Cookies Policy
The website need some cookies and similar means to function. If you permit us, we will use those means to collect data on your visits for aggregated statistics to improve our service. Find out More
Accept Reject
  • Menu
Interest
Topics
Details

Details

  • Name

    Pedro Pereira Rodrigues
  • Cluster

    Computer Science
  • Role

    External Research Collaborator
  • Since

    04th January 2010
Publications

2023

Obstructive sleep apnea: A categorical cluster analysis and visualization

Authors
Ferreira-Santos, D; Rodrigues, PP;

Publication
PULMONOLOGY

Abstract
Introduction and Objectives: Obstructive sleep apnea (OSA) is a prevalent sleep condition which is very heterogeneous although not formally characterized as such, resulting in missed or delayed diagnosis. Cluster analysis has been used in different clinical domains, particularly within sleep disorders. We aim to understand OSA heterogeneity and provide a variety of cluster visualizations to communicate the information clearly and efficiently.Materials and Methods: We applied an extension of k-means to be used in categorical variables: k -modes, to identify OSA patients' groups, based on demographic, physical examination, clinical his-tory, and comorbidities characterization variables (n = 40) obtained from a derivation and validation cohorts (211 and 53, respectively) from the northern region of Portugal. Missing values were imputed with k-nearest neighbours (k-NN) and a chi-square test was held for feature selection.Results: Thirteen variables were inserted in phenotypes, resulting in the following three clus-ters: Cluster 1, middle-aged males reporting witnessed apneas and high alcohol consumption before sleep; Cluster 2, middle-aged women with increased neck circumference (NC), non -repairing sleep and morning headaches; and Cluster 3, obese elderly males with increased NC, witnessed apneas and alcohol consumption. Patients from the validation cohort assigned to dif-ferent clusters showed similar proportions when compared with the derivation cohort, for mild (C1: 56 vs 75%, P = 0.230; C2: 61 vs 75%, P = 0.128; C3: 45 vs 48%, P = 0.831), moderate (C1: 24 vs 25%; C2: 20 vs 25%; C3: 25 vs 19%) and severe (C1: 20 vs 0%; C2: 18 vs 0%; C3: 29 vs 33%) levels. Therefore, the allocation supported the validation of the obtained clusters.Conclusions: Our findings suggest different OSA patients' groups, creating the need to rethink these patients' stereotypical baseline characteristics.(c) 2021 Sociedade Portuguesa de Pneumologia. Published by Elsevier Espana, S.L.U. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

2023

The Association Between Comorbidities and Prescribed Drugs in Patients With Suspected Obstructive Sleep Apnea: Inductive Rule Learning Approach

Authors
Ferreira-Santos, D; Rodrigues, PP;

Publication
JOURNAL OF MEDICAL INTERNET RESEARCH

Abstract
[No abstract available]

2023

A randomized controlled trial to assess the impact of psychoeducation on the quality of life of parents with children with congenital heart defects-Quantitative component

Authors
Rodrigues, MG; Rodrigues, JD; Moreira, JA; Clemente, F; Dias, CC; Azevedo, LF; Rodrigues, PP; Areias, JC; Areias, ME;

Publication
CHILD CARE HEALTH AND DEVELOPMENT

Abstract
PurposeTo develop, implement and assess the results of psychoeducation to improve the QoL of parents with CHD newborns.MethodsParticipants were parents of inpatient newborns with the diagnosis of non-syndromic CHD. We conducted a parallel RCT with an allocation ratio of 1:1 (intervention vs. control), considering the newborns, using mixed methods research. The intervention group received psychoeducation (Parental Psychoeducation in CHD [PPeCHD]) and the usual routines, and the control group received just the regular practices. The allocation concealment was assured. PI was involved in enrolling participants, developing and implementing the intervention, data collection and data analysis. We followed the Consolidated Standards of Reporting Trials (CONSORT) guidelines.ResultsParents of eight newborns were allocated to the intervention group (n = 15 parents) and eight to the control group (n = 13 parents). It was performed as an intention-to-treat (ITT) analysis. In M2 (4 weeks), the intervention group presented better QoL levels in the physical, psychological, and environmental domains of World Health Organization Quality of Life instrument (WHOQOL-Bref). In M3 (16 weeks), scores in physical and psychological domains maintained a statistically significant difference between the groups.ConclusionsThe PPeCHD, the psychoeducational intervention we developed, positively impacted parental QoL. These results support the initial hypothesis. This study is a fundamental milestone in this research field, adding new essential information to the literature.

2023

Hierarchical growth in neural networks structure: Organizing inputs by Order of Hierarchical Complexity

Authors
Leite S.; Mota B.; Silva A.R.; Commons M.L.; Miller P.M.; Rodrigues P.P.;

Publication
PLOS ONE

Abstract
Several studies demonstrate that the structure of the brain increases in hierarchical complexity throughout development. We tested if the structure of artificial neural networks also increases in hierarchical complexity while learning a developing task, called the balance beam problem. Previous simulations of this developmental task do not reflect a necessary premise underlying development: a more complex structure can be built out of less complex ones, while ensuring that the more complex structure does not replace the less complex one. In order to address this necessity, we segregated the input set by subsets of increasing Orders of Hierarchical Complexity. This is a complexity measure that has been extensively shown to underlie the complexity behavior and hypothesized to underlie the complexity of the neural structure of the brain. After segregating the input set, minimal neural network models were trained separately for each input subset, and adjacent complexity models were analyzed sequentially to observe whether there was a structural progression. Results show that three different network structural progressions were found, performing with similar accuracy, pointing towards self-organization. Also, more complex structures could be built out of less complex ones without substituting them, successfully addressing catastrophic forgetting and leveraging performance of previous models in the literature. Furthermore, the model structures trained on the two highest complexity subsets performed better than simulations of the balance beam present in the literature. As a major contribution, this work was successful in addressing hierarchical complexity structural growth in neural networks, and is the first that segregates inputs by Order of Hierarchical Complexity. Since this measure can be applied to all domains of data, the present method can be applied to future simulations, systematizing the simulation of developmental and evolutionary structural growth in neural networks.

2023

Automatic Delta-Adjustment Method Applied to Missing Not At Random Imputation

Authors
Pereira, RC; Rodrigues, PP; Figueiredo, MAT; Abreu, PH;

Publication
Computational Science - ICCS 2023 - 23rd International Conference, Prague, Czech Republic, July 3-5, 2023, Proceedings, Part I

Abstract