Documentos donde el Tema es "Materias > Psicología"

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2024

Artículo Materias > Ingeniería
Materias > Psicología
Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Artículos y libros
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica
Universidad Internacional do Cuanza > Investigación > Producción Científica
Abierto Inglés Predicting depression intensity from microblogs and social media posts has numerous benefits and applications, including predicting early psychological disorders and stress in individuals or the general public. A major challenge in predicting depression using social media posts is that the existing studies do not focus on predicting the intensity of depression in social media texts but rather only perform the binary classification of depression and moreover noisy data makes it difficult to predict the true depression in the social media text. This study intends to begin by collecting relevant Tweets and generating a corpus of 210000 public tweets using Twitter public application programming interfaces (APIs). A strategy is devised to filter out only depression-related tweets by creating a list of relevant hashtags to reduce noise in the corpus. Furthermore, an algorithm is developed to annotate the data into three depression classes: ‘Mild,’ ‘Moderate,’ and ‘Severe,’ based on International Classification of Diseases-10 (ICD-10) depression diagnostic criteria. Different baseline classifiers are applied to the annotated dataset to get a preliminary idea of classification performance on the corpus. Further FastText-based model is applied and fine-tuned with different preprocessing techniques and hyperparameter tuning to produce the tuned model, which significantly increases the depression classification performance to an 84% F1 score and 90% accuracy compared to baselines. Finally, a FastText-based weighted soft voting ensemble (WSVE) is proposed to boost the model’s performance by combining several other classifiers and assigning weights to individual models according to their individual performances. The proposed WSVE outperformed all baselines as well as FastText alone, with an F1 of 89%, 5% higher than FastText alone, and an accuracy of 93%, 3% higher than FastText alone. The proposed model better captures the contextual features of the relatively small sample class and aids in the detection of early depression intensity prediction from tweets with impactful performances. metadata Rizwan, Muhammad; Mushtaq, Muhammad Faheem; Rafiq, Maryam; Mehmood, Arif; Diez, Isabel de la Torre; Gracia Villar, Mónica; Garay, Helena y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, monica.gracia@uneatlantico.es, helena.garay@uneatlantico.es, SIN ESPECIFICAR (2024) Depression Intensity Classification from Tweets Using FastText Based Weighted Soft Voting Ensemble. Computers, Materials & Continua, 78 (2). pp. 2047-2066. ISSN 1546-2226

2023

Artículo Materias > Educación
Materias > Comunicación
Materias > Psicología
Universidad Europea del Atlántico > Investigación > Producción Científica
Fundación Universitaria Internacional de Colombia > Investigación > Artículos y libros
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica
Universidad Internacional do Cuanza > Investigación > Producción Científica
Abierto Inglés Communication professionals are experiencing a growing level of exposure to traumatic events as a result of their involvement in the coverage of various tragedies, including accidents, climatic disasters, rights violations, and acts of terrorism. However, it is worth noting that journalism and communication university courses often lack comprehensive instruction on effectively managing emotional challenges, anxiety, trauma, self-care, and the prevention of vicarious trauma. The objective of this study is to assess the inclusion of emotional management within the curricula of Journalism and Communication programmes offered by two universities in Catalonia, namely the University of Barcelona and the Autonomous University of Barcelona. In order to accomplish this objective, a series of semi-structured interviews were carried out with a total of twelve (12) professors who specialise in the fields of Journalism and Communication. Additionally, a thorough analysis was conducted on a set of 97 study plan guides. The results indicate that none of the participants in the interviews possess knowledge regarding any existing training programmes focused on emotional management. Furthermore, they unanimously agree on the importance of implementing such courses. The study plans did not include any subjects that were specifically dedicated to the topic of emotional management. This study presents a set of strategies aimed at creating a cross-disciplinary teaching-learning model that offers a comprehensive educational experience for students. This entails integrating precise subject matter on the previously mentioned topics, fostering critical contemplation and discourse regarding emotions within the educational setting, and advocating for ethical and sound professional behaviours. metadata Escudero, Carolina; Prola, Thomas; Fraga, Leticia y Soriano Flores, Emmanuel mail SIN ESPECIFICAR, thomas.prola@uneatlantico.es, leticia.fraga@uneatlantico.es, emmanuel.soriano@uneatlantico.es (2023) Emotional Management in Journalism and Communication Studies. Social Space, 23 (2). pp. 507-534.

