ABSTRACT. This work addresses the development of a novel computer-aided methodology for discourse analysis of TV newscasts. A TV newscast constitutes a particular type of discourse and has become a central part of the modern-day lives of millions of people. It is important to understand how this media content works and how it affects human life. To support the study of TV newscasts under the discourse analysis perspective, this work proposes a newscast structure to recover its main units and extract relevant data, named here as newscast discursive metadata (NDM). The NDM describes aspects, such as screen time and field size of newscasts' participants and themes addressed. Data mining and image analysis methods are used to extract and analyze the NDM of a dataset containing 41 editions of two Brazilian newscasts. The experimental results are promising, demonstrating the effectiveness of the proposed methodology.Keywords: journalism, computing, discursive metadata, discourse analysis.Análise semiodiscursiva de telejornais, baseada em mineração de dados e processamento de imagens RESUMO. Este artigo aborda a análise semiodiscursiva de telejornais, baseada em técnicas de mineração de dados e processamento de imagens. Um telejornal constitui um tipo específico de discurso e desempenha papel central no quotidiano de milhões de pessoas. Objetivando-se apoiar o estudo de telejornais, sob a perspectiva da análise do discurso, este trabalho propõe uma estrutura para telejornais que permite recuperar suas principais unidades constituintes e extrair dados para sua análise. Estes dados são denominados neste trabalho de Metadados Discursivos de Telejornais (MDTs). Os MDTs descrevem aspectos como capital visual e plano fílmico dos participantes de telejornais, temáticas abordadas, entre outros. Técnicas de mineração de dados e processamento de imagens são utilizadas para extrair e analisar os MDTs associados a uma base contendo 41 edições de dois telejornais brasileiros. Os resultados experimentais são promissores, e demonstram a eficácia e aplicabilidade da abordagem proposta.Palavras-chave: jornalismo, computação, metadados discursivos, análise do discurso.

Background In Belo Horizonte, a city with 3,000,000 inhabitants, a survey was performed in six hospitals, between July 2016 and June 2018, about surgical site infection (SSI) in patients undergoing clean surgery procedures. The main objective is to statistically evaluate such incidences and enable an analysis of the SSI predictive power, through MLP (Multilayer Perceptron) pattern recognition algorithms. Methods Through the Hospital Infection Control Committees (CCIH) of the hospitals, a data collection on SSI was carried out through the software SACIH - Automated System for Hospital Infection Control. So, three procedures were performed: a treatment of the collected database for use of intact samples; a statistical analysis on the profile of the collected hospitals; an evaluation of the predictive power of five types of MLPs (Backpropagation Standard, Momentum, Resilient Propagation, Weight Decay and Quick Propagation) for SSI prediction. The MLPs were tested with 3, 5, 7 and 10 neurons in the hidden layer and with a division of the database for the resampling process (65% or 75% for testing, 35% or 25% for validation). They were compared by measuring the AUC (Area Under the Curve - ranging from 0 to 1) presented for each of the configurations. Results From 45,990 records, 12,811 were able for analysis. The statistical analysis results were: the average age is 49 years old (predominantly between 30 and 50); the surgeries had an average time of 134.13 minutes; the average hospital stay is 4 days (from 0 to 200 days), the death rate reached 1% and the SSI 1.49%. A maximum prediction power of 0.742 was found. Conclusion There was a loss of 60% of the database samples due to the presence of noise. However, it was possible to have a relevant sample to assess the profile of these six hospitals. The predictive process, presented some configurations with results that reached 0.742, what promises the use of the structure for the monitoring of automated SSI for patients submitted to surgeries considered clean. To optimize data collection, enable other hospitals to use the prediction tool and minimize noise from the database, two mobile application were developed: one for monitoring the patient in the hospital and other for monitoring after hospital discharge. The SSI prediction analysis tool is available at www.nois.org.br. Disclosures All Authors: No reported disclosures

Background In the hospitals of Belo Horizonte (a city with more than 3,000,000 inhabitants), a survey was conducted between July 2016 and June 2018, focused on surgical site infection (SSI) in patients undergoing bariatric surgery procedures. The main objective is to statistically evaluate such incidences and enable a study of the prediction power of SSI through MLPs (Multilayer Perceptron), a pattern recognition algorithm. Methods Data were collected on SSI by the Hospital Infection Control Committees (CCIH) of the hospitals involved in the research. After data collection, three procedures were performed: a treatment of the database collected for the use of intact samples; a statistical analysis on the profile of the hospitals collected and; an assessment of the predictive power of five types of MLP (Backpropagation Standard, Momentum, Resilient Propagation, Weight Decay, and Quick Propagation) for SSI prediction. MLPs were tested with 3, 5, 7, and 10 hidden layer neurons and a database split for the resampling process (65% or 75% for testing, 35% or 25% for validation). They were compared by measuring AUC (Area Under the Curve - ranging from 0 to 1) presented for each of the configurations. Results From 3473 initial data, only 2491 were intact for analysis. Statistically, it was found that: the average age of the patients was 39 years (ranging from 16 to 65); the average duration of surgery was 138 minutes; and 0.8% of patients had SSI. Regarding the predictive power of SSI, the experiments have a minimum value of 0.350 and a maximum of 0.756. Conclusion Despite the loss rate of almost 30% of the database samples due to the presence of noise, it was possible to have a relevant sampling for the profile evaluation of Belo Horizonte hospitals. Moreover, for the predictive process, although some configurations have results that reached 0.755, which makes promising the use of the structure for automated SSI monitoring for patients undergoing bariatric surgery. To optimize data collection and enable other hospitals to use the SSI prediction tool (available in www.sacihweb.com), two mobile application were developed: one for monitoring the patient in the hospital and the other for monitoring after hospital discharge. Disclosures All Authors: No reported disclosures

