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Detalhes

Detalhes

  • Nome

    Pedro Pereira Rodrigues
  • Cluster

    Informática
  • Cargo

    Investigador Colaborador Externo
  • Desde

    04 janeiro 2010
Publicações

2022

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

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

Publicação
PATIENT EDUCATION AND COUNSELING

Abstract

2022

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

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

Publicação
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

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

Publicação
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)

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

Publicação
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.

2022

Achieving rapid and significant results in healthcare services by using the theory of constraints

Autores
Bacelar-Silva, GM; Cox, JF; Rodrigues, P;

Publicação
Health Systems

Abstract