Examinando por Autor "Jojoa Acosta, Mario Fernando"

Mostrando 1 - 7 de 7
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    Analysis of the effects of lockdown on staff and students at universities in Spain and Colombia using natural language processing techniques
    (MDPI, 2022-05-07) Jojoa Acosta, Mario Fernando; García-Zapirain, Begoña; González Reyes, Marino J. ; Pérez Villa, Bernardo; Urizar Mayora, Elena; Ponce Marquez, Sara; Tobar Blandón, Maria Fernanda
    The aim of this study is to analyze the effects of lockdown using natural language processing techniques, particularly sentiment analysis methods applied at large scale. Further, our work searches to analyze the impact of COVID-19 on the university community, jointly on staff and students, and with a multi-country perspective. The main findings of this work show that the most often related words were “family”, “anxiety”, “house”, and “life”. Besides this finding, we also have shown that staff have a slightly less negative perception of the consequences of COVID-19 in their daily life. We have used artificial intelligence models such as swivel embedding and a multilayer perceptron as classification algorithms. The performance that was reached in terms of accuracy metrics was 88.8% and 88.5% for students and staff, respectively. The main conclusion of our study is that higher education institutions and policymakers around the world may benefit from these findings while formulating policy recommendations and strategies to support students during this and any future pandemics.
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    Application of machine learning techniques to help in the feature selection related to hospital readmissions of suicidal behavior
    (Springer, 2024-02) Castillo Sánchez, Gema; Jojoa Acosta, Mario Fernando; García-Zapirain, Begoña; Torre Díez, Isabel de la; Franco Martín, Manuel Ángel
    Suicide was the main source of death from external causes in Spain in 2020, with 3,941 cases. The importance of identifying those mental disorders that influenced hospital readmissions will allow us to manage the health care of suicidal behavior. The feature selection of each hospital in this region was carried out by applying Machine learning (ML) and traditional statistical methods. The results of the characteristics that best explain the readmissions of each hospital after assessment by the psychiatry specialist are presented. Adjustment disorder, alcohol abuse, depressive syndrome, personality disorder, and dysthymic disorder were selected for this region. The most influential methods or characteristics associated with suicide were benzodiazepine poisoning, suicidal ideation, medication poisoning, antipsychotic poisoning, and suicide and/or self-harm by jumping. Suicidal behavior is a concern in our society, so the results are relevant for hospital management and decision-making for its prevention
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    Are AI systems biased against the poor?: a machine learning analysis using Word2Vec and GloVe embeddings
    (Springer Science and Business Media Deutschland GmbH, 2024-04) Curto Rex, Georgina; Jojoa Acosta, Mario Fernando; Comim, Flavio; García-Zapirain, Begoña
    Among the myriad of technical approaches and abstract guidelines proposed to the topic of AI bias, there has been an urgent call to translate the principle of fairness into the operational AI reality with the involvement of social sciences specialists to analyse the context of specific types of bias, since there is not a generalizable solution. This article offers an interdisciplinary contribution to the topic of AI and societal bias, in particular against the poor, providing a conceptual framework of the issue and a tailor-made model from which meaningful data are obtained using Natural Language Processing word vectors in pretrained Google Word2Vec, Twitter and Wikipedia GloVe word embeddings. The results of the study offer the first set of data that evidences the existence of bias against the poor and suggest that Google Word2vec shows a higher degree of bias when the terms are related to beliefs, whereas bias is higher in Twitter GloVe when the terms express behaviour. This article contributes to the body of work on bias, both from and AI and a social sciences perspective, by providing evidence of a transversal aggravating factor for historical types of discrimination. The evidence of bias against the poor also has important consequences in terms of human development, since it often leads to discrimination, which constitutes an obstacle for the effectiveness of poverty reduction policies
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    Flow prediction in sound-based uroflowmetry
    (Nature Research, 2025-01-03) Álvarez Arteaga, Marcos Lázaro; Arjona Aguilera, Laura; Jojoa Acosta, Mario Fernando; Bahillo, Alfonso
    Sound-based uroflowmetry (SU) offers a non-invasive alternative to traditional uroflowmetry (UF) for evaluating lower urinary tract dysfunctions, enabling home-based testing and reducing the need for clinic visits. This study compares SU and UF in estimating urine flow rate and voided volume in 50 male volunteers (aged 18–60), with UF results from a Minze uroflowmeter as the reference standard. Audio signals recorded during voiding were segmented and machine learning algorithms (gradient boosting, random forest, and support vector machine) estimated flow parameters from three devices: Ultramic384k, Mi A1 smartphone, and Oppo smartwatch. The mean absolute error for flow rate estimation were 2.6, 2.5 and 2.9 ml/s, with R2 values of 84%, 83%, and 79%, respectively. Analysis of the Ultramic384k’s frequency range showed that the 0–8 kHz band contained 83% of significant components, suggesting higher sampling frequencies are unnecessary. A 1000 ms segment size was optimal for balancing computational efficiency and accuracy. Lin’s concordance coefficients for urine flow and voided volume using the smartwatch (0–8 kHz, 1000 ms) were 0.9 and 0.85, respectively, demonstrating that SU is a reliable, cost-effective alternative to UF for estimating key uroflowmetry parameters, with added patient convenience
