Catherine Duclos1, Gian Luigi Cartolano, Michael Ghez, Alain Venot. 1. Laboratoire d'informatique médicale et de bioinformatique (LIM&BIO), UFR de Santé, Médecine et Biologie Humaine, Paris 13, Bobigny cedex, France. catherine.duclos@avc.ap-hop-paris.fr.
Abstract
OBJECTIVE: The aim of this study was to construct automatically a knowledge base concerning the pharmacodynamic properties of antibiotics and a visualization tool. DESIGN: The authors studied the various guidelines used to write the pharmacodynamics section of the Summary of Product Characteristics (SPC) for antibiotics and constructed a conceptual model of the information. Particular words, syntagms, and punctuation elements were marked in the SPC texts, and automatic extraction was then used to build a knowledge base. This base was used to create dynamic HTML tables displaying the activity spectra of the antibiotics. MEASUREMENTS: The authors analyzed the performances of automatic extraction (recall and precision). RESULTS: The conceptual pharmacodynamics model dealt with antibiotics, pathogens, susceptibility tests, and the prevalence of resistance. Automatic extraction had a recall rate of 97.9% and a precision of 96.2%. The tool displaying antibiotic spectra and resistance prevalences used color codes to identify differences in susceptibility. CONCLUSION: This tool can provide an overview of the prevalence of resistance as expressed in SPC in primary care settings. Its potential impact should be evaluated.
OBJECTIVE: The aim of this study was to construct automatically a knowledge base concerning the pharmacodynamic properties of antibiotics and a visualization tool. DESIGN: The authors studied the various guidelines used to write the pharmacodynamics section of the Summary of Product Characteristics (SPC) for antibiotics and constructed a conceptual model of the information. Particular words, syntagms, and punctuation elements were marked in the SPC texts, and automatic extraction was then used to build a knowledge base. This base was used to create dynamic HTML tables displaying the activity spectra of the antibiotics. MEASUREMENTS: The authors analyzed the performances of automatic extraction (recall and precision). RESULTS: The conceptual pharmacodynamics model dealt with antibiotics, pathogens, susceptibility tests, and the prevalence of resistance. Automatic extraction had a recall rate of 97.9% and a precision of 96.2%. The tool displaying antibiotic spectra and resistance prevalences used color codes to identify differences in susceptibility. CONCLUSION: This tool can provide an overview of the prevalence of resistance as expressed in SPC in primary care settings. Its potential impact should be evaluated.