Browsing by Author "Braga, Ana Cristina"
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- Exploring Features to Classify Occupational Accidents in the Retail SectorPublication . Sena, Inês; Braga, Ana Cristina; Novais, Paulo; Fernandes, Florbela P.; Pacheco, Maria F.; Vaz, Clara B.; Lima, José; Pereira, Ana I.The Machine Learning approach is used in several application domains, and its exploitation in predicting accidents in occupational safety is relatively recent. The present study aims to apply different Machine Learning algorithms for classifying the occurrence or nonoccurrence of accidents at work in the retail sector. The approach consists of obtaining an impact score for each store and work unit, considering two databases of a retail company, the preventive safety actions, and the action plans. Subsequently, each score is associated with the occurrence or non-occurrence of accidents during January and May 2023. Of the five classification algorithms applied, the Support Vector Machine was the one that obtained the best accuracy and precision values for the preventive safety actions. As for the set of actions plan, the Logistic Regression reached the best results in all calculated metrics. With this study, estimating the impact score of the study variables makes it possible to identify the occurrence of accidents at work in the retail sector with high precision and accuracy.
- Integrated feature selection and classification algorithm in the prediction of work-related accidents in the retail sector: a comparative studyPublication . Sena, Inês; Lima, Laíres; Silva, Felipe G.; Braga, Ana Cristina; Novais, Paulo; Fernandes, Florbela P.; Pacheco, Maria F.; Vaz, Clara; Lima, José; Pereira, Ana I.Assessing the different factors that contribute to accidents in the workplace is essential to ensure the safety and well-being of employees. Given the importance of risk identification in hazard prediction, this work proposes a comparative study between different feature selection techniques (χ2 test and Forward Feature Selection) combined with learning algorithms (Support Vector Machine, Random Forest, and Naive Bayes), both applied to a database of a leading company in the retail sector, in Portugal. The goal is to conclude which factors of each database have the most significant impact on the occurrence of accidents. Initial databases include accident records, ergonomic workplace analysis, hazard intervention and risk assessment, climate databases, and holiday records. Each method was evaluated based on its accuracy in the forecast of the occurrence of the accident. The results showed that the Forward Feature Selection-Random Forest pair performed better among the assessed combinations, considering the case study database. In addition, data from accident records and ergonomic workplace analysis have the largest number of features with the most significant predictive impact on accident prediction. Future studies will be carried out to evaluate factors from other databases that may have meaningful information for predicting accidents.
- Perceção dos utentes acerca da terapêutica medicamentosa prescrita após consulta médica e após dispensa na farmáciaPublication . Pinto, Isabel C.; Coelho, Joana C.M.M.; Braga, Ana Cristina; Pereira, Paula; Cardoso, TiffanyA promoção do uso racional do medicamento é fundamental para assegurar a eficácia terapêutica e minimizar os riscos. É considerável o número de utentes que não compreende o tratamento prescrito, muitas vezes por ausência de informação verbal e/ou escrita aquando da consulta médica e dispensa na Farmácia, o que resulta em grandes dificuldades para uma correta terapia medicamentosa, levando à ineficácia do tratamento. Determinar a perceção do conhecimento sobre a terapêutica medicamentosa a realizar, após consulta médica e após dispensa na Farmácia, e identificar fatores relacionados. Este estudo transversal e descritivo-correlacional, obteve-se uma amostra de 150 utentes de centros de saúde e farmácias do norte de Portugal, 64,0% do sexo feminino e 36,0% do sexo masculino, com idades entre 18 e 90 anos (média de 57,1). foi aplicado um questionário de autopreenchimento, incluindo a escala de classificação da perceção do conhecimento (Frohlich, 2010). Na análise dos dados foi utilizada estatística descritiva e o teste t-Student, com nível de significância de 5%. A perceção do conhecimento sobre a terapêutica medicamentosa dos utentes é insuficiente tanto após dispensa na farmácia (70,7%) como após a consulta médica (70,7%), só uma minoria dos utentes tiveram um bom conhecimento após consulta médica (5,3%) e após dispensa na farmácia comunitária (2,7%). Das questões realizadas as que obtiveram nível de conhecimento mais baixo foram as relacionadas com o esquecimento de uma ou mais doses, as interações com medicamentos ou alimentos e os efeitos secundários. Não foram verificadas diferenças entre a perceção dos utentes da farmácia e da consulta médica (p=0,191), provavelmente devido ao limitado tamanho da amostra. Contrariamente ao esperado, a escolaridade não está associada à perceção do conhecimento sobre a terapêutica (centro de saúde p=0,842; farmácia p=0,307). A perceção do conhecimento da terapêutica medicamentosa é insuficiente, tanto após consulta médica e como após dispensa na farmácia. Não se encontraram diferenças entre a perceção do conhecimento entre os utentes de centros de saúde e de farmácias, provavelmente devido à dimensão limitada da amostra. A escolaridade não parece estar associada com a perceção do conhecimento sobre a terapêutica medicamentosa.
