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

2022

Impact in the quality of life of parents of children with chronic diseases using psychoeducational interventions – A systematic review with meta-analysis

Authors
Rodrigues, MG; Rodrigues, JD; Pereira, AT; Azevedo, LF; Rodrigues, PP; Areias, JC; Areias, ME;

Publication
PATIENT EDUCATION AND COUNSELING

Abstract

2022

Biomarkers for Alzheimer’s Disease in the Current State: A Narrative Review

Authors
Gunes, S; Aizawa, Y; Sugashi, T; Sugimoto, M; Rodrigues, PP;

Publication
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES

Abstract
Alzheimer’s disease (AD) has become a problem, owing to its high prevalence in an aging society with no treatment available after onset. However, early diagnosis is essential for preventive intervention to delay disease onset due to its slow progression. The current AD diagnostic methods are typically invasive and expensive, limiting their potential for widespread use. Thus, the development of biomarkers in available biofluids, such as blood, urine, and saliva, which enables low or non-invasive, reasonable, and objective evaluation of AD status, is an urgent task. Here, we reviewed studies that examined biomarker candidates for the early detection of AD. Some of the candidates showed potential biomarkers, but further validation studies are needed. We also reviewed studies for non-invasive biomarkers of AD. Given the complexity of the AD continuum, multiple biomarkers with machine-learning-classification methods have been recently used to enhance diagnostic accuracy and characterize individual AD phenotypes. Artificial intelligence and new body fluid-based biomarkers, in combination with other risk factors, will provide a novel solution that may revolutionize the early diagnosis of AD.

2022

Partial Multiple Imputation with Variational Autoencoders: Tackling Not at Randomness in Healthcare Data

Authors
Cardoso Pereira R.; Henriques Abreu P.; Rodrigues P.P.P.;

Publication
IEEE Journal of Biomedical and Health Informatics

Abstract
Missing data can pose severe consequences in critical contexts, such as clinical research based on routinely collected healthcare data. This issue is usually handled with imputation strategies, but these tend to produce poor and biased results under the Missing Not At Random (MNAR) mechanism. A recent trend that has been showing promising results for MNAR is the use of generative models, particularly Variational Autoencoders. However, they have a limitation: the imputed values are the result of a single sample, which can be biased. To tackle it, an extension to the Variational Autoencoder that uses a partial multiple imputation procedure is introduced in this work. The proposed method was compared to 8 state-of-the-art imputation strategies, in an experimental setup with 34 datasets from the medical context, injected with the MNAR mechanism (10% to 80% rates). The results were evaluated through the Mean Absolute Error, with the new method being the overall best in 71% of the datasets, significantly outperforming the remaining ones, particularly for high missing rates. Finally, a case study of a classification task with heart failure data was also conducted, where this method induced improvements in 50% of the classifiers.

2022

Helping early obstructive sleep apnea diagnosis with machine learning: A systematic review (Preprint)

Authors
Ferreira-Santos, D; Amorim, P; Silva Martins, T; Monteiro-Soares, M; Pereira Rodrigues, P;

Publication
Journal of Medical Internet Research

Abstract
BACKGROUND

American Academy of Sleep Medicine guidelines suggests that clinical prediction algorithms can be used to screen obstructive sleep apnea (OSA) patients without replacing polysomnography (PSG) – the gold standard.

OBJECTIVE

We aimed to identify, gather, and analyze existing machine learning approaches that are being used for disease screening in adult patients suspected of OSA.

METHODS

We searched MEDLINE, Scopus and ISI Web of Knowledge databases for evaluating the validity of different machine learning techniques, with PSG as the gold standard outcome measures. This systematic review was registered in PROSPERO under reference CRD42021221339.

RESULTS

Our search retrieved 5479 articles, of which 63 articles were included. We found 23 studies performing diagnostic models’ development alone, 26 with added internal validation, and 14 applying the clinical prediction algorithm to an independent sample (although not all reporting the most common discrimination metrics - sensitivity and/or specificity). Logistic regression was applied in 35 studies, linear regression in 16, support vector machine in 9, neural networks in 8, decision trees in 6, and Bayesian networks in 4. Random forest, discriminant analysis, classification and regression tree, and nomogram were each performed in 2 studies, while Pearson correlation, adaptative neuro-fuzzy inference system, artificial immune recognition system, genetic algorithm, supersparse linear integer models, and k-nearest neighbors’ algorithm each in 1 study. The best AUC was .98 [.96-.99] for age, waist circumference, Epworth somnolence, and oxygen saturation as predictors in a logistic regression.

CONCLUSIONS

Although high values were obtained, they still lack external validation results in large cohorts and a standard OSA criteria definition.

2021

CMIID: A comprehensive medical information identifier for clinical search harmonization in Data Safe Havens

Authors
Domingues, MAP; Camacho, R; Rodrigues, PP;

Publication
JOURNAL OF BIOMEDICAL INFORMATICS

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.