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Innovations in financial services have given rise to Financial Technology (FinTech) as a disruptive technology which is now shaping the future of the financial sector through services like crowdfunding, peer-to-peer loans, and alternative underwriting platforms. Given the status of the web as a vital information resource, accessible and usable online FinTech services can aid people with disabilities in taking full advantage of the presented financial services to support them in basic needs like accessing high-quality care. This study set out to analyze the accessibility and usability of 32 Saudi FinTech websites by means of various automated tools and manual inspection. The results revealed the investigated websites to have a significant number of accessibility and usability problems that cause prejudice against disabled individuals and foster an overall poor user experience. Thus, more work is needed to disseminate knowledge about the importance of developing suitable FinTech websites and services to assist disabled people.

In the logistics service industry, the rapid advancement of Information Technology (IT) has cultivated a competitive dynamic that promotes uptake of logistics information technologies by Logistics Service Providers (LSPs). However, there remain many LSPs who have yet to embrace IT and thus have not realized their fullest competitiveness and financial success. This research conceptualizes the role of IT in logistics operations and investigates the degree to which IT, its usage, and its capabilities can affect LSP performance and competitiveness. The research interviews a logistics operations manager and the findings support that a firm’s performance and competitive advantage are influenced by IT usage and (especially) capability. As IT capability complements service processes and allows for innovation in services, this research and suggested future research provide not only theoretical and technological but also managerial implications.

Background The COVID-19 pandemic remains an immediate and present concern, yet as of now there is still no approved therapeutic available for the treatment of COVID-19.This study aimed to investigate and report evidence concerning demographic characteristics and currently-used medications that contribute to the ultimate outcomes of COVID-19 ICU patients. Methods A retrospective cohort study was conducted among all COVID-19 patients in the Intensive Care Unit (ICU) of Asir Central Hospital in Saudi Arabia between the 1st and 30th of June 2020. Data extracted from patients’ medical records included their demographics, home medications, medications used to treat COVID-19, treatment durations, ICU stay, hospital stay, and ultimate outcome (recovery or death).Descriptive statistics and regression modelling were used to analyze and compare the results. The study was approved by the Institutional Ethics Committees at both Asir Central Hospital and King Khalid University. Results A total of 118 patients with median age of 57 years having definite clinical and disease outcomes were included in the study. Male patients accounted for 87% of the study population, and more than 65% experienced at least one comorbidity. The mean hospital and ICU stay was 11.4 and 9.8 days, respectively. The most common drugs used were tocilizumab (31.4%), triple combination therapy (45.8%), favipiravir (56.8%), dexamethasone (86.7%), and enoxaparin (83%). Treatment with enoxaparin significantly reduced the length of ICU stay (p = 0.04) and was found to be associated with mortality reduction in patients aged 50−75 (p = 0.03), whereas the triple regimen therapy and tocilizumab significantly increased the length of ICU stay in all patients (p = 0.01, p = 0.02 respectively). Conclusion COVID-19 tends to affect males more significantly than females. The use of enoxaparin is an important part of COVID-19 treatment, especially for those above 50 years of age, while the use of triple combination therapy and tocilizumab in COVID-19 protocols should be reevaluated and restricted to patients who have high likelihood of benefit.

WOS:000556407600004 In this article, an algorithm is proposed for creating an ensemble classifier. The name of the algorithm is the F-score subspace method (FsBoost). According to this method, the features are selected with the F-score and classified with different or the same classifiers. In the next step, the ensemble classifier is created. Two versions that are named FsBoost.V1 and FsBoost.V2 have been developed based on classification by the same or different classifiers. According to the results obtained, the results are consistent with the literature. Besides, a higher accuracy rate is obtained compared with many algorithms in the literature. The algorithm is fast because it has a few steps. It is thought that the algorithm will be successful due to these advantages.