2018

Revista Materias > Psicología Universidad Europea del Atlántico > Investigación > Revistas Científicas
Fundación Universitaria Internacional de Colombia > Investigación > Revistas Científicas
Universidad Internacional Iberoamericana México > Investigación > Revistas Científicas
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Revistas Científicas
Universidad Internacional do Cuanza > Investigación > Revistas Científicas
Abierto Inglés MLS Psychology Research es una revista científica que tiene como finalidad publicar artículos originales de investigación y de revisión tanto en áreas básicas como aplicadas y metodológicas que supongan una contribución al progreso de cualquier ámbito de la psicología científica como objetivo principal. MLSPR acogerá a artículo que analicen la conducta y procesos mentales tanto de individuos como de grupos, y que abarque aspectos de la experiencia humana. MLSPR atenderá a diferentes enfoques dentro de la psicología: Psicología clínica, Psicoterapea, Psicología educativa, Psicología del desarrollo, Neuropsicología, Psicología social, etc. metadata SIN ESPECIFICAR mail mls@devnull.funiber.org (2018) MLS Psychology Research. [Revista]

Este listado fue generado el Wed Oct 30 23:43:29 2024 UTC.

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Performance of the 4C and SEIMC scoring systems in predicting mortality from onset to current COVID-19 pandemic in emergency departments

The evolution of the COVID-19 pandemic has been associated with variations in clinical presentation and severity. Similarly, prediction scores may suffer changes in their diagnostic accuracy. The aim of this study was to test the 30-day mortality predictive validity of the 4C and SEIMC scores during the sixth wave of the pandemic and to compare them with those of validation studies. This was a longitudinal retrospective observational study. COVID-19 patients who were admitted to the Emergency Department of a Spanish hospital from December 15, 2021, to January 31, 2022, were selected. A side-by-side comparison with the pivotal validation studies was subsequently performed. The main measures were 30-day mortality and the 4C and SEIMC scores. A total of 27,614 patients were considered in the study, including 22,361 from the 4C, 4,627 from the SEIMC and 626 from our hospital. The 30-day mortality rate was significantly lower than that reported in the validation studies. The AUCs were 0.931 (95% CI: 0.90–0.95) for 4C and 0.903 (95% CI: 086–0.93) for SEIMC, which were significantly greater than those obtained in the first wave. Despite the changes that have occurred during the coronavirus disease 2019 (COVID-19) pandemic, with a reduction in lethality, scorecard systems are currently still useful tools for detecting patients with poor disease risk, with better prognostic capacity.

Producción Científica

Pedro Ángel de Santos Castro mail , Carlos del Pozo Vegas mail , Leyre Teresa Pinilla Arribas mail , Daniel Zalama Sánchez mail , Ancor Sanz-García mail , Tony Giancarlo Vásquez del Águila mail , Pablo González Izquierdo mail , Sara de Santos Sánchez mail , Cristina Mazas Pérez-Oleaga mail cristina.mazas@uneatlantico.es, Irma Dominguez Azpíroz mail irma.dominguez@unini.edu.mx, Iñaki Elío Pascual mail inaki.elio@uneatlantico.es, Francisco Martín-Rodríguez mail ,

de Santos Castro

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DiabSense: early diagnosis of non-insulin-dependent diabetes mellitus using smartphone-based human activity recognition and diabetic retinopathy analysis with Graph Neural Network

