Use of the C4.5 machine learning algorithm to test a clinical guideline-based decision support system.

International audienceWell-designed medical decision support system (DSS) have been shown to improve health care quality. However, before they can be used in real clinical situations, these systems must be extensively tested, to ensure that they conform to the clinical guidelines (CG) on which they are based. Existing methods cannot be used for the systematic testing of all possible test cases. We describe here a new exhaustive dynamic verification method. In this method, the DSS is considered to be a black box, and the Quinlan C4.5 algorithm is used to build a decision tree from an exhaustive set of DSS input vectors and outputs. This method was successfully used for the testing of a medical DSS relating to chronic diseases: the ASTI critiquing module for type 2 diabetes

Une expérience originale de test automatisé d'un système d'aide à la décision clinique basé sur les guides de bonnes pratiques

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An ontology of bacteria to help physicians to compare antibacterial spectra.

International audienceGeneral practitioners (GPs) may lack specialist microbiological knowledge, making it difficult for them to use documents concerning antibacterial spectra provided by French health authorities. We have developed a tool to help GPs to compare antibacterial spectra, based on an ontology of bacteria generated using OWL-DL language. This tool makes it possible to search for information concerning the antibiotic susceptibility of given bacteria, regardless of the way in which this information is expressed in the document. Applied to the whole document, the tool made 4528 spectra explicit, whereas only 3471 could be understood without microbiological reasoning. A preliminary study showed that the performance of this tool was similar to that of an expert microbiologist (94 to 98% correct responses) and better than that of unassisted GPs (84-90% correct responses)

An ontology of bacteria to help physicians to compare antibacterial spectra.

International audienceGeneral practitioners (GPs) may lack specialist microbiological knowledge, making it difficult for them to use documents concerning antibacterial spectra provided by French health authorities. We have developed a tool to help GPs to compare antibacterial spectra, based on an ontology of bacteria generated using OWL-DL language. This tool makes it possible to search for information concerning the antibiotic susceptibility of given bacteria, regardless of the way in which this information is expressed in the document. Applied to the whole document, the tool made 4528 spectra explicit, whereas only 3471 could be understood without microbiological reasoning. A preliminary study showed that the performance of this tool was similar to that of an expert microbiologist (94 to 98% correct responses) and better than that of unassisted GPs (84-90% correct responses)

An ontology of bacteria to help physicians to compare antibacterial spectra

General practitioners (GPs) may lack specialist microbiological knowledge, making it difficult for them to use documents concerning antibacterial spectra provided by French health authorities. We have developed a tool to help GPs to compare antibacterial spectra, based on an ontology of bacteria generated using OWL-DL language. This tool makes it possible to search for information concerning the antibiotic susceptibility of given bacteria, regardless of the way in which this information is expressed in the document. Applied to the whole document, the tool made 4528 spectra explicit, whereas only 3471 could be understood without microbiological reasoning. A preliminary study showed that the performance of this tool was similar to that of an expert microbiologist (94 to 98% correct responses) and better than that of unassisted GPs (84–90% correct responses)

Use of the C4.5 machine learning algorithm to test a clinical guideline-based decision support system.

International audienceWell-designed medical decision support system (DSS) have been shown to improve health care quality. However, before they can be used in real clinical situations, these systems must be extensively tested, to ensure that they conform to the clinical guidelines (CG) on which they are based. Existing methods cannot be used for the systematic testing of all possible test cases. We describe here a new exhaustive dynamic verification method. In this method, the DSS is considered to be a black box, and the Quinlan C4.5 algorithm is used to build a decision tree from an exhaustive set of DSS input vectors and outputs. This method was successfully used for the testing of a medical DSS relating to chronic diseases: the ASTI critiquing module for type 2 diabetes