Details
Name
Pedro Pereira RodriguesCluster
Computer ScienceRole
External Research CollaboratorSince
04th January 2010
Nationality
PortugalCentre
Artificial Intelligence and Decision SupportContacts
+351220402963
pedro.p.rodrigues@inesctec.pt
2023
Authors
Ferreira Santos, D; Pereira Rodrigues, P;
Publication
Journal of medical Internet research
Abstract
[No abstract available]
2022
Authors
Rodrigues, MG; Rodrigues, JD; Pereira, AT; Azevedo, LF; Rodrigues, PP; Areias, JC; Areias, ME;
Publication
PATIENT EDUCATION AND COUNSELING
Abstract
2022
Authors
Gunes, S; Aizawa, Y; Sugashi, T; Sugimoto, M; Rodrigues, PP;
Publication
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
Abstract
2022
Authors
Pereira, RC; Abreu, PH; Rodrigues, PP;
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
Authors
Ferreira Santos, D; Amorim, P; Martins, TS; Monteiro Soares, M; Rodrigues, PP;
Publication
JOURNAL OF MEDICAL INTERNET RESEARCH
Abstract 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. We aimed to identify, gather, and analyze existing machine learning approaches that are being used for disease screening in adult patients suspected of OSA. 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. 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. Although high values were obtained, they still lack external validation results in large cohorts and a standard OSA criteria definition.
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