Intervalos de predição para redes neurais artificiais via regressão não linear

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Ciência da Computação.Este trabalho descreve a aplicação de uma técnica de regressão não linear (mínimos quadrados) para obter predições intervalares em redes neurais artificiais (RNA#s). Através de uma simulação de Monte Carlo é mostrada uma maneira de escolher um ajuste de parâmetros (pesos) para uma rede neural, de acordo com um critério de seleção que é baseado na magnitude dos intervalos de predição fornecidos pela rede. Com esta técnica foi possível obter as predições intervalares com amplitude desejada e com probabilidade de cobertura conhecida, de acordo com um grau de confiança escolhido. Os resultados e as discussões associadas indicam ser possível e factível a obtenção destes intervalos, fazendo com que a resposta das redes seja mais informativa e consequentemente aumentando sua aplicabilidade. A implementação computacional está disponível em www.inf.ufsc.br/~dandrade. This work describes the application of a nonlinear regression technique (least squares) to create prediction intervals on artificial neural networks (ANN´s). Through Monte Carlo#s simulations it is shown a way of choosing the set of parameters (weights) to a neural network, according to a selection criteria based on the magnitude of the prediction intervals provided by the net. With this technique it is possible to obtain the prediction intervals with the desired amplitude and with known coverage probability, according to the chosen confidence level. The associated results and discussions indicate to be possible and feasible to obtain these intervals, thus making the network response more informative and consequently increasing its applicability. The computational implementation is available in www.inf.ufsc.br/~dandrade

SARS-CoV-2 infection in patients with inflammatory bowel disease: comparison between the first and second pandemic waves

Background: In Italy, the incidence of SARS-CoV-2 infection peaked in April and November 2020, defining two pandemic waves of coronavirus disease 2019 (COVID-19). This study compared the characteristics and outcomes of patients with inflammatory bowel disease (IBD) and SARS-CoV-2 infections between pandemic waves. Methods: Observational longitudinal study of IBD patients with SARS-CoV-2 infection. Patients with established diagnoses of IBD and of SARS-CoV-2 infection were consecutively enrolled in two periods: (i) first wave, from 1 March 2020 to 31 May 2020; and (ii) second wave, from 15 September to 15 December 2020. Results: We enrolled 937 IBD patients (219 in the first wave, 718 in the second wave). Patients of the first wave were older (mean ± SD: 46.3 ± 16.2 vs. 44.1 ± 15.4 years, p = 0.06), more likely to have ulcerative colitis (58.0% vs. 44.4%, p < 0.001) and comorbidities (48.9% vs. 38.9%; p < 0.01), and more frequently residing in Northern Italy (73.1% vs. 46.0%, p < 0.001) than patients of the second wave. There were no significant differences between pandemic waves in sex (male: 54.3% vs. 53.3%, p = 0.82) or frequency of active IBD (44.3% vs. 39.0%, p = 0.18). The rates of negative outcomes were significantly higher in the first than second wave: pneumonia (27.8% vs. 11.7%, p < 0.001), hospital admission (27.4% vs. 9.7%, p < 0.001), ventilatory support (11.9% vs. 5.4%, p < 0.003) and death (5.5% vs. 1.8%, p < 0.007). Conclusion: Between the first and second SARS-CoV-2 pandemic waves, demographic, clinical and geographical features of IBD patients were different as were the symptoms and outcomes of infection. These differences are likely due to the different epidemiological situations and diagnostic possibilities between the two waves

Therapies for inflammatory bowel disease do not pose additional risks for adverse outcomes of SARS-CoV-2 infection: an IG-IBD study

Background Older age and comorbidities are the main risk factors for adverse COVID-19 outcomes in patients with inflammatory bowel disease (IBD). The impact of IBD medications is still under investigation. Aims To assess risk factors for adverse outcomes of COVID-19 in IBD patients and use the identified risk factors to build risk indices. Methods Observational cohort study. Univariable and multivariable logistic regression was used to identify risk factors associated with pneumonia, hospitalisation, need for ventilatory support, and death. Results Of the 937 patients (446 with ulcerative colitis [UC]) evaluated, 128 (13.7%) had asymptomatic SARS-CoV-2 infection, 664 (70.8%) had a favourable course, and 135 (15.5%) had moderate or severe COVID-19. In UC patients, obesity, active disease and comorbidities were significantly associated with adverse outcomes. In patients with Crohn's disease (CD), age, obesity, comorbidities and an additional immune-mediated inflammatory disease were identified as risk factors. These risk factors were incorporated into two indices to identify patients with UC or CD with a higher risk of adverse COVID-19 outcomes. In multivariable analyses, no single IBD medication was associated with poor COVID-19 outcomes, but anti-TNF agents were associated with a lower risk of pneumonia in UC, and lower risks of hospitalisation and severe COVID-19 in CD. Conclusion The course of COVID-19 in patients with IBD is similar to that in the general population. IBD patients with active disease and comorbidities are at greater risk of adverse COVID-19 outcomes. IBD medications do not pose additional risks. The risk indices may help to identify patients who should be prioritised for COVID-19 re-vaccination or for therapies for SARS-CoV-2 infection

