There is a need of ensuring that learning (ML) models are interpretable. Higher interpretability of the model means easier comprehension and explanation of future predictions for end-users. Further, interpretable ML models allow healthcare experts to make reasonable and data-driven decisions to provide personalized decisions that can ultimately lead to higher quality of service in healthcare. Generally, we can classify interpretability approaches in two groups where the first focuses on personalized interpretation (local interpretability) while the second summarizes prediction models on a population level (global interpretability). Alternatively, we can group interpretability methods into model-specific techniques, which are designed to interpret predictions generated by a specific model, such as a neural network, and model-agnostic approaches, which provide easy-to-understand explanations of predictions made by any ML model. Here, we give an overview of interpretability approaches using structured data and provide examples of practical interpretability of ML in different areas of healthcare, including prediction of healthrelated outcomes, optimizing treatments, or improving the efficiency of screening for specific conditions. Further, we outline future directions for interpretable ML and highlight the importance of developing algorithmic solutions that can enable ML driven decision making in high-stakes healthcare problems.

Most screening tests for T2DM in use today were developed using multivariate regression methods that are often further simplified to allow transformation into a scoring formula. The increasing volume of electronically collected data opened the opportunity to develop more complex, accurate prediction models that can be continuously updated using machine learning approaches. This study compares machine learning-based prediction models (i.e. Glmnet, RF, XGBoost, LightGBM) to commonly used regression models for prediction of undiagnosed T2DM. The performance in prediction of fasting plasma glucose level was measured using 100 bootstrap iterations in different subsets of data simulating new incoming data in 6-month batches. With 6 months of data available, simple regression model performed with the lowest average RMSE of 0.838, followed by RF (0.842), LightGBM (0.846), Glmnet (0.859) and XGBoost (0.881). When more data were added, Glmnet improved with the highest rate (+ 3.4%). The highest level of variable selection stability over time was observed with LightGBM models. Our results show no clinically relevant improvement when more sophisticated prediction models were used. Since higher stability of selected variables over time contributes to simpler interpretation of the models, interpretability and model calibration should also be considered in development of clinical prediction models. Type 2 diabetes mellitus (T2DM) is very common and is responsible for very considerable morbidity, mortality. Furthermore, it is a substantial financial drain both on individuals/families, health systems and societies. Of major concern is that the incidence and prevalence of T2DM are increasing rapidly-globally. In 2017, it was estimated that 425 million people had any type of diabetes (approx. 5.5% of worldwide population) of which 90% had T2DM and according to projection estimations the prevalence is going to increase substantially in the coming years; by 2,045, for example, a 48% increase of prevalence from the above numbers is expected or in absolute numbers an estimated 629 million people (approx. 6.6% of the worldwide population) are expected to be suffering from any type of diabetes 1. T2DM can lead to substantially increased risk of macrovascular and microvascular disease, especially in those with inadequate glycaemic control 2. Progression of T2DM from impaired fasting glucose is typically slow and more importantly, its symptoms may remain undetected for many years. Delays in diagnosis are an important contributory factor to poor control and risk of complications 3. Data mining is nowadays applied to various fields of science, including healthcare and medicine. Often applied are pattern recognition, disease prediction and classification using various data mining techniques 4. Due to the increased prevalence of T2DM, various techniques have been used to build predictive models and models for early disease diagnosis, such as logistic and Cox proportional hazard regression models 5-7 , Random Forest 8,9 , boosted ensembles 10,1...

Emotional intelligence was higher in nursing than engineering students, and slightly higher in women than men. It was not associated with previous caring experience.

Aim The aim of this study was to assess the validity of the Warwick‐Edinburgh Mental Well‐being Scale used for measuring mental well‐being. Background Nursing students’ mental well‐being is often poor due to various academic and personal stressors. Nursing students are involved in clinical practice and are facing birth, death, health, diseases and other stressful situations. They may be exposed to higher levels of stress than students from other study programmes. Methods A cross‐sectional study was conducted among nursing students in Slovenia. We performed a 6‐step analysis of the psychometric properties of the Warwick‐Edinburgh Mental Well‐being Scale. Moreover, content validity of the scale was assessed. Results The scale formed a unidimensional scale with good homogeneity (H < 0.40) and reliability (α = 0.91; β = 0.87; λ = 0.92; ω = 0.91). The confirmatory factor analysis suggested that the WEMWBS was suitable for use as a single scale (RMSEA = 0.085, CFI = 0.907; TLI = 0.891) and measures one construct, mental well‐being. I‐CVI is acceptable for all 14 items, kappa coefficient was excellent, and S‐CVI was assessed as acceptable. Conclusions The Slovenian version of the scale achieved good validity and reliability in a sample of nursing students and is recommended for future usage. Implications for Nursing Management The validated questionnaire can be used by nurse managers to assess nursing students’ mental well‐being during their clinical placement.

For conducting research, nurses typically use commercial statistical packages. R software is a free, powerful, and flexible alternative, but is less familiar and used less frequently in nursing research. In this paper, we use data from a previous study to demonstrate a few typical steps in exploratory data analysis using R. A step‐by‐step description of some basic analyses in R is provided here, including examples of specific functions to read and manipulate the data, calculate scores from individual questionnaire items, and prepare a correlation plot and summary table.