ABSTRACT
Objective: To conduct a geospatial analysis of suicide deaths among young people in the state of Paraná, southern Brazil, and evaluate their association with socioeconomic and spatial determinants. Methods: Data were obtained from the Mortality Information System and the Brazilian Institute of Geography and Statistics. Data on suicide mortality rates (SMR) were extracted for three age groups (15-19, 20-24, and 25-29 years) from two 5-year periods (1998-2002 and 2008-2012). Geospatial data were analyzed through exploratory spatial data analysis. We applied Bayesian networks algorithms to explore the network structure of the socioeconomic predictors of SMR. Results: We observed spatial dependency in SMR in both periods, revealing geospatial clusters of high SMR. Our results show that socioeconomic deprivation at the municipality level was an important determinant of suicide in the youth population in Paraná, and significantly influenced the formation of high-risk SMR clusters. Conclusion: While youth suicide is multifactorial, there are predictable geospatial and sociodemographic factors associated with high SMR among municipalities in Paraná. Suicide among youth aged 15-29 occurs in geographic clusters which are associated with socioeconomic deprivation. Rural settings with poor infrastructure and development also correlate with increased SMR clusters.
Subject(s)
Humans , Male , Female , Adolescent , Adult , Young Adult , Vulnerable Populations/statistics & numerical data , Suicide, Completed/statistics & numerical data , Socioeconomic Factors , Time Factors , Brazil , Risk Factors , Bayes Theorem , Cities , Age Distribution , Spatio-Temporal AnalysisABSTRACT
OBJECTIVE: To conduct a geospatial analysis of suicide deaths among young people in the state of Paraná, southern Brazil, and evaluate their association with socioeconomic and spatial determinants. METHODS: Data were obtained from the Mortality Information System and the Brazilian Institute of Geography and Statistics. Data on suicide mortality rates (SMR) were extracted for three age groups (15-19, 20-24, and 25-29 years) from two 5-year periods (1998-2002 and 2008-2012). Geospatial data were analyzed through exploratory spatial data analysis. We applied Bayesian networks algorithms to explore the network structure of the socioeconomic predictors of SMR. RESULTS: We observed spatial dependency in SMR in both periods, revealing geospatial clusters of high SMR. Our results show that socioeconomic deprivation at the municipality level was an important determinant of suicide in the youth population in Paraná, and significantly influenced the formation of high-risk SMR clusters. CONCLUSION: While youth suicide is multifactorial, there are predictable geospatial and sociodemographic factors associated with high SMR among municipalities in Paraná. Suicide among youth aged 15-29 occurs in geographic clusters which are associated with socioeconomic deprivation. Rural settings with poor infrastructure and development also correlate with increased SMR clusters.
Subject(s)
Suicide, Completed/statistics & numerical data , Vulnerable Populations/statistics & numerical data , Adolescent , Adult , Age Distribution , Bayes Theorem , Brazil , Cities , Female , Humans , Male , Risk Factors , Socioeconomic Factors , Spatio-Temporal Analysis , Time Factors , Young AdultABSTRACT
Maintaining balance is a motor task of crucial importance for humans to perform their daily activities safely and independently. Studies in the field of Artificial Intelligence have considered different classification methods in order to distinguish healthy subjects from patients with certain motor disorders based on their postural strategies during the balance control. The main purpose of this paper is to compare the performance between Decision Tree (DT) and Genetic Programming (GP) - both classification methods of easy interpretation by health professionals - to distinguish postural sway patterns produced by healthy and stroke individuals based on 16 widely used posturographic variables. For this purpose, we used a posturographic dataset of time-series of center-of-pressure displacements derived from 19 stroke patients and 19 healthy matched subjects in three quiet standing tasks of balance control. Then, DT and GP models were trained and tested under two different experiments where accuracy, sensitivity and specificity were adopted as performance metrics. The DT method has performed statistically significant (P < 0.05) better in both cases, showing for example an accuracy of 72.8% against 69.2% from GP in the second experiment of this paper.
Subject(s)
Postural Balance , Stroke/diagnosis , Case-Control Studies , Decision Trees , Female , Humans , Male , Middle Aged , Sensitivity and SpecificityABSTRACT
Este artigo apresenta um Sistema Baseado em Conhecimentos (SBC) para auxiliar no Diagnóstico Clínico das Crises Epiléticas (CE). Foi baseado na classificaçäo por tipo de crise da International League Against Epilepsia/ILAE81. O objetivo do sistema é obter um conjunto de sintomas apresentado pelo paciente, classificar o tipo de crise e indicar um provável diagnóstico. Para fazer o tratamento da incerteza será utilizado o Teorema de Bayes. O modo de classificaçäo utilizou as técnicas da Inteligência Artificial Simbólica através do Shell Kappa-PC e o paradigma de Orientaçäo a objetos
Subject(s)
Humans , Male , Female , Bayes Theorem , Epilepsy/diagnosis , Artificial IntelligenceABSTRACT
Este artigo apresenta um Sistema Baseado em Conhecimentos (SBC) para auxiliar no Diagnostico Clinico das Crises Epilepticas (CE). Foi baseado na classificacao por tipo de crise da International League Against Epilepsia/ILAE81. O objetivo do sistema e obter um conjunto de sintomas apresentado pelo paciente, classificar o tipo de crise e indicar um provavel diagnostico. Para fazer o tratamento da incerteza sera utilizado o Teorema de Bayes. O modo de classificacao utilizou as tecnicas da Inteligencia Artificial Simbolica atraves do Shell Kappa-PC e o paradigma de Orientacao a Objetos.
Subject(s)
Seizures , Diagnosis , Expert Systems , Artificial Intelligence , Epilepsy , Diagnosis , Expert Systems , Artificial IntelligenceABSTRACT
Com o objetivo de desenvolver sistemas automatizados de aquisiçäo de conhecimento para auxiliar na classificaçäo de crises epilépticas, este artigo apresenta um Sistema de Auxílio ao Diagnóstico Clínico de Crises Epilépticas, baseado na metodologia KADS (Knowledge Acquisition and Design Structure). Os métodos utilizados pelas ferramentas, tanto para a aquisiçäo como representaçÝo do conhecimento, representam o escopo da Engenharia do Conhecimento. O objetivo do sistema é obter conjuntos de sintomas apresentados pelo paciente e descrever um provável diagnóstico. O modo de classificaçäo utiliza as técnicas de IA simbólica, através do "shell Kappa-PC"e foi baseado na classificaçäo por tipo de crise da Liga Internacional Contra Epilepsia/ILAE. O sistema encontra-se na fase de teste e manutençäo das regras que fazem parte do módulo de propagaçäo do foco epiléptico
