Abstract
Precise machine learning models for the early identification of anomalies based on biosignal data retrieved from bedside monitors could improve intensive care, by helping clinicians make decisions in advance and produce on-time responses. However, traditional models show limitations when dealing with the high complexity of this task. Layered Learning (LL) emerges as a solution, as it consists of the hierarchical decomposition of the problem into simpler tasks. This paper explores the uncovered potential of LL in the early detection of Acute Hypotensive Episodes (AHEs). We leverage information from the MIMIC-III Database to test different subdivisions of the main task and study how to combine the outcomes from distinct layers. In addition to this, we also test a novel approach to reduce false positives in AHE predictions.

Abstract
Imbalanced learning is one of the most relevant problems in machine learning. However, it faces two crucial challenges. First, the amount of methods proposed to deal with such problem has grown immensely, making the validation of a large set of methods impractical. Second, it requires specialised knowledge, hindering its use by those without such level of experience. In this paper, we propose the Automated Imbalanced Classification method, ATOMIC. Such a method is the first automated machine learning approach for imbalanced classification tasks. It provides a ranking of solutions most likely to ensure an optimal approximation to a new domain, drastically reducing associated computational complexity and energy consumption. We carry this out by anticipating the loss of a large set of predictive solutions in new imbalanced learning tasks. We compare the predictive performance of ATOMIC against state-of-the-art methods using 101 imbalanced data sets. Results demonstrate that the proposed method provides a relevant approach to imbalanced learning while reducing learning and testing efforts of candidate solutions by approximately 95%.

Abstract

Abstract
This paper compares the accuracy of a set of 22 methods for short-term hotel demand forecasting for lead times up to 14 days ahead. Machine learning models are compared with methods ranging from seasonal naive to exponential smoothing methods for double seasonality. The machine learning methods considered include a new approach based on arbitrating, in which several forecasting models are dynamically combined to obtain predictions. Arbitrating is a metalearning approach that combines the output of experts according to predictions of the loss that they will incur. Particularly, the dynamic ensemble method is used. The methods were compared using a real time series of daily demand for a four-star hotel located in the south of Europe. The forecasting performance of those methods was assessed using three alternative accuracy measures. Results from extensive empirical experiments led us to conclude that machine learning methods outperform traditional hotel demand forecasting methods. We found that the use of machine learning models can reduce the root mean squared error up to 54% for a 1-day forecast horizon, and up to 45% for a 14-days forecast horizon, when compared with traditional exponential smoothing methods.

Abstract
This study aimed to assess the effectiveness of a structured intervention on the frequency of self-care behaviors with arteriovenous fistula (AVF) by patients on hemodialysis. This is a quasi-experimental study with pre- and post-measurements. Participants were assigned to an intervention group (IG) (n = 48) or to a control group (CG) (n = 41). IG patients were subject to a structured intervention on self-care with AVF (SISC-AVF) consisting of both a theoretical and a practical part. After SISC-AVF application, patients in the IG showed better overall self-care behaviors with AVF than patients in the CG (79.2% and 91.4%, respectively, p < .001) as well as better self-care concerning both the management of signs and symptoms (90.1% and 94.4% respectively, p = .004) and the prevention of complications (72.7% and 89.5%, respectively, p < .001). The study results suggest that the SISC-AVF had positive effects on patients in the IG.

Abstract
Aim Testing the validity and reliability of the Scale for the Environments Evaluation of Professional Nursing Practice (SEE-Nursing Practice). Background The environment of professional nursing practice is key to achieve better results for clients, nurses and institutions. Therefore, instruments enabling the assessment of all its attributes are required. Method Cross-sectional methodological study. The SEE-Nursing Practice, based on a previous qualitative study and literature review, was applied as a questionnaire. Exploratory and confirmatory factor analyses were used to assess construct validity. Results A total of 752 nurses participated in the study. Exploratory factor analysis of the SEE-Nursing Practice led to a factor solution with 93 items and three subscales. The Structure, Process and Outcome subscales, respectively, have 43, 37 and 13 items, loaded in 6 factors, 6 factors and 2 factors and explaining 62.6%, 59.2% and 67.4% of the total variance. Cronbach's alpha of the overall scale and of the 3 subscales was greater than 0.90. Confirmatory factor analysis showed a good fit. Conclusion SEE-Nursing Practice is a good valid and reliable instrument. Implications for nursing management The SEE-Nursing Practice enables assessing practice environments and is a tool for nursing managers in the definition of strategies ensuring favourable environments for nursing care quality.