Sistemas Hipertexto & Hipermédia - Reflexão, Ensino e Arte

Neste artigo começa por caracterizar-se o desenvolvimento dos sistemas hiper-texto/hipermédia associado ao paradigma da representação mental. Procura-se analisar o fundamento da atitude de mimésis entre os referidos sistemas e as representações internas do indivíduo no processamento de informação. Seguidamente, destaca-se o papel das tecnologias hipertexto/hipermédia no sistema educativo e o impacto inerente à sua implementação. Inclui, no final, uma reflexão sobre a cultura hipertexto/hipermédia e as suas repercussões no mundo da arte

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

Abstract: Machine learning methods offer great promise for fast and accurate detection and prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest radiographs (CXR) and chest computed tomography (CT) images. Many articles have been published in 2020 describing new machine learning-based models for both of these tasks, but it is unclear which are of potential clinical utility. In this systematic review, we consider all published papers and preprints, for the period from 1 January 2020 to 3 October 2020, which describe new machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images. All manuscripts uploaded to bioRxiv, medRxiv and arXiv along with all entries in EMBASE and MEDLINE in this timeframe are considered. Our search identified 2,212 studies, of which 415 were included after initial screening and, after quality screening, 62 studies were included in this systematic review. Our review finds that none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases. This is a major weakness, given the urgency with which validated COVID-19 models are needed. To address this, we give many recommendations which, if followed, will solve these issues and lead to higher-quality model development and well-documented manuscripts

Permutation group algebras and parking functions

Available from British Library Document Supply Centre- DSC:DXN060973 / BLDSC - British Library Document Supply CentreSIGLEGBUnited Kingdo

Cyclical modulation of human ventricular repolarization by respiration.

Background: Respiratory modulation of autonomic input to the sinus node results in cyclical modulation of heart rate, known as respiratory sinus arrhythmia (RSA). We hypothesized that the respiratory cycle may also exert cyclical modulation on ventricular repolarization, which may be separately measurable using local endocardial recordings. Methods and Results: The study included 16 subjects with normal ventricles undergoing routine clinical electrophysiological procedures for supraventricular arrhythmias. Unipolar electrograms were recorded from 10 right and 10 left ventricular endocardial sites. Breathing was voluntarily regulated at 5 fixed frequencies (6, 9, 12, 15, and 30 breaths per min) and heart rate was clamped by RV pacing. Activation-recovery intervals (ARI: a surrogate for APD) exhibited significant (p < 0.025) cyclical variation at the respiratory frequency in all subjects; ARI shortened with inspiration and lengthened with expiration. Peak-to-peak ARI variation ranged from 0–26 ms; the spatial pattern varied with subject. Arterial blood pressure also oscillated at the respiratory frequency (p < 0.025) and lagged behind respiration by between 1.5 s and 0.65 s from slowest to fastest breathing rates respectively. Systolic oscillation amplitude was significantly greater than diastolic (14 ± 5 vs. 8 ± 4 mm Hg ± SD, p < 0.001). Conclusions: Observations in humans with healthy ventricles using multiple left and right ventricular endocardial recordings showed that ARI action potential duration (APD) varied cyclically with respiration

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

Many machine learning-based approaches have been developed for the prognosis and diagnosis of COVID-19 from medical images and this Analysis identifies over 2,200 relevant published papers and preprints in this area. After initial screening, 62 studies are analysed and the authors find they all have methodological flaws standing in the way of clinical utility. The authors have several recommendations to address these issues. Machine learning methods offer great promise for fast and accurate detection and prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest radiographs (CXR) and chest computed tomography (CT) images. Many articles have been published in 2020 describing new machine learning-based models for both of these tasks, but it is unclear which are of potential clinical utility. In this systematic review, we consider all published papers and preprints, for the period from 1 January 2020 to 3 October 2020, which describe new machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images. All manuscripts uploaded to bioRxiv, medRxiv and arXiv along with all entries in EMBASE and MEDLINE in this timeframe are considered. Our search identified 2,212 studies, of which 415 were included after initial screening and, after quality screening, 62 studies were included in this systematic review. Our review finds that none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases. This is a major weakness, given the urgency with which validated COVID-19 models are needed. To address this, we give many recommendations which, if followed, will solve these issues and lead to higher-quality model development and well-documented manuscripts.Peer reviewe

The impact of imputation quality on machine learning classifiers for datasets with missing values

Background: Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missing values are typically imputed using established methods, followed by classification of the now complete samples. The focus of the machine learning researcher is to optimise the classifier’s performance. Methods: We utilise three simulated and three real-world clinical datasets with different feature types and missingness patterns. Initially, we evaluate how the downstream classifier performance depends on the choice of classifier and imputation methods. We employ ANOVA to quantitatively evaluate how the choice of missingness rate, imputation method, and classifier method influences the performance. Additionally, we compare commonly used methods for assessing imputation quality and introduce a class of discrepancy scores based on the sliced Wasserstein distance. We also assess the stability of the imputations and the interpretability of model built on the imputed data. Results: The performance of the classifier is most affected by the percentage of missingness in the test data, with a considerable performance decline observed as the test missingness rate increases. We also show that the commonly used measures for assessing imputation quality tend to lead to imputed data which poorly matches the underlying data distribution, whereas our new class of discrepancy scores performs much better on this measure. Furthermore, we show that the interpretability of classifier models trained using poorly imputed data is compromised. Conclusions: It is imperative to consider the quality of the imputation when performing downstream classification as the effects on the classifier can be considerable