Anxiety disorders refer to mental health conditions characterized by excessive and persistent worry, fear or dread, which can interfere with daily life. These disorders are pervasive worldwide but can be treated with psychotherapy, medication, or both. Detecting them in time is key to avoid further severe development after an initial crisis. In this paper, we present a solution based on self-monitoring, using wearables, smartphones and Machine Learning (ML) to assess users' anxiety and panic levels. This is the first paper to embrace and present a visualisation system for mental health focused on patients. User studies support the solution's quality. With this system, patients are empowered to control their situation better, helping the medical staff to get more insight into when and where a crisis occurs.

Asset management involves the coordinated activities of an organisation to derive value from assets, which may include physical assets. It encompasses activities related to design, construction, installation, operation, maintenance, renewal, and asset disposal. Asset management ensures the coordination of all activities, resources, and data related to physical assets. Recording and monitoring all maintenance activities is a key part of asset management, often done using work orders (WOs). Technicians typically create WOs using “free text”, which can result in missing or ungrammatical words, making it difficult to identify trends and analyse information. To standardise the terminology used for the same asset maintenance operation, this paper proposes a method that suggests words to technicians as they complete WOs. The word suggestion algorithm is based on past maintenance records, and a Bayesian network-based recommender system adapts to present needs verified by technicians using implicit user feedback. Implementing this system aims to normalise the terms used by technicians when filling in a WO. The corpus for this work comes from asset management records collected in a health facility in Portugal operated by a private company.

Stress has a great effect on people's lives that can not be understated. While it can be good, since it helps humans to adapt to new and different situations, it can also be harmful when not dealt with properly, leading to chronic stress. The objective of this paper is developing a stress monitoring solution, that can be used in real life, while being able to tackle this challenge in a positive way. The SMILE data set was provided to team Anxolotl, and all it was needed was to develop a robust model. We developed a supervised learning model for classification in Python, presenting the final result of 64.1% in accuracy and a f1-score of 54.96%. The resulting solution stood the robustness test, presenting low variation between runs, which was a major point for it's possible integration in the Anxolotl app in the future.

Introduction: The outcomes of reperfusion in ST-segment Elevation Myocardial Infarction (STEMI) are time-dependent, and percutaneous coronary intervention (PCI) should be performed within 60 minutes from hospital admission in PCI centers – door-to-balloon time (D2B). The association between Off-Hours Admission (OHA) and long-term outcomes is controversial when considering contemporary organized STEMI networks. Purpose: This study aims to analyze how OHA influences D2B and long-term mortality. Methods: Retrospective study of consecutive STEMI patients (pts), admitted in a PCI-centre with a local Emergency Department, between 2010 and 2015. Pts submitted to rescue-PCI were excluded. OHA was defined as admission at night (8p.m. to 8a.m), weekends and nonworking holidays. Predictors of OHA and D2B were studied by logistic regression analysis. Demographic, clinical, angiographic and procedural variables were evaluated using stepwise Cox regression analysis to determine independent predictors of 5-year all-cause mortality (5yM). The cumulative incidence of 5yM stratified by hours of admission was calculated according to the Kaplan-Meier method. Results: Of 901 pts, 472pts (52.4%) were admitted during off-hours. These pts were younger (61±13 vs 64±12, p=0.002) and had a lower median patient-delay time (128min vs 157min, p=0.014). Clinical severity at presentation, defined by systolic arterial pressure and Killip-Kimball (KK) class, did not differ between groups. OHA did not impact D2B (89 min vs 88 min, p=0.550), which was in turn influenced by age ≥75y (OR 1.85, 95% CI 1.31–2.61, p5 years. 5yM rate was 9.7%. After multivariate cox regression analysis, independent determinants of long-term mortality were age (HR 1.05, 95% CI 1.02–1.08, p<0.001), previous history of heart failure (HR 6.76, 95% CI 1.32–34.72, p=0.022) and pulmonary disease (HR 3.79, 95% CI 1.16–12.33, p=0.027), presentation with KK ≥2 (HR 2.82, 95% CI 1.32–6.01, p=0.007) and radial artery access in catheterization (HR 0.39, 95% CI 0.18–0.83, p=0.014) – figure 1. Although there was an association between a higher D2B time and 5yM (87min vs 101min, p=0.024), neither OHA nor D2B were independent predictors of long-term mortality – figure 2. Conclusion: OHA did not seem to influence D2B and long-term STEMI outcomes in our PCI-centre. 5yM was mostly influenced by patient characteristics and clinical severity at presentation. Funding Acknowledgement: Type of funding sources: None. Figure 1. Predictors of long-term mortalityFigure 2. 5-year survival stratified by OHA