Automated systems to identify relevant documents in product risk management

<p>Abstract</p> <p>Background</p> <p>Product risk management involves critical assessment of the risks and benefits of health products circulating in the market. One of the important sources of safety information is the primary literature, especially for newer products which regulatory authorities have relatively little experience with. Although the primary literature provides vast and diverse information, only a small proportion of which is useful for product risk assessment work. Hence, the aim of this study is to explore the possibility of using text mining to automate the identification of useful articles, which will reduce the time taken for literature search and hence improving work efficiency. In this study, term-frequency inverse document-frequency values were computed for predictors extracted from the titles and abstracts of articles related to three tumour necrosis factors-alpha blockers. A general automated system was developed using only general predictors and was tested for its generalizability using articles related to four other drug classes. Several specific automated systems were developed using both general and specific predictors and training sets of different sizes in order to determine the minimum number of articles required for developing such systems.</p> <p>Results</p> <p>The general automated system had an area under the curve value of 0.731 and was able to rank 34.6% and 46.2% of the total number of 'useful' articles among the first 10% and 20% of the articles presented to the evaluators when tested on the generalizability set. However, its use may be limited by the subjective definition of useful articles. For the specific automated system, it was found that only 20 articles were required to develop a specific automated system with a prediction performance (AUC 0.748) that was better than that of general automated system.</p> <p>Conclusions</p> <p>Specific automated systems can be developed rapidly and avoid problems caused by subjective definition of useful articles. Thus the efficiency of product risk management can be improved with the use of specific automated systems.</p

Results of the Ontology Alignment Evaluation Initiative 2021

The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity and use different evaluation modalities (e.g., blind evaluation, open evaluation, or consensus). The OAEI 2021 campaign offered 13 tracks and was attended by 21 participants. This paper is an overall presentation of that campaign

Une ontologie pour modéliser les bioagresseurs des plantes

National audienceLe projet ANR "Des Données aux Connaissances en Agronomie et Biodiversité" (D2KAB) met à disposition une archive de bulletins agricoles publiée sur le Web. Pour annoter les bulletins à l’aide des maladies et des bioagresseurs des culture, nous avons besoin d’une nouvelle ressource sémantique. Plusieurs ontologies et graphes de connaissances existent déjà sur le sujet mais ne couvrent pas l’intégralité de nos besoins. Nous avons donc développé une nouvelle ontologie "BioAGgressor Ontology" (BAGO) en réutilisant le plus possible des éléments d’ontologies existantes. Cette nouvelle ontologie a été développée en utilisant la méthodologie LOT en partenariat avec 4 experts en agriculture, entomologie et maladie des plantes

Une ontologie pour modéliser les bioagresseurs des plantes

National audienceLe projet ANR "Des Données aux Connaissances en Agronomie et Biodiversité" (D2KAB) met à disposition une archive de bulletins agricoles publiée sur le Web. Pour annoter les bulletins à l’aide des maladies et des bioagresseurs des culture, nous avons besoin d’une nouvelle ressource sémantique. Plusieurs ontologies et graphes de connaissances existent déjà sur le sujet mais ne couvrent pas l’intégralité de nos besoins. Nous avons donc développé une nouvelle ontologie "BioAGgressor Ontology" (BAGO) en réutilisant le plus possible des éléments d’ontologies existantes. Cette nouvelle ontologie a été développée en utilisant la méthodologie LOT en partenariat avec 4 experts en agriculture, entomologie et maladie des plantes