Filtering free-text medical data based on machine learning

Autor:	Georgy Kopanitsa, Dmitry Panfilov, Sofia Grechishcheva, Iuliia Lenivtceva
Rok vydání:	2021
Předmět:	Russian language Computer science business.industry Feature extraction Health records Accuracy improvement Machine learning computer.software_genre Data filtering Text messaging General Earth and Planetary Sciences Artificial intelligence business computer General Environmental Science
Zdroj:	Procedia Computer Science. 193:82-91
ISSN:	1877-0509
DOI:	10.1016/j.procs.2021.10.009
Popis:	This article describes the results of data filtering of electronic health records for patients diagnosed with aortic aneurysm in two different medical centers to prepare data for further feature extraction. The accuracy improvement of filtered data was achieved by using machine learning methods of classification and natural language processing methods, taking into account the specificity of Russian language. Based on accuracy and F-measure, two methods of data filtering were compared: 1) rule-based approach; 2) classification approach. The results show that the designed classification is appropriate in terms of accuracy for data filtering.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::364d69a6646fe822feba0ecb6907fa8a https://doi.org/10.1016/j.procs.2021.10.009 Zobrazit plný text záznamu