Abstract

Abstract
Missing data is a problem often found in real-world datasets and it can degrade the performance of most machine learning models. Several deep learning techniques have been used to address this issue, and one of them is the Autoencoder and its Denoising and Variational variants. These models are able to learn a representation of the data with missing values and generate plausible new ones to replace them. This study surveys the use of Autoencoders for the imputation of tabular data and considers 26 works published between 2014 and 2020. The analysis is mainly focused on discussing patterns and recommendations for the architecture, hyperparameters and training settings of the network, while providing a detailed discussion of the results obtained by Autoencoders when compared to other state-of-the-art methods, and of the data contexts where they have been applied. The conclusions include a set of recommendations for the technical settings of the network, and show that Denoising Autoencoders outperform their competitors, particularly the often used statistical methods.

Abstract

Abstract

Abstract

Abstract
Objective: This study aimed to identify psychoeducational interventions applied to parents of children with chronic diseases and evaluate their impact on their quality of life (QoL). Methods: It was conducted in six databases, complemented by references from the included studies and other reviews, manual search, and contact with experts. We included primary studies on parents of children with chronic diseases that studied psychoeducational interventions versus standard care. Results: We screened 6604 titles and abstracts, reviewed the full text of 60 records, and included 37 primary studies. Half of the studies were on Asthma. We found three intervention formats: one-to-one (43%), groups (49%), and combined approach with individual and group settings (8%). More than 60% of the included studies found statistically significant differences between the intervention and the control group (p < 0.05). Conclusion: Several interventions have shown efficacy in improving parental QoL. Despite that, there is insufficient evidence of interventions' implementation. Practice implications: A holistic approach encompassing the patient and the family's biopsychosocial dimensions is fundamental in successfully managing chronic disease in children. It is vital to design and implement interventions accommodating the common issues experienced by children, parents, and families that deal with chronic childhood conditions. Systematic review registration number PROSPERO 2018 CRD42018092135.