Multi-relational data mining: an introduction
ACM SIGKDD Explorations Newsletter
Improving the performance of dictionary-based approaches in protein name recognition
Journal of Biomedical Informatics - Special issue: Named entity recognition in biomedicine
Term identification in the biomedical literature
Journal of Biomedical Informatics - Special issue: Named entity recognition in biomedicine
Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data
Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
A hybrid approach to protein name identification in biomedical texts
Information Processing and Management: an International Journal
Tagging gene and protein names in full text articles
BioMed '02 Proceedings of the ACL-02 workshop on Natural language processing in the biomedical domain - Volume 3
Text Mining for Biology And Biomedicine
Text Mining for Biology And Biomedicine
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Hi-index | 0.00 |
This paper addresses the problem of extracting and processing relevant information from unstructured electronic documents of the biomedical domain. The documents are full scientific papers. This problem imposes several challenges, such as identifying text passages that contain relevant information, collecting the relevant information pieces, populating a database and a data warehouse, and mining these data. For this purpose, this paper proposes the IEDSS-Bio, an environment for Information Extraction and Decision Support System in Biomedical domain. In a case study, experiments with machine learning for identifying relevant text passages (disease and treatment effects, and patients number information on Sickle Cell Anemia papers) showed that the best results (95.9% accuracy) were obtained with a statistical method and the use of preprocessing techniques to resample the examples and to eliminate noise.