Non-Insulin-Dependent Diabetes Mellitus (NIDDM) is a chronic health condition caused by high blood sugar levels, and if not treated early, it can lead to serious complications i.e. blindness. Human Activity Recognition (HAR) offers potential for early NIDDM diagnosis, emerging as a key application for HAR technology. This research introduces DiabSense, a state-of-the-art smartphone-dependent system for early staging of NIDDM. DiabSense incorporates HAR and Diabetic Retinopathy (DR) upon leveraging the power of two different Graph Neural Networks (GNN). HAR uses a comprehensive array of 23 human activities resembling Diabetes symptoms, and DR is a prevalent complication of NIDDM. Graph Attention Network (GAT) in HAR achieved 98.32% accuracy on sensor data, while Graph Convolutional Network (GCN) in the Aptos 2019 dataset scored 84.48%, surpassing other state-of-the-art models. The trained GCN analyzed retinal images of four experimental human subjects for DR report generation, and GAT generated their average duration of daily activities over 30 days. The daily activities in non-diabetic periods of diabetic patients were measured and compared with the daily activities of the experimental subjects, which helped generate risk factors. Fusing risk factors with DR conditions enabled early diagnosis recommendations for the experimental subjects despite the absence of any apparent symptoms. The comparison of DiabSense system outcome with clinical diagnosis reports in the experimental subjects was conducted using the A1C test. The test results confirmed the accurate assessment of early diagnosis requirements for experimental subjects by the system. Overall, DiabSense exhibits significant potential for ensuring early NIDDM treatment, improving millions of lives worldwide.

Producción Científica

Md Nuho Ul Alam mail , Ibrahim Hasnine mail , Erfanul Hoque Bahadur mail , Abdul Kadar Muhammad Masum mail , Mercedes Briones Urbano mail mercedes.briones@uneatlantico.es, Manuel Masías Vergara mail manuel.masias@uneatlantico.es, Jia Uddin mail , Imran Ashraf mail , Md. Abdus Samad mail ,

Alam

<a class="ep_document_link" href="/14278/1/s41746-024-01194-6.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>

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Clinical phenotypes and short-term outcomes based on prehospital point-of-care testing and on-scene vital signs

Emergency medical services (EMSs) face critical situations that require patient risk classification based on analytical and vital signs. We aimed to establish clustering-derived phenotypes based on prehospital analytical and vital signs that allow risk stratification. This was a prospective, multicenter, EMS-delivered, ambulance-based cohort study considering six advanced life support units, 38 basic life support units, and four tertiary hospitals in Spain. Adults with unselected acute diseases managed by the EMS and evacuated with discharge priority to emergency departments were considered between January 1, 2020, and June 30, 2023. Prehospital point-of-care testing and on-scene vital signs were used for the unsupervised machine learning method (clustering) to determine the phenotypes. Then phenotypes were compared with the primary outcome (cumulative mortality (all-cause) at 2, 7, and 30 days). A total of 7909 patients were included. The median (IQR) age was 64 (51–80) years, 41% were women, and 26% were living in rural areas. Three clusters were identified: alpha 16.2% (1281 patients), beta 28.8% (2279), and gamma 55% (4349). The mortality rates for alpha, beta and gamma at 2 days were 18.6%, 4.1%, and 0.8%, respectively; at 7 days, were 24.7%, 6.2%, and 1.7%; and at 30 days, were 33%, 10.2%, and 3.2%, respectively. Based on standard vital signs and blood test biomarkers in the prehospital scenario, three clusters were identified: alpha (high-risk), beta and gamma (medium- and low-risk, respectively). This permits the EMS system to quickly identify patients who are potentially compromised and to proactively implement the necessary interventions.

Producción Científica

Raúl López-Izquierdo mail , Carlos del Pozo Vegas mail , Ancor Sanz-García mail , Agustín Mayo Íscar mail , Miguel A. Castro Villamor mail , Eduardo René Silva Alvarado mail eduardo.silva@funiber.org, Santos Gracia Villar mail santos.gracia@uneatlantico.es, Luis Alonso Dzul López mail luis.dzul@uneatlantico.es, Silvia Aparicio Obregón mail silvia.aparicio@uneatlantico.es, Rubén Calderón Iglesias mail ruben.calderon@uneatlantico.es, Joan B. Soriano mail , Francisco Martín-Rodríguez mail ,

