Background: Health recommender systems (HRSs) offer the potential to motivate and engage users to change their behavior by sharing better choices and actionable knowledge based on observed user behavior. Objective: We aim to review HRSs targeting nonmedical professionals (laypersons) to better understand the current state of the art and identify both the main trends and the gaps with respect to current implementations. Methods: We conducted a systematic literature review according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and synthesized the results. A total of 73 published studies that reported both an implementation and evaluation of an HRS targeted to laypersons were included and analyzed in this review. Results: Recommended items were classified into four major categories: lifestyle, nutrition, general health care information, and specific health conditions. The majority of HRSs use hybrid recommendation algorithms. Evaluations of HRSs vary greatly; half of the studies only evaluated the algorithm with various metrics, whereas others performed full-scale randomized controlled trials or conducted in-the-wild studies to evaluate the impact of HRSs, thereby showing that the field is slowly maturing. On the basis of our review, we derived five reporting guidelines that can serve as a reference frame for future HRS studies. HRS studies should clarify who the target user is and to whom the recommendations apply, what is recommended and how the recommendations are presented to the user, where the data set can be found, what algorithms were used to calculate the recommendations, and what evaluation protocol was used. Conclusions: There is significant opportunity for an HRS to inform and guide health actions. Through this review, we promote the discussion of ways to augment HRS research by recommending a reference frame with five design guidelines.

Classification is an important and widely used machine learning technique in bioinformatics. Researchers and other end-users of machine learning software often prefer to work with comprehensible models where knowledge extraction and explanation of reasoning behind the classification model are possible. This paper presents an extension to an existing machine learning environment and a study on visual tuning of decision tree classifiers. The motivation for this research comes from the need to build effective and easily interpretable decision tree models by so called one-button data mining approach where no parameter tuning is needed. To avoid bias in classification, no classification performance measure is used during the tuning of the model that is constrained exclusively by the dimensions of the produced decision tree. The proposed visual tuning of decision trees was evaluated on 40 datasets containing classical machine learning problems and 31 datasets from the field of bioinformatics. Although we did not expected significant differences in classification performance, the results demonstrate a significant increase of accuracy in less complex visually tuned decision trees. In contrast to classical machine learning benchmarking datasets, we observe higher accuracy gains in bioinformatics datasets. Additionally, a user study was carried out to confirm the assumption that the tree tuning times are significantly lower for the proposed method in comparison to manual tuning of the decision tree. The empirical results demonstrate that by building simple models constrained by predefined visual boundaries, one not only achieves good comprehensibility, but also very good classification performance that does not differ from usually more complex models built using default settings of the classical decision tree algorithm. In addition, our study demonstrates the suitability of visually tuned decision trees for datasets with binary class attributes and a high number of possibly redundant attributes that are very common in bioinformatics.

Emotional intelligence in nursing is of global interest. International studies identify that emotional intelligence influences nurses' work and relationships with patients. It is associated with compassion and care. Nursing students scored higher on measures of emotional intelligence compared to students of other study programmes. The level of emotional intelligence increases with age and tends to be higher in women. This study aims to measure the differences in emotional intelligence between nursing students with previous caring experience and those without; to examine the effects of gender on emotional intelligence scores; and to test whether nursing students score higher than engineering colleagues on emotional intelligence measures. A cross-sectional descriptive study design was used. The study included 113 nursing and 104 engineering students at the beginning of their first year of study at a university in Slovenia. Emotional intelligence was measured using the Trait Emotional Intelligence Questionnaire (TEIQue) and Schutte Self Report Emotional Intelligence Test (SSEIT). Shapiro-Wilk's test of normality was used to test the sample distribution, while the differences in mean values were tested using Student t-test of independent samples. Emotional intelligence was higher in nursing students (n = 113) than engineering students (n = 104) in both measures [TEIQue t = 3.972; p < 0.001; SSEIT t = 8.288; p < 0.001]. Although nursing female students achieved higher emotional intelligence scores than male students on both measures, the difference was not statistically significant [TEIQue t = -0.839; p = 0.403; SSEIT t = -1.159; p = 0.249]. EI scores in nursing students with previous caring experience were not higher compared to students without such experience for any measure [TEIQue t = -1.633; p = 0.105; SSEIT t = -0.595; p = 0.553]. Emotional intelligence was higher in nursing than engineering students, and slightly higher in women than men. It was not associated with previous caring experience.