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Automatically detect diagnostic patterns based on clinical notes through Text Mining
Author(s) -
João Ribeiro,
Júlio Duarte,
Filipe Portela,
Manuel Filipe Santos
Publication year - 2019
Publication title -
procedia computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.334
H-Index - 76
ISSN - 1877-0509
DOI - 10.1016/j.procs.2019.11.027
Subject(s) - computer science , field (mathematics) , word (group theory) , interpretation (philosophy) , artificial intelligence , natural language processing , cluster (spacecraft) , data science , data mining , information retrieval , machine learning , linguistics , philosophy , mathematics , pure mathematics , programming language
The importance of standardized treatment for patients is huge because it can reduce waiting times, costs in hospitals and make treatment more effective for patients. According to these patterns, the creation of a tool that can make the admission and interpretation of free text will become an important step in the medical field. For the analysis of the unstructured text, the "RapidMiner" tool was used. Following the text analysis, the word frequency technique will be used in the reports and the respective word counts, as well as the cluster analysis that allows the creation of combinations of words. For the modeling we used several Text Mining techniques focused on the main algorithms, since these are properly scientifically proven and that, normally, they are able to obtain better results.

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