiste Lamy, Karima Sedki, Rosy Tsopra
Přispěvatelé: Laboratoire d'Informatique Médicale et Ingénierie des Connaissances en e-Santé (LIMICS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Sorbonne Paris Nord, Université Paris 13 (UP13)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM), This work was supported by the French drug agency (ANSM, Agence Nationale de Sécurité du Médicament et des produits de santé) through the RaMiPA project AAP 2016., Lamy, Jean-Baptiste, Laboratoire d'Automatique, de Mécanique et d'Informatique industrielles et Humaines - UMR 8201 (LAMIH), Université de Valenciennes et du Hainaut-Cambrésis (UVHC)-Centre National de la Recherche Scientifique (CNRS)-INSA Institut National des Sciences Appliquées Hauts-de-France (INSA Hauts-De-France)
Jazyk: angličtina
Rok vydání: 2018
Předmět:
020205 medical informatics
Computer science
Knowledge Bases
[SDV]Life Sciences [q-bio]
Medicine (miscellaneous)
02 engineering and technology
[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Machine Learning
0302 clinical medicine
MESH: Practice Guidelines as Topic
[SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases
0202 electrical engineering
electronic engineering
information engineering
Data Mining
030212 general & internal medicine
Preference learning
Practice Patterns
Physicians'
MESH: Machine Learning
Management science
Inconsistencies in guidelines
Bacterial Infections
Preference
Anti-Bacterial Agents
3. Good health
Clinical Practice
Antibiotherapy
Knowledge base
Practice Guidelines as Topic
MESH: Guideline Adherence
[SDV.MHEP.MI] Life Sciences [q-bio]/Human health and pathology/Infectious diseases
Guideline Adherence
Clinical practice guidelines
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI]
MESH: Knowledge Bases
MESH: Bacterial Infections
Context (language use)
Primary care
Clinical decision support system
MESH: Primary Health Care
Decision Support Techniques
03 medical and health sciences
Artificial Intelligence
MESH: Anti-Bacterial Agents
Humans
[INFO]Computer Science [cs]
MESH: Humans
Primary Health Care
business.industry
MESH: Data Mining
MESH: Decision Support Techniques
Decision Support Systems
Clinical
[SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie
MESH: Practice Patterns
Physicians'
MESH: Decision Support Systems
Clinical
[SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie
business
Zdroj: Artificial Intelligence in Medicine
Artificial Intelligence in Medicine, Elsevier, 2018, 89, pp.24-33. ⟨10.1016/j.artmed.2018.04.013⟩
Artificial Intelligence in Medicine, In press, 89, pp.24-33. ⟨10.1016/j.artmed.2018.04.013⟩
Artificial Intelligence in Medicine, Elsevier, In press, 89, pp.24-33. ⟨10.1016/j.artmed.2018.04.013⟩
ISSN: 0933-3657
DOI: 10.1016/j.artmed.2018.04.013⟩
Popis: International audience; 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.
Databáze: OpenAIRE
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