Background A survey was conducted in three hospitals, between July 2016 and June 2018, about surgical site infection (SSI) in patients undergoing surgeries to correct aortic artery aneurysms in the city of Belo Horizonte, with more than 3,000,000 of inhabitants. The general objective is to statistically evaluate such incidences and enable an analysis of the predictive power of SSI, through MLP (Multilayer Perceptron) pattern recognition algorithms. Methods Through the Hospital Infection Control Committees (CCIH) of the hospitals involved in the research, data collection on SSI was carried out. Such data is used in the analysis during your routine SSI surveillance procedures. Thus, three procedures were performed: a treatment of the database collected for use of intact samples; a statistical analysis on the profile of the collected hospitals and; an assessment of the predictive power of five types of MLPs (Backpropagation Standard, Momentum, Resilient Propagation, Weight Decay and Quick Propagation) for SSI prediction. The MLPs were tested with 3, 5, 7 and 10 neurons in the hidden layer and with a division of the database for the resampling process (65% or 75% for testing, 35% or 25% for validation). They were compared by measuring the AUC (Area Under the Curve - ranging from 0 to 1) for each of the configurations. Results From 600 records, 575 were complete for analysis. It was found that: the average age is 68 years (from 24 to 98 years); the average hospital stay is 9 days (with a maximum of 127 days), the death rate reached 6.43% and the SSI rate 2.78%. A maximum prediction power of 0.75 was found. Conclusion There was a loss of 4% of the database samples due to the presence of noise. It was possible to evaluate the profile of the three hospitals. The predictive process presented configurations with results that reached 0.75, which promises the use of the structure for the monitoring of automated SSI for patients undergoing surgery to correct aortic artery aneurysms. To optimize data collection, enable other hospitals to use the prediction tool and minimize noise from the database, two mobile application were developed: one for monitoring the patient in the hospital and another for monitoring after hospital discharge. The SSI prediction analysis tool is available at www.nois.org.br. Disclosures All Authors: No reported disclosures

Background A survey was carried out in five hospitals, between July 2016 and June 2018, on surgical site infection (SSI) in patients undergoing infected surgery procedures, in the city of Belo Horizonte (3,000,000 inhabitants). The general objective is to statistically evaluate such incidences and enable an analysis of the SSI predictive power, through MLP (Multilayer Perceptron) pattern recognition algorithms. Methods Through the Hospital Infection Control Committees (CCIH) of the hospitals, a data collection on SSI was carried out. Such data is used in the analysis during your routine SSI surveillance procedures. So, three procedures were performed: a treatment of the collected database for use of intact samples; a statistical analysis on the profile of the collected hospitals and; an assessment of the predictive power of five types of MLPs (Backpropagation Standard, Momentum, Resilient Propagation, Weight Decay and Quick Propagation) for SSI prediction. The MLPs were tested with 3, 5, 7 and 10 neurons in the hidden layer and with a division of the database for the resampling process (65% or 75% for testing, 35% or 25% for validation). They were compared by measuring the AUC (Area Under the Curve - ranging from 0 to 1) presented for each of the configurations. Results From 1770 records, 810 were intact for analysis. It was found that: the average age is 53 years old (from 0 to 98 years old); the surgeries had an average time of approximately 140 minutes; the average hospital stay is 19 days, the death rate reached 10.86% and the SSI rate was 6.04%. A maximum prediction power of 0.729 was found. Conclusion There was a loss of 54% of the database samples due to the presence of noise. However, it was possible to have a relevant sample to assess the profile of these five hospitals. The predictive process, presented some configurations with results that reached 0.729, which promises the use of the structure for the monitoring of automated SSI for patients submitted to infected surgeries. To optimize data collection, enable other hospitals to use the prediction tool and minimize noise from the database, two mobile application were developed: one for monitoring the patient in the hospital and another for monitoring after hospital discharge. The SSI prediction analysis tool is available at www.nois.org.br. Disclosures All Authors: No reported disclosures