EXTRAEEXTRacción de Asociaciones entre Enfermedades y otros conceptos médicos
- Pascual Carrasco, Mario
- Araujo Serna, Lourdes
- Martínez Romo, Juan
- Duque Fernández, Andrés
- López Ostenero, Fernando
- Sanchez de Madariaga, Ricardo
- Muñoz Carrero, Adolfo
ISSN: 1135-5948
Année de publication: 2019
Número: 63
Pages: 171-174
Type: Article
D'autres publications dans: Procesamiento del lenguaje natural
Résumé
This project aims to improve the techniques for extracting Association Rules (AR) between diseases, or between diseases and other medical concepts. These rules allow the representation of medical knowledge underlying a set of Electronic Medical Records (EHR). Particularly, we plan to explore semi-supervised techniques that allow us to achieve similar results to those obtained using supervised techniques, but requiring minimal supervision. The project intends to make significant progress in the selection of relevant AR, which may be applied in the health domain for developing diagnostic help systems, or for disease prevention.
Références bibliographiques
- Agrawal, R., T. Imielinski, y A. Swami. 1993. Mining association rules between sets of items in large databases. En Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, SIGMOD ’93, páginas 207–216, New York, NY, USA. ACM.
- Han, J., J. Pei, y Y. Yin. 2000. Mining frequent patterns without candidate generation. SIGMOD Rec., 29(2):1–12, Mayo.
- Kang’ethe, S. M. y P. W. Wagacha. 2014. Extracting diagnosis patterns in electronic medical records using association rule mining. International Journal of Computer Applications, 108(15):19–26, December. Full text available.
- Rashid, M. A., M. T. Hoque, y A. Sattar. 2014. Association rules mining based clinical observations. CoRR, abs/1401.2571.
- Stilou, S., P. D. Bamidis, N. Maglaveras, y C. Pappas. 2001. Mining association rules from clinical databases: An intelligent diagnostic process in healthcare. En MEDINFO 2001 - Proceedings of the 10th World Congress on Medical Informatics, September 2-5, 2001, London, UK, páginas 1399–1403.
- Witten, I. H., E. Frank, y M. A. Hall. 2011. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 3rd edición.