Therapies for inflammatory bowel disease do not pose additional risks for adverse outcomes of SARS-CoV-2 infection: an IG-IBD study

none70noBackground: Older age and comorbidities are the main risk factors for adverse COVID-19 outcomes in patients with inflammatory bowel disease (IBD). The impact of IBD medications is still under investigation. Aims: To assess risk factors for adverse outcomes of COVID-19 in IBD patients and use the identified risk factors to build risk indices. Methods: Observational cohort study. Univariable and multivariable logistic regression was used to identify risk factors associated with pneumonia, hospitalisation, need for ventilatory support, and death. Results: Of the 937 patients (446 with ulcerative colitis [UC]) evaluated, 128 (13.7%) had asymptomatic SARS-CoV-2 infection, 664 (70.8%) had a favourable course, and 135 (15.5%) had moderate or severe COVID-19. In UC patients, obesity, active disease and comorbidities were significantly associated with adverse outcomes. In patients with Crohn's disease (CD), age, obesity, comorbidities and an additional immune-mediated inflammatory disease were identified as risk factors. These risk factors were incorporated into two indices to identify patients with UC or CD with a higher risk of adverse COVID-19 outcomes. In multivariable analyses, no single IBD medication was associated with poor COVID-19 outcomes, but anti-TNF agents were associated with a lower risk of pneumonia in UC, and lower risks of hospitalisation and severe COVID-19 in CD. Conclusion: The course of COVID-19 in patients with IBD is similar to that in the general population. IBD patients with active disease and comorbidities are at greater risk of adverse COVID-19 outcomes. IBD medications do not pose additional risks. The risk indices may help to identify patients who should be prioritised for COVID-19 re-vaccination or for therapies for SARS-CoV-2 infection.noneBezzio C.; Armuzzi A.; Furfaro F.; Ardizzone S.; Milla M.; Carparelli S.; Orlando A.; Caprioli F.A.; Castiglione F.; Vigano C.; Ribaldone D.G.; Zingone F.; Monterubbianesi R.; Imperatore N.; Festa S.; Daperno M.; Scucchi L.; Ferronato A.; Pastorelli L.; Balestrieri P.; Ricci C.; Cappello M.; Felice C.; Fiorino G.; Saibeni S.; Coppini F.; Alvisi P.; Gerardi V.; Variola A.; Mazzuoli S.; Lenti M.V.; Pugliese D.; Allocca M.; Ferretti F.; Roselli J.; Bossa F.; Giuliano A.; Piazza N.; Manes G.; Sartini A.; Buda A.; Micheli F.; Ciardo V.; Casella G.; Viscido A.; Bodini G.; Casini V.; Soriano A.; Amato A.; Grossi L.; Onali S.; Rottoli M.; Spagnuolo R.; Baroni S.; Cortelezzi C.C.; Baldoni M.; Vernero M.; Scaldaferri F.; Maconi G.; Guarino A.D.; Palermo A.; D'Inca R.; Scribano M.L.; Biancone L.; Carrozza L.; Ascolani M.; Costa F.; Di Sabatino A.; Zammarchi I.; Gottin M.; Conforti F.S.Bezzio, C.; Armuzzi, A.; Furfaro, F.; Ardizzone, S.; Milla, M.; Carparelli, S.; Orlando, A.; Caprioli, F. A.; Castiglione, F.; Vigano, C.; Ribaldone, D. G.; Zingone, F.; Monterubbianesi, R.; Imperatore, N.; Festa, S.; Daperno, M.; Scucchi, L.; Ferronato, A.; Pastorelli, L.; Balestrieri, P.; Ricci, C.; Cappello, M.; Felice, C.; Fiorino, G.; Saibeni, S.; Coppini, F.; Alvisi, P.; Gerardi, V.; Variola, A.; Mazzuoli, S.; Lenti, M. V.; Pugliese, D.; Allocca, M.; Ferretti, F.; Roselli, J.; Bossa, F.; Giuliano, A.; Piazza, N.; Manes, G.; Sartini, A.; Buda, A.; Micheli, F.; Ciardo, V.; Casella, G.; Viscido, A.; Bodini, G.; Casini, V.; Soriano, A.; Amato, A.; Grossi, L.; Onali, S.; Rottoli, M.; Spagnuolo, R.; Baroni, S.; Cortelezzi, C. C.; Baldoni, M.; Vernero, M.; Scaldaferri, F.; Maconi, G.; Guarino, A. D.; Palermo, A.; D'Inca, R.; Scribano, M. L.; Biancone, L.; Carrozza, L.; Ascolani, M.; Costa, F.; Di Sabatino, A.; Zammarchi, I.; Gottin, M.; Conforti, F. S