López-Izquierdo

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Novel model to authenticate role-based medical users for blockchain-based IoMT devices

The IoT (Internet of Things) has played a promising role in e-healthcare applications during the last decade. Medical sensors record a variety of data and transmit them over the IoT network to facilitate remote patient monitoring. When a patient visits a hospital he may need to connect or disconnect medical devices from the medical healthcare system frequently. Also, multiple entities (e.g., doctors, medical staff, etc.) need access to patient data and require distinct sets of patient data. As a result of the dynamic nature of medical devices, medical users require frequent access to data, which raises complex security concerns. Granting access to a whole set of data creates privacy issues. Also, each of these medical user need to grant access rights to a specific set of medical data, which is quite a tedious task. In order to provide role-based access to medical users, this study proposes a blockchain-based framework for authenticating multiple entities based on the trust domain to reduce the administrative burden. This study is further validated by simulation on the infura blockchain using solidity and Python. The results demonstrate that role-based authorization and multi-entities authentication have been implemented and the owner of medical data can control access rights at any time and grant medical users easy access to a set of data in a healthcare system. The system has minimal latency compared to existing blockchain systems that lack multi-entity authentication and role-based authorization.

Producción Científica

Shadab Alam mail , Muhammad Shehzad Aslam mail , Ayesha Altaf mail , Faiza Iqbal mail , Natasha Nigar mail , Juan Castanedo Galán mail juan.castanedo@uneatlantico.es, Daniel Gavilanes Aray mail daniel.gavilanes@uneatlantico.es, Isabel de la Torre Díez mail , Imran Ashraf mail ,

Alam

<a href="/14933/1/s41746-024-01194-6.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>

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Clinical phenotypes and short-term outcomes based on prehospital point-of-care testing and on-scene vital signs

Emergency medical services (EMSs) face critical situations that require patient risk classification based on analytical and vital signs. We aimed to establish clustering-derived phenotypes based on prehospital analytical and vital signs that allow risk stratification. This was a prospective, multicenter, EMS-delivered, ambulance-based cohort study considering six advanced life support units, 38 basic life support units, and four tertiary hospitals in Spain. Adults with unselected acute diseases managed by the EMS and evacuated with discharge priority to emergency departments were considered between January 1, 2020, and June 30, 2023. Prehospital point-of-care testing and on-scene vital signs were used for the unsupervised machine learning method (clustering) to determine the phenotypes. Then phenotypes were compared with the primary outcome (cumulative mortality (all-cause) at 2, 7, and 30 days). A total of 7909 patients were included. The median (IQR) age was 64 (51–80) years, 41% were women, and 26% were living in rural areas. Three clusters were identified: alpha 16.2% (1281 patients), beta 28.8% (2279), and gamma 55% (4349). The mortality rates for alpha, beta and gamma at 2 days were 18.6%, 4.1%, and 0.8%, respectively; at 7 days, were 24.7%, 6.2%, and 1.7%; and at 30 days, were 33%, 10.2%, and 3.2%, respectively. Based on standard vital signs and blood test biomarkers in the prehospital scenario, three clusters were identified: alpha (high-risk), beta and gamma (medium- and low-risk, respectively). This permits the EMS system to quickly identify patients who are potentially compromised and to proactively implement the necessary interventions.

Producción Científica

Raúl López-Izquierdo mail , Carlos del Pozo Vegas mail , Ancor Sanz-García mail , Agustín Mayo Íscar mail , Miguel A. Castro Villamor mail , Eduardo René Silva Alvarado mail eduardo.silva@funiber.org, Santos Gracia Villar mail santos.gracia@uneatlantico.es, Luis Alonso Dzul López mail luis.dzul@uneatlantico.es, Silvia Aparicio Obregón mail silvia.aparicio@uneatlantico.es, Rubén Calderón Iglesias mail ruben.calderon@uneatlantico.es, Joan B. Soriano mail , Francisco Martín-Rodríguez mail ,

López-Izquierdo