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    The impact of COVID 19 on university staff and students from iberoamerica: online learning and teaching experience
    (MDPI AG, 2021-05-28) Jojoa Acosta, Mario Fernando; Lázaro Pérez, Esther; García-Zapirain, Begoña; González Reyes, Marino J.; Urizar Mayora, Elena
    The COVID-19 pandemic has created a great impact on mental health in society. Considering the little attention paid by scientific studies to either students or university staff during lockdown, the current study has two aims: (a) to analyze the evolution of mental health and (b) to identify predictors of educational/professional experience and online learning/teaching experience. (2) Methods: 1084 university students and 554 staff in total from four different countries (Spain, Colombia, Chile and Nicaragua) participated in the study, affiliated with nine different universities, four of them Spanish and one of which was online. We used an online survey known as LockedDown, which consists of 82 items, analyzed with classical multiple regression analyses and machine learning techniques. (3) Results: Stress level and feelings of anxiety and depression of students and staff either increased or remained over the weeks. A better online learning experience for university students was associated with the age, perception of the experience as beneficial and support of the university. (4) Conclusions: The study has shown evidence of the emotional impact and quality of life for both students and staff. For students, the evolution of feelings of anxiety and depression, as well as the support offered by the university affected the educational experience and online learning. For staff who experienced a positive professional experience, with access to services and products, the quality-of-life levels were maintained
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    Natural language processing analysis applied to COVID-19 open-text opinions using a distilBERT model for sentiment categorization
    (Springer Science and Business Media Deutschland GmbH, 2024-06) Jojoa Acosta, Mario Fernando; Eftekhar, Parvin; Nowrouzi-Kia, Behdin; García-Zapirain, Begoña
    COVID-19 is a disease that affects the quality of life in all aspects. However, the government policy applied in 2020 impacted the lifestyle of the whole world. In this sense, the study of sentiments of people in different countries is a very important task to face future challenges related to lockdown caused by a virus. To contribute to this objective, we have proposed a natural language processing model with the aim to detect positive and negative feelings in open-text answers obtained from a survey in pandemic times. We have proposed a distilBERT transformer model to carry out this task. We have used three approaches to perform a comparison, obtaining for our best model the following average metrics: Accuracy: 0.823, Precision: 0.826, Recall: 0.793 and F1 Score: 0.803
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    Sentiment analysis techniques applied to raw-text data from a csq-8 questionnaire about mindfulness in times of Covid-19 to improve strategy generation
    (MDPI AG, 2021-06-13) Jojoa Acosta, Mario Fernando; Castillo Sánchez, Gema; García-Zapirain, Begoña ; Torre Díez, Isabel de la; Franco Martín, Manuel Ángel
    The use of artificial intelligence in health care has grown quickly. In this sense, we present our work related to the application of Natural Language Processing techniques, as a tool to analyze the sentiment perception of users who answered two questions from the CSQ-8 questionnaires with raw Spanish free-text. Their responses are related to mindfulness, which is a novel technique used to control stress and anxiety caused by different factors in daily life. As such, we proposed an online course where this method was applied in order to improve the quality of life of health care professionals in COVID 19 pandemic times. We also carried out an evaluation of the satisfaction level of the participants involved, with a view to establishing strategies to improve future experiences. To automatically perform this task, we used Natural Language Processing (NLP) models such as swivel embedding, neural networks, and transfer learning, so as to classify the inputs into the following three categories: negative, neutral, and positive. Due to the limited amount of data available—86 registers for the first and 68 for the second—transfer learning techniques were required. The length of the text had no limit from the user’s standpoint, and our approach attained a maximum accuracy of 93.02% and 90.53%, respectively, based on ground truth labeled by three experts. Finally, we proposed a complementary analysis, using computer graphic text representation based on word frequency, to help researchers identify relevant information about the opinions with an objective approach to sentiment. The main conclusion drawn from this work is that the application of NLP techniques in small amounts of data using transfer learning is able to obtain enough accuracy in sentiment analysis and text classification stages