Rui Camacho

Abstract
Cancer is one of the diseases with the highest mortality rate in the world. To understand the different origins of the disease, and to facilitate the development of new ways to treat it, laboratories cultivate, in vitro, cancer cells (cell lines), taken from patients with cancer. These cell lines enable researchers to test new approaches and to have an appropriate procedure for comparison of results. The methods used in an initial study at EMBL-EBI Institute (Cambridge, UK) were based on algorithms that construct “propositional like” models. The results reported were promising but we believe that they can be improved. A relevant limitation of the algorithms used in the original study is the absence or severe lack of comprehensibility of the models constructed. In Life Sciences, the possibility of understanding a model is an asset to help the specialist to understand the phenomenon that produced the data. With our study we have improved the performance of forecasting models and constructed understandable models. To meet these objectives we have used Graph Mining and Inductive Logic Programming algorithms. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
AbstractThe timely identification of cohort participants at higher risk for attrition is important to earlier interventions and efficient use of research resources. Machine learning may have advantages over the conventional approaches to improve discrimination by analysing complex interactions among predictors. We developed predictive models of attrition applying a conventional regression model and different machine learning methods. A total of 542 very preterm (<?32 gestational weeks) infants born in Portugal as part of the European Effective Perinatal Intensive Care in Europe (EPICE) cohort were included. We tested a model with a fixed number of predictors (Baseline) and a second with a dynamic number of variables added from each follow-up (Incremental). Eight classification methods were applied: AdaBoost, Artificial Neural Networks, Functional Trees, J48, J48Consolidated, K-Nearest Neighbours, Random Forest and Logistic Regression. Performance was compared using AUC- PR (Area Under the Curve—Precision Recall), Accuracy, Sensitivity and F-measure. Attrition at the four follow-ups were, respectively: 16%, 25%, 13% and 17%. Both models demonstrated good predictive performance, AUC-PR ranging between 69 and 94.1 in Baseline and from 72.5 to 97.1 in Incremental model. Of the whole set of methods, Random Forest presented the best performance at all follow-ups [AUC-PR1: 94.1 (2.0); AUC-PR2: 91.2 (1.2); AUC-PR3: 97.1 (1.0); AUC-PR4: 96.5 (1.7)]. Logistic Regression performed well below Random Forest. The top-ranked predictors were common for both models in all follow-ups: birthweight, gestational age, maternal age, and length of hospital stay. Random Forest presented the highest capacity for prediction and provided interpretable predictors. Researchers involved in cohorts can benefit from our robust models to prepare for and prevent loss to follow-up by directing efforts toward individuals at higher risk.

Abstract

Abstract
Objective/background: To describe and characterize insomnia symptoms and nightmare profiles in Portugal during the first six weeks of a national lockdown due to COVID-19. Patients/methods: An open cohort study was conducted to collect information of the general population during the first wave of SARS-CoV-2/COVID-19 pandemic in Portugal. We analyzed data from 5011 participants (>= 16 years) who answered a weekly questionnaire about their well-being. Two questions about the frequency of insomnia and nightmares about COVID-19 were consecutively applied during six weeks (March-May 2020). Latent class analysis was conducted and different insomnia and nightmare profiles were identified. Associations between individual characteristics and both profiles were estimated using odds ratios (ORs) and 95% confidence intervals (CI). Results: Five insomnia (No insomnia, Stable-mild, Decreasing-moderate, Stable-severe, Increasing-severe) and three nightmares profiles (Stable-mild, Stable-moderate, Stable-severe) were identified. Being female, younger, perceiving their income as insufficient and feelings of fear towards COVID-19 were associated with higher odds of insomnia (Women: OR = 6.98 95%CI: 4.18-11.64; >= 60 years: OR = 0.30 95%CI: 0.18-0.53; Insufficient income: adjusted OR (aOR) = 8.413 95% CI: 3.93-16.84; Often presenting fear of being infected with SARS-CoV-2 infection: aOR = 9.13 95%CI: 6.36-13.11), and nightmares (Women: OR = 2.60 95%CI: 1.74-3.86; >= 60 years: OR = 0.45 95%CI: 0.28-0.74; Insufficient income: aOR = 2.60 95%CI: 1.20e5.20; Often/almost always presenting fear of being infected with SARS-CoV-2 infection: aOR = 6.62 95%CI: 5.01-8.74). Having a diagnosis of SARS-CoV-2 virus infection was associated with worse patterns of nightmares about the pandemic. Conclusions: Social and psychological individual factors are important characteristics to consider in the developmentof therapeutic strategies to supportpeoplewithsleep problems during the COVID-19 pandemic.

Abstract
Over the last decades clinical research has been driven by informatics changes nourished by distinct research endeavors. Inherent to this evolution, several issues have been the focus of a variety of studies: multi-location patient data access, interoperability between terminological and classification systems and clinical practice and records harmonization. Having these problems in mind, the Data Safe Haven paradigm emerged to promote a newborn architecture, better reasoning and safe and easy access to distinct Clinical Data Repositories. This study aim is to present a novel solution for clinical search harmonization within a safe environment, making use of a hybrid coding taxonomy that enables researchers to collect information from multiple repositories based on a clinical domain query definition. Results show that is possible to query multiple repositories using a single query definition based on clinical domains and the capabilities of the Unified Medical Language System, although it leads to deterioration of the framework response times. Participants of a Focus Group and a System Usability Scale questionnaire rated the framework with a median value of 72.5, indicating the hybrid coding taxonomy could be enriched with additional metadata to further improve the refinement of the results and enable the possibility of using this system as data quality tagging mechanism. © 2020 Elsevier Inc.