Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Objective To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease. Design Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group. Data sources PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020. Study selection Studies that developed or validated a multivariable covid-19 related prediction model. Data extraction At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). Results 37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models. Conclusion Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. Systematic review registration Protocol https://osf.io/ehc47/, registration https://osf.io/wy245. Readers’ note This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.

https://www.bmj.com/content/bmj/369/bmj.m1328.full.pdf

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

IEEE Transactions on Pattern Analysis and Machine Intelligence

Pattern recognition

Biophysics of Computation

Temporally structured sources may arise in blind equalization as the result of coding schemes. In this context, a promising approach involves the use of correntropy, which is capable of exploring both the statistical information and the temporal structure of the signals. In this work, we perform a detailed analysis of the correntropy-based criterion for equalization, giving a special attention to the effect of the adjustable parameters as well as to the comparison with the analytical correntropy measure, whose formal derivation is another contribution of this work. The experimental results indicate the attainable performance and the influence of the main parameters. Keywords— Adaptive filtering, Blind channel equalization, Information-theoretic learning, Correntropy.

Analysis of the Correntropy-Based Criterion for Blind Equalization with Precoded Sources

In the Blind Source Separation problem, Post-Nonlinear models are among the few nonlinear type of mixtures that may be separated by independent component analysis methods. However, such methods usually involve higher order statistics, neural networks or even metaheuristics. In this paper, we propose a simple separation method based on the gradient-descent approach, using elements of two classical second order statistic-based methods, AMUSE and SOBI. Adaptation is per-formed in two stages, the linear and the nonlinear one. Necessary conditions are that the sources be temporally colored and certain constraints on the separation structure be met. First results show a good performance of the proposed algorithm.

An SOS-Based Algorithm for Source Separation in Nonlinear Mixtures

Motor imagery (MI) has been increasingly studied for neurorehabilitation purposes. However, issues such as large intra- and inter-subject variability still limit its practical, and more clinical, applications. Seeking appropriate features for MI-neuromodulation training is thus a crucial step. This work presents a protocol that selects features related to the MI mental patterns maximizing consistency (i.e., minimizing variability) across two recordings in different days. We apply our methodology to 3 healthy adults and 3 children with cerebral palsy, illustrating its feasibility for MI training protocols.

Feature Consistency Criterion for Motor Imagery-Based Neuromodulation

Resumo Neste trabalho, apresenta-se uma interface cérebro-computador baseada em imaginação de movimento e analisa-se seu desempenho. A interface tem por fundamentos atributos espectrais e um classificador linear, e realiza um processo de seleção de eletrodos baseado em um wrapper progressivo. Os resultados mostram que a estratégia adotada leva a um bom desempenho se comparada à literatura da área.

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Fundamentos do Projeto de Interfaces Cérebro-Computador Baseadas em Imaginação de Movimento

In dimension reduction problems, the adopted technique may produce disparities between the representation errors of two or more different groups. For instance, in the projected space, a specific class can be better represented in comparison with the other ones. Depending on the situation, this unfair result may introduce ethical concerns. Aiming at overcoming this inconvenience, a fairness measure can be considered when performing dimension reduction through Principal Component Analysis. However, a solution that increases fairness tends to increase the reconstruction error. In other words, there is a trade-off between equity and performance. In this context, this paper proposes to address this trade-off in Fair Principal Component Analysis problems by means of a multi-objective-based approach. For this purpose, we adopt a fairness measure associated with the disparity between the representation errors of different groups. Moreover, we investigate if the solution of a classical Principal Component Analysis can be used to find a fair projection. Numerical experiments attest that a fairer result can be achieved with a very small loss in the reconstruction error.

Romis Attux

Papers

Analysis of the Correntropy-Based Criterion for Blind Equalization with Precoded Sources

An SOS-Based Algorithm for Source Separation in Nonlinear Mixtures

Feature Consistency Criterion for Motor Imagery-Based Neuromodulation

Fundamentos do Projeto de Interfaces Cérebro-Computador Baseadas em Imaginação de Movimento

A novel multi-objective-based approach to analyze trade-offs in Fair Principal Component Analysis