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Details

  • Name

    Pedro Pereira Rodrigues
  • Cluster

    Computer Science
  • Role

    External Research Collaborator
  • Since

    04th January 2010
Publications

2018

Systematic overview of neuroanatomical differences in ADHD: Definitive evidence

Authors
Vieira de Melo, BBV; Trigueiro, MJ; Rodrigues, PP;

Publication
DEVELOPMENTAL NEUROPSYCHOLOGY

Abstract
Objectives: This article seeks to identify neuroanatomical differences in ADHD through an overview of systematic reviews that report encephalic differences compared to a control group in volume, area, activation likelihood or chemical composition.Methods: We conducted a systematic search using Cochrane guidelines and PRISMA criteria in PubMed, Scopus, Web of Science, Cochrane Database of Systematic Reviews and Database of Abstracts of Reviews of Effects.Results: Results revealed broad encephalic involvement that includes a functional frontal and cingulate hypoactivation and structural differences in corpus callosum, cerebellum and basal nuclei.Conclusions: ADHD symptoms might be due to a multi-network unbalanced functioning hypothesis.

2018

Impact of Imputing Missing Data in Bayesian Network Structure Learning for Obstructive Sleep Apnea Diagnosis

Authors
Santos, DF; Soares, MM; Rodrigues, PP;

Publication
Building Continents of Knowledge in Oceans of Data: The Future of Co-Created eHealth - Proceedings of MIE 2018, Medical Informatics Europe, Gothenburg, Sweden, April 24-26, 2018

Abstract

2018

Sifting Through Chaos: Extracting Information from Unstructured Legal Opinions

Authors
Oliveira, BM; Guimarães, RV; Antunes, L; Rodrigues, PP;

Publication
Building Continents of Knowledge in Oceans of Data: The Future of Co-Created eHealth - Proceedings of MIE 2018, Medical Informatics Europe, Gothenburg, Sweden, April 24-26, 2018

Abstract

2018

Impact of imputing missing data in Bayesian network structure learning for obstructive sleep apnea diagnosis

Authors
Ferreira Santos, D; Monteiro Soares, M; Rodrigues, PP;

Publication
Studies in Health Technology and Informatics

Abstract
Numerous diagnostic decisions are made every day by healthcare professionals. Bayesian networks can provide a useful aid to the process, but learning their structure from data generally requires the absence of missing data, a common problem in medical data. We have studied missing data imputation using a step-wise nearest neighbors' algorithm, which we recommended given its limited impact on the assessed validity of structure learning Bayesian network classifiers for Obstructive Sleep Apnea diagnosis. © 2018 European Federation for Medical Informatics (EFMI) and IOS Press.

2018

Finding Groups in Obstructive Sleep Apnea Patients: A Categorical Cluster Analysis

Authors
Santos, DF; Rodrigues, PP;

Publication
31st IEEE International Symposium on Computer-Based Medical Systems, CBMS 2018, Karlstad, Sweden, June 18-21, 2018

Abstract
Obstructive sleep apnea (OSA) is a significant sleep problem with various clinical presentations that have not been formally characterized. This poses critical challenges for its recognition, resulting in missed or delayed diagnosis. Recently, cluster analysis has been used in different clinical domains, particularly within numeric variables. We applied an extension of k-means to be used in categorical variables: k-modes, to identify groups of OSA patients. Demographic, physical examination, clinical history, and comorbidities characterization variables (n=46) were collected from 318 patients; missing values were all imputed with k-nearest neighbors (k-NN). Feature selection, through Chi-square test, was executed and 17 variables were inserted in cluster analysis, resulting in three clusters. Cluster 1 having an age between 65 and 90 years (54%), 78% of males, with the presence of diabetes and gastroesophageal reflux, and high OSA prevalence; Cluster 2 presented a lower percentage of OSA (46%), with middle-aged women without comorbidities, but with gastroesophageal reflux; and Cluster 3 was very similar to cluster 1, only differing in age (45-64) and comorbidities were not present. Our results suggest that there are different groups of OSA patients, creating the need to rethink the baseline characteristics of these patients before being sent to perform polysomnography (gold standard exam for diagnosis). © 2018 IEEE.