The population models allow for a better understanding of the dynamical interactions with the environment and hence can provide a way for understanding the population changes. They are helpful in studying the biological invasions, environmental conservation and many other applications. These models become more complicated when accounting for the stochastic and/or random variations due to different sources. In the current work, a spectral technique is suggested to analyze the stochastic population model with random parameters. The model contains mixed sources of uncertainties, noise and uncertain parameters. The suggested algorithm uses the spectral decompositions for both types of randomness. The spectral techniques have the advantages of high rates of convergence. A deterministic system is derived using the statistical properties of the random bases. The classical analytical and/or numerical techniques can be used to analyze the deterministic system and obtain the solution statistics. The technique presented in the current work is applicable to many complex systems with both stochastic and random parameters. It has the advantage of separating the contributions due to different sources of uncertainty. Hence, the sensitivity index of any uncertain parameter can be evaluated. This is a clear advantage compared with other techniques used in the literature.

Heart failure is considered one of the leading cause of death around the world. The diagnosis of heart failure is a challenging task especially in under-developed and developing countries where there is a paucity of human experts and equipments. Hence, different researchers have developed different intelligent systems for automated detection of heart failure. However, most of these methods are facing the problem of overfitting i.e. the recently proposed methods improved heart failure detection accuracy on testing data while compromising heart failure detection accuracy on training data. Consequently, the constructed models overfit to the testing data. In order, to come up with an intelligent system that would show good performance on both training and testing data, in this paper we develop a novel diagnostic system. The proposed diagnostic system uses random search algorithm (RSA) for features selection and random forest model for heart failure prediction. The proposed diagnostic system is optimized using grid search algorithm. Two types of experiments are performed to evaluate the precision of the proposed method. In the first experiment, only random forest model is developed while in the second experiment the proposed RSA based random forest model is developed. Experiments are performed using an online heart failure database namely Cleveland dataset. The proposed method is efficient and less complex than conventional random forest model as it produces 3.3% higher accuracy than conventional random forest model while using only 7 features. Moreover, the proposed method shows better performance than five other state of the art machine learning models. In addition, the proposed method achieved classification accuracy of 93.33% while improving the training accuracy as well. Finally, the proposed method shows better performance than eleven recently proposed methods for heart failure detection.

MotivationDrug-drug interactions (DDIs) are complex processes which may depend on many clinical and non-clinical factors. Identifying and distinguishing ways in which drugs interact remains a challenge. To minimize DDIs and to personalize treatment based on accurate stratification of patients, it is crucial that mechanisms of interaction can be identified. Most DDIs are a consequence of metabolic mechanisms of interaction, but DDIs with different mechanisms occur less frequently and are therefore more difficult to identify.ResultsWe developed a method (D4) for computationally identifying potential DDIs and determining whether they interact based on one of eleven mechanisms of interaction. D4 predicts DDIs and their mechanisms through features that are generated through a deep learning approach from phenotypic and functional knowledge about drugs, their side-effects and targets. Our findings indicate that our method is able to identify known DDIs with high accuracy and that D4 can determine mechanisms of interaction. We also identify numerous novel and potential DDIs for each mechanism of interaction and evaluate our predictions using DDIs from adverse event reporting systems.Availabilityhttps://github.com/bio-ontology-research-group/D4Contactarnoor@kau.edu.sa and robert.hoehndorf@kaust.edu.sa

Heart failure (HF) is considered a deadliest disease worldwide. Therefore, different intelligent medical decision support systems have been widely proposed for detection of HF in literature. However, low rate of accuracies achieved on the HF data is a major problem in these decision support systems. To improve the prediction accuracy, we have developed a feature-driven decision support system consisting of two main stages. In the first stage, χ2 statistical model is used to rank the commonly used 13 HF features. Based on the χ2 test score, an optimal subset of features is searched using forward best-first search strategy. In the second stage, Gaussian Naive Bayes (GNB) classifier is used as a predictive model. The performance of the newly proposed method (χ2-GNB) is evaluated by using an online heart disease database of 297 subjects. Experimental results show that our proposed method could achieve a prediction accuracy of 93.33%. The developed method (i.e., χ2-GNB) improves the HF prediction performance of GNB model by 3.33%. Moreover, the newly proposed method also shows better performance than the available methods in literature that achieved accuracies in the range of 57.85–92.22%.