- Perception of users about the prescribed drug therapy after medical consultation and after pharmacy dispensingPublication . Pinto, Isabel C.; Coelho, Joana C.M.M.; Braga, Ana Cristina; Pereira, Paula; Cardoso, TiffanyPromoting rational use of medicines is crucial to ensure therapeutic efficacy. Many users do not understand prescribed treatment, often for lack of information during the medical consultation and pharmacy dispensing, which results in difficulties for correct drug therapy. Objectives: Determine the perception of knowledge about the drug therapy, after medical consultation and after Pharmacy dispensing, and identify related factors. Methods: This cross-sectional and study, had a sample of 150 users of health centers and pharmacies in the north of Portugal, 64% females and 36% males, aged between 18 and 90 years (mean 57). A self-administered questionnaire was applied, including knowledge perception scale (Frohlich'10). In data analysis was used descriptive statistics and t-student test (significance level 5%). Results: The perception of knowledge about drug therapy is insufficient either after medical consultation (70.7%) or after pharmacy dispensing (70.7%), only a minority of users had a good knowledge after medical consultation (5.3%) and after dispensing in community pharmacy (2.7%). The lowest knowledge was related with forgetting doses, drugs/food interactions and side effects. No differences were found between the perceptions of users of medical centers and pharmacy (p=0.191), neither between the educational level (health center p=0.842, p=0.307 pharmacy). Conclusions: The perception of knowledge about drug therapy is quite insufficient both after medical consultation and after pharmacy dispensing. There were no found differences between the perceptions of users of medical centers and pharmacy, probably due to the limited sample size. Contrary to expectation, the education level is not associated with the perception of knowledge about drugs therapy.
- Predicting Retail Store Transaction Patterns: A Comparison of ARIMA and Machine Learning ModelsPublication . Vaz, Clara B.; Sena, Inês; Braga, Ana Cristina; Novais, Paulo; Lima, José; Pereira, Ana I.Retail transactions represent sales of consumer goods, or final goods, by consumer companies. This sector faces security challenges due to the hustle and bustle of sales, affecting employees’ workload. In this context, it is essential to estimate the number of customers who will appear in the store daily so that companies can dynamically adjust employee schedules, aligning workforce capacity with expected demand. This can be achieved by forecasting transactions using past observations and forecasting algorithms. This study aims to compare the ARIMA time series algorithm with several Machine Learning algorithms to predict the number of daily transactions in different store patterns, considering data variability. The study identifies four typical store patterns based on these criteria using daily transaction data between 2019 and 2023 from all retail stores of the leading company in Portugal. Due to data variability and the results obtained, the algorithm that presents the most minor errors in predicting daily transactions is selected for each store. This study’s ultimate goal is to fill the gap in forecasting daily customer transactions and present a suitable forecasting model to mitigate risks associated with transactions in retail stores.