Overlap-based undersampling method for classification of imbalanced medical datasets.

Early diagnosis of some life-threatening diseases such as cancers and heart is crucial for effective treatments. Supervised machine learning has proved to be a very useful tool to serve this purpose. Historical data of patients including clinical and demographic information is used for training learning algorithms. This builds predictive models that provide initial diagnoses. However, in the medical domain, it is common to have the positive class under-represented in a dataset. In such a scenario, a typical learning algorithm tends to be biased towards the negative class, which is the majority class, and misclassify positive cases. This is known as the class imbalance problem. In this paper, a framework for predictive diagnostics of diseases with imbalanced records is presented. To reduce the classification bias, we propose the usage of an overlap-based undersampling method to improve the visibility of minority class samples in the region where the two classes overlap. This is achieved by detecting and removing negative class instances from the overlapping region. This will improve class separability in the data space. Experimental results show achievement of high accuracy in the positive class, which is highly preferable in the medical domain, while good trade-offs between sensitivity and specificity were obtained. Results also show that the method often outperformed other state-of-the-art and well-established techniques

Symbols in engineering drawings (SiED): an imbalanced dataset benchmarked by convolutional neural networks.

Engineering drawings are common across different domains such as Oil & Gas, construction, mechanical and other domains. Automatic processing and analysis of these drawings is a challenging task. This is partly due to the complexity of these documents and also due to the lack of dataset availability in the public domain that can help push the research in this area. In this paper, we present a multiclass imbalanced dataset for the research community made of 2432 instances of engineering symbols. These symbols were extracted from a collection of complex engineering drawings known as Piping and Instrumentation Diagram (P&ID). By providing such dataset to the research community, we anticipate that this will help attract more attention to an important, yet overlooked industrial problem, and will also advance the research in such important and timely topics. We discuss the datasets characteristics in details, and we also show how Convolutional Neural Networks (CNNs) perform on such extremely imbalanced datasets. Finally, conclusions and future directions are discussed

Psychosocial Impact of 8 Weeks COVID-19 Quarantine on Italian Parents and their Children

Objectives: Italy was affected greatly by Coronavirus disease 2019 (COVID-19), emerging mainly in the Italian province of Lombardy. This outbreak led to profound governmental interventions along with a strict quarantine. This quarantine may have psychosocial impact on children and parents in particular. The aim of this study was to evaluate the impact of 8 weeks COVID-19 quarantine on psychosocial functioning of Italian parents and their children. Methods: In this cross-sectional survey, we included parents and children resided in Italy during the 8 weeks COVID-19 quarantine. We evaluated social and emotional functioning, clinical symptoms possibly related to emotional distress, and change in perspectives using a questionnaire. Results: The majority of 2315 parents (98% mothers) frequently experienced fear of getting ill (92%) and fluctuating moods (84%), the latter showing correlation to experiencing stress due to being in continuous close vicinity to their children (77%, r = 0.33). Parents reported a positive effect on the relationship with their partner (79%) and their children (89%). Irritability in children was frequent (74%) and correlated to parental fluctuating moods (r = 0.40). The vast majority of the participants (91%) reported that their perspectives for the future had changed. Conclusions for Practice: Our findings suggest a profound impact of the COVID-19 quarantine on emotional functioning of parents and their children in Italy. Despite the protective measure of quarantine against national viral spread and subsequent infection, health care professionals should be aware of this emotional impact, in order to develop protective or therapeutic interventions