Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

Digital processing of speech signals

Slime mould algorithm: A new method for stochastic optimization

https://fada.birzeit.edu/bitstream/20.500.11889/5314/1/Hybrid%20Whale%20Optimization%20Algorithm%20with%20Simulated%20Annealing%20for%20FS.pdf

Hybrid Whale Optimization Algorithm with Simulated Annealing for Feature Selection

Binary grey wolf optimization approaches for feature selection

Binary ant lion approaches for feature selection

Feature selection algorithm explores the data to eliminate noisy, irrelevant, redundant data, and simultaneously optimize the classification performance. In this paper, a classification accuracy-based fitness function is proposed by gray-wolf optimizer to find optimal feature subset. Gray-wolf optimizer is a new evolutionary computation technique which mimics the leadership hierarchy and hunting mechanism of gray wolves in nature. The aim of the gray wolf optimization is find optimal regions of the complex search space through the interaction of individuals in the population. Compared with particle swarm optimization (PSP) and Genetic Algorithms (GA) over a set of UCI machine learning data repository, the proposed approach proves better performance in both classification accuracy and feature size reduction. Moreover, the gray wolf optimization approach proves much robustness against initialization in comparison with PSO and GA optimizers.

Feature Subset Selection Approach by Gray-Wolf Optimization

In this paper, a variant of gray wolf optimization (GWO) that uses reinforcement learning principles combined with neural networks to enhance the performance is proposed. The aim is to overcome, by reinforced learning, the common challenge of setting the right parameters for the algorithm. In GWO, a single parameter is used to control the exploration/exploitation rate, which influences the performance of the algorithm. Rather than using a global way to change this parameter for all the agents, we use reinforcement learning to set it on an individual basis. The adaptation of the exploration rate for each agent depends on the agent’s own experience and the current terrain of the search space. In order to achieve this, experience repository is built based on the neural network to map a set of agents’ states to a set of corresponding actions that specifically influence the exploration rate. The experience repository is updated by all the search agents to reflect experience and to enhance the future actions continuously. The resulted algorithm is called experienced GWO (EGWO) and its performance is assessed on solving feature selection problems and on finding optimal weights for neural networks algorithm. We use a set of performance indicators to evaluate the efficiency of the method. Results over various data sets demonstrate an advance of the EGWO over the original GWO and over other metaheuristics, such as genetic algorithms and particle swarm optimization.

https://bura.brunel.ac.uk/bitstream/2438/13560/4/FullText.pdf

Experienced Gray Wolf Optimization Through Reinforcement Learning and Neural Networks

This work was partially supported by the IPROCOM Marie Curie initial training network, funded
through the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework
Programme FP7/2007-2013/ under REA grants agreement No. 316555, and by the Romanian
National Authority for Scientific Research, CNDIUEFISCDI, project number PN-II-PT-PCCA-2011-3.2-
0917. The funders had no role in study design, data collection and analysis, decision to publish, or
preparation of the manuscript.

/pdf/feature-selection-via-chaotic-antlion-optimization-3p1wty1vem.pdf

Hossam M. Zawbaa

Papers

Binary grey wolf optimization approaches for feature selection

Binary ant lion approaches for feature selection

Feature Subset Selection Approach by Gray-Wolf Optimization

Experienced Gray Wolf Optimization Through Reinforcement Learning and Neural Networks

Feature Selection via Chaotic Antlion Optimization.