Mineração de dados e características da mortalidade infantil

Vianna, Rossana Cristina Xavier Ferreira; Moro, Cláudia Maria Cabral; Moysés, Samuel Jorge; Carvalho, Déborah Ribeiro; Nievola, Júlio César

doi:10.1590/s0102-311x2010000300011

Cited by 16 publications

(11 citation statements)

References 17 publications

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“…After examining the different data mining tools, the software WEKA version 3.6, was chosen mainly because of its characteristics: it is a user-friendly tool for health professionals and, as a free application, does not represent any additional cost [23]. …”

Section: Methodsmentioning

confidence: 99%

Applying data mining techniques to improve diagnosis in neonatal jaundice

Ferreira

Oliveira

Freitas

2012

BMC Med Inform Decis Mak

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BackgroundHyperbilirubinemia is emerging as an increasingly common problem in newborns due to a decreasing hospital length of stay after birth. Jaundice is the most common disease of the newborn and although being benign in most cases it can lead to severe neurological consequences if poorly evaluated. In different areas of medicine, data mining has contributed to improve the results obtained with other methodologies.Hence, the aim of this study was to improve the diagnosis of neonatal jaundice with the application of data mining techniques.MethodsThis study followed the different phases of the Cross Industry Standard Process for Data Mining model as its methodology.This observational study was performed at the Obstetrics Department of a central hospital (Centro Hospitalar Tâmega e Sousa – EPE), from February to March of 2011. A total of 227 healthy newborn infants with 35 or more weeks of gestation were enrolled in the study. Over 70 variables were collected and analyzed. Also, transcutaneous bilirubin levels were measured from birth to hospital discharge with maximum time intervals of 8 hours between measurements, using a noninvasive bilirubinometer.Different attribute subsets were used to train and test classification models using algorithms included in Weka data mining software, such as decision trees (J48) and neural networks (multilayer perceptron). The accuracy results were compared with the traditional methods for prediction of hyperbilirubinemia.ResultsThe application of different classification algorithms to the collected data allowed predicting subsequent hyperbilirubinemia with high accuracy. In particular, at 24 hours of life of newborns, the accuracy for the prediction of hyperbilirubinemia was 89%. The best results were obtained using the following algorithms: naive Bayes, multilayer perceptron and simple logistic.ConclusionsThe findings of our study sustain that, new approaches, such as data mining, may support medical decision, contributing to improve diagnosis in neonatal jaundice.

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Section: Methodsmentioning

confidence: 99%

Applying data mining techniques to improve diagnosis in neonatal jaundice

Ferreira

Oliveira

Freitas

2012

BMC Med Inform Decis Mak

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“…O estudo realizado por Vianna et al 26 utilizou mineração textual em conjunto com algoritmos de aprendizagem supervisionada, como árvores de decisão, para analisar dados do Sinasc e do SIM para identificar fatores associados à mortalidade infantil, verificando que fatores como gravidez na adolescência estariam associados a maiores taxas de mortalidade.…”

Section: Aprendizagem Supervisionadaunclassified

“…Kim et al 37 Nakamura et al 38 Vianna et al 26 Redes neurais Identificação de grupos de trabalhadores com altos riscos de pneumoconiose.…”

Section: áRvores De Decisãounclassified

Perspectivas do uso de mineração de dados e aprendizado de máquina em saúde e segurança no trabalho

Fernandes

Filho

2019

Rev. bras. saúde ocup.

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Resumo Introdução: a variedade, volume e velocidade de geração de dados (big data) possibilitam novas e mais complexas análises. Objetivo: discutir e apresentar técnicas de mineração de dados (data mining) e de aprendizado de máquina (machine learning) para auxiliar pesquisadores de Saúde e Segurança no Trabalho (SST) na escolha da técnica adequada para lidar com big data. Métodos: revisão bibliográfica com foco em data mining e no uso de análises preditivas com machine learning e suas aplicações para auxiliar diagnósticos e predição de riscos em SST. Resultados: a literatura indica que aplicações de data mining com algoritmos de machine learning para análises preditivas em saúde pública e em SST apresentam melhor desempenho em comparação com análises tradicionais. São sugeridas técnicas de acordo com o tipo de pesquisa almejada. Discussão: data mining tem se tornado uma alternativa cada vez mais comum para lidar com bancos de dados de saúde pública, possibilitando analisar grandes volumes de dados de morbidade e mortalidade. Tais técnicas não visam substituir o fator humano, mas auxiliar em processos de tomada de decisão, servir de ferramenta para a análise estatística e gerar conhecimento para subsidiar ações que possam melhorar a qualidade de vida do trabalhador.

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“…Data stored in the Excel® database can hide similarities that cannot be detected. Due to this the KDD process was used, Knowledge discovery in database, which comprises a technique used to identify such previously unknown information and extract it from the dataset, allowing strategic decision-making [11][12] .…”

Section: Kdd Processmentioning

confidence: 99%

Adverse Reactions to Amphotericin B in Adults - Data Mining

Loução¹,

Jacomini²,

Moroni³

et al. 2018

Rev. Bras. Farm. Hosp. Serv. Saúde

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Background: Amphotericin B deoxycholate (d-AmB) is a drug widely used in fungal infections. The adverse effects of this medicine may be acute (infusion-related) or late (cumulative dose-related). Objective: This study aims to evaluate the frequency of adverse reactions to amphotericin B deoxycholate (d-AmB) in adults in a University Hospital, describe the profile of drug use in the institution and evaluate the factors related to the development of reactions with the aid of data mining. Methods: A database with the characteristics of the patients and information about the adverse reactions was created. Then, data mining was performed with the aid of WEKA software. Results: Fifteen adult patients who had used d-AmB in the selected period were identified, being 60% male. The mean age was 41.8 years old. Only 6 patients concluded their treatments with d-AmB, the others had to interrupt the treatment due to some intercurrence. Among the acute phase reactions, the most frequent were vomiting, nausea, phlebitis, hyperthermia and headache, which were related to the drug infusion. As for subacute reactions, the most frequent were urea and creatinine elevation, and hypokalemia. 30 rules were identified after data mining: d-AmB longer-term use, even with normal initial creatinine and urea, they had a greater number of acute reactions; patients with no creatinine change during d-AmB treatment had less subacute reactions; higher number of nephrotoxic drugs use, in those with increased creatinine, they presented a greater number of subacute reactions. Conclusions: The results demonstrate that there is a relation between the time of use and the greater number of adverse reactions. This safety data help clinicians to make decisions.

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Mineração de dados e características da mortalidade infantil

Abstract: Abstract

Cited by 16 publications

References 17 publications

Applying data mining techniques to improve diagnosis in neonatal jaundice

Applying data mining techniques to improve diagnosis in neonatal jaundice

Perspectivas do uso de mineração de dados e aprendizado de máquina em saúde e segurança no trabalho

Adverse Reactions to Amphotericin B in Adults - Data Mining

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