Objectives: To examine drug overdose records in Brazil from 2000 to 2020, analyzing trends over time in overdoses and overall sociodemographic characteristics of the deceased.
Methods: Using data from the Brazilian Mortality Information System (Sistema de Informações sobre Mortalidade), we identified records from 2000-2020 in which the underlying cause-of-death was one of the following codes: X40-X45 (accidental poisoning), X60-X65 (intentional poisoning), or Y10-Y15 (undetermined intentionality poisoning). The Brazilian dataset included 21,410 deaths.
This study explores the distribution of international, regional and national scientific output in health information and communication, indexed in the MEDLINE and LILACS databases, between 1996 and 2005. A selection of articles was based on the hierarchical structure of Information Science in MeSH vocabulary. Four specific domains were determined: health information, medical informatics, scientific communications on healthcare and healthcare communications.
View Article and Find Full Text PDFStud Health Technol Inform
April 2005
The representation of texts by term vectors with element values calculated by a TFIDF method yields to significant results in text similarity problems, such as finding related documents in bibliographic or full-text databases and identifying MeSH concepts from medical texts by lexical approach and also harmonizing journal citation in ISI/SciELO references and normalizing author's affiliation in MEDLINE. Our work considered "trigrams" as the terms (elements) of a term vector representing a text, according to the Trigram Phrase Matching published by the NLM's Indexing Initiative and its logarithmic Term Frequency-Inverse Document Frequency method for term weighting. Trigrams are overlapping 3-char strings from a text, extracted by a couple of rules, and a trigram matching method may improve the probability of identifying synonym phrases or similar texts.
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