Depressive symptoms in different populations emerge from factors that relate to the lifestyle and background of individuals. For the university student populations, it is important to understand how sociodemographic and lifestyle characteristics influence their mental health and contribute to the development of depressive symptoms in order to come up with effective support and targeted interventions. In this study, we apply unsupervised Machine Learning (ML) to identify depression-related patterns in university students using a set of behavioral and demographic features collected from wearable devices and questionnaires. Using a selected set of features that includes parental education, income, academic activity, and daily behavioral and physiological measures, we clustered student profiles and assessed how these structural factors relate to depressive symptoms measured through the Patient Health Questionnaire-9 (PHQ-9) outcome. Feature importance and ANOVA analyses were used to evaluate cluster formation and interpretability. Our findings suggest that behavioral features, such as walking activity levels and exercise patterns, have a major role in producing depression-related clusters. Moreover, we observed meaningful variability and associations between demographic factors, like parental education and study time, with the depression outcomes. This work contributes to the growing field of fair and explainable ML in mental health and emphasizes the potential of data-driven tools for personalized decision-making for mental health screening in students and younger populations.
Post-cardiac arrest coma, is a consequence of post-cardiac arrest anoxic brain injury (PCABI) that significantly impacts patient survival, with neurological prognosis guiding withdrawal of life-sustaining therapy (WLST). Continuous electroencephalography (EEG) facilitates real-time monitoring of brain activity. However, there is no consensus on which EEG-based patterns may link with recovery or deterioration. In this study, we applied Topological Data Analysis (TDA) to analyze EEG-derived functional connectivity (FC) networks, revealing that patients with poor outcomes exhibit higher network density and efficiency. These findings suggest that a network reorganization may be linked to worse prognoses, highlighting the potential of TDA as a complementary approach for brain network characterization in coma assessment.
This study explores an approach for customizing a large language model (LLM) for use in a medical context through fine-tuning. Structured cardiovascular knowledge and a dataset of cardiac risk cases—specifically, the Cardiovascular Disease Ontology (CVDO) and patient profiles from the Framingham dataset—were used for model adaptation. The objective was to customize a compact natural language model using different types of cardiovascular information to estimate cardiac risk. The results showed that the model’s responses varied depending on the training data, and integration analysis revealed that contextual training reshaped the model’s semantic structure. These findings highlight the model’s adaptability to different domains and the value of combining structured and semi-structured medical data sources to guide its performance.
Endoscopic assessment of the stomach is difficult since visibility is limited, food or gastric pathology may hinder exploration and the passive organ motion is variable. Artificial intelligence (AI) may improve the navigation experience, but it requires depth and pose annotations. Current public datasets do not provide these annotations and they are generated from a small number of synthetic stomach models, thereby restraining the possible scenarios. In this work, a realistic synthetic endoscopy dataset is generated from 3D stomach models reconstructed from real CT-stomachs (30 patients). Gastric wall textures were learned by generative adversarial networks (GANs) trained with mucosa patches extracted from real endoscopic images. A virtual camera and light source were animated to follow a retrograde trajectory within each stomach model, devised to simulate realistic endoscopic navigation. This simulation produced RGB frames, each paired with a depth map and annotated 6-DoF camera pose. An expert evaluation against a reference synthetic dataset [1] showed a preference for the proposed dataset in 80% of cases, with highest scores in the antrum and incisura regions. The dataset was used to fine-tune a depth and pose estimation network, achieving 86.26% depth accuracy and 0.289 cm mean translation error, and released for public access, available at https://doi.org/10.5281/zenodo.17178144.
This study aimed to assess the prognostic value of radiomic features extracted from cardiac magnetic resonance (CMR) perfusion imaging, both alone and in combination with clinical variables, for mortality prediction in patients with coronary artery disease (CAD). Logistic regression and random forest models were trained with class weighting and stratified cross-validation across the three main coronary territories: left anterior descending, right coronary, and left circumflex arteries. Models based solely on radiomic features achieved moderate predictive performance, with AUC values between 0.64 and 0.69. When clinical variables such as leukocyte count, C-reactive protein (CRP), BNP, infarct location, and ejection fraction were included, model performance improved significantly, reaching AUCs between 0.83 and 0.85. These findings demonstrate that integrating radiomic and clinical information enhances predictive accuracy and supports the use of CMR radiomics as a complementary tool for non-invasive prognosis assessment in CAD. Future multicenter studies and deep learning approaches are warranted to validate and generalize these results.
