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Breast cancer is a global public health dilemma and the most prevalent cancer in the world. Effective treatment plans improve patient survival rates and well-being. The five-year survival rate is often used to develop treatment selection and survival prediction models. However, unlike other types of cancer, breast cancer patients can have long survival rates. Therefore, the authors propose a novel two-level framework to provide clinical decision support for treatment selection contingent on survival prediction.

The first level classifies patients into different survival periods using machine learning algorithms. The second level has two models with different survival rates (five-year and ten-year). Thus, based on the classification results of the first level, the authors employed Bayesian networks (BNs) to infer the effect of treatment on survival in the second level.

The authors validated the proposed approach with electronic health record data from the TriNetX Research Network. For the first level, the authors obtained 85% accuracy in survival classification. For the second level, the authors found that the topology of BNs using Causal Minimum Message Length had the highest accuracy and area under the ROC curve for both models. Notably, treatment selection substantially impacted survival rates, implying the two-level approach better aided clinical decision support on treatment selection.

The authors have developed a reference tool for medical practitioners that supports treatment decisions and patient education to identify patient treatment preferences and to enhance patient healthcare.

Clinical notes typically contain medical jargons and specialized words and phrases that are complicated and technical to most people, which is one of the most challenging obstacles in health information dissemination to consumers by healthcare providers. The authors aim to investigate how to leverage machine learning techniques to transform clinical notes of interest into understandable expressions.

The authors propose a natural language processing pipeline that is capable of extracting relevant information from long unstructured clinical notes and simplifying lexicons by replacing medical jargons and technical terms. Particularly, the authors develop an unsupervised keywords matching method to extract relevant information from clinical notes. To automatically evaluate completeness of the extracted information, the authors perform a multi-label classification task on the relevant texts. To simplify lexicons in the relevant text, the authors identify complex words using a sequence labeler and leverage transformer models to generate candidate words for substitution. The authors validate the proposed pipeline using 58,167 discharge summaries from critical care services.

The results show that the proposed pipeline can identify relevant information with high completeness and simplify complex expressions in clinical notes so that the converted notes have a high level of readability but a low degree of meaning change.

The proposed pipeline can help healthcare consumers well understand their medical information and therefore strengthen communications between healthcare providers and consumers for better care.

An innovative pipeline approach is developed to address the health literacy problem confronted by healthcare providers and consumers in the ongoing digital transformation process in the healthcare industry.

The purpose of this paper is to describe an extension of the boundary element method (BEM) and the dual reciprocity boundary element method (DRBEM) formulations developed for one- and two-dimensional steady-state problems, to analyse transient convection–diffusion problems associated with first-order chemical reaction.

The mathematical modelling has used a dual reciprocity approximation to transform the domain integrals arising in the transient equation into equivalent boundary integrals. The integral representation formula for the corresponding problem is obtained from the Green’s second identity, using the fundamental solution of the corresponding steady-state equation with constant coefficients. The finite difference method is used to simulate the time evolution procedure for solving the resulting system of equations. Three different radial basis functions have been successfully implemented to increase the accuracy of the solution and improving the rate of convergence.

The numerical results obtained demonstrate the excellent agreement with the analytical solutions to establish the validity of the proposed approach and to confirm its efficiency.

Finally, the proposed BEM and DRBEM numerical solutions have not displayed any artificial diffusion, oscillatory behaviour or damping of the wave front, as appears in other different numerical methods.

Citizens are substantial stakeholders in every e-government system, thus their willingness to use and ability to access the system are critical. Unequal access and information and communication technology usage, which is known as digital divide, however has been identified as one of the major obstacles to the implementation of e-government system. As digital divide inhibits citizen’s acceptance to e-government, it should be overcome despite the lack of deep theoretical understanding on this issue. This research aimed to investigate the digital divide and its direct impact on e-government system success of local governments in Indonesia as well as indirect impact through the mediation role of trust. In order to get a comprehensive understanding of digital divide, this study introduced a new type of digital divide, the innovativeness divide.

The research problems were approached by applying two-stage sequential mixed method research approach comprising of both qualitative and quantitative studies. In the first phase, an initial research model was proposed based on a literature review. Semi-structured interview with 12 users of e-government systems was then conducted to explore and enhance this initial research model. Data collected in this phase were analyzed with a two-stage content analysis approach and the initial model was then amended based on the findings. As a result, a comprehensive research model with 16 hypotheses was proposed for examination in the second phase.

In the second phase, quantitative method was applied. A questionnaire was developed based on findings in the first phase. A pilot study was conducted to refine the questionnaire, which was then distributed in a national survey resulting in 237 useable responses. Data collected in this phase were analyzed using Partial Least Square based Structural Equation Modeling.

The results of quantitative analysis confirmed 13 hypotheses. All direct influences of the variables of digital divide on e-government system success were supported. The mediating effects of trust in e-government in the relationship between capability divide and e-government system success as well as in the relationship between innovativeness divide and e-government system success were supported, but was rejected in the relationship between access divide and e-government system success. Furthermore, the results supported the moderating effects of demographic variables of age, residential place, and education.

This research has both theoretical and practical contributions. The study contributes to the developments of literature on digital divide and e-government by providing a more comprehensive framework, and also to the implementation of e-government by local governments and the improvement of e-government Readiness Index of Indonesia.

This paper aims to show the pragmatic studies that examine whether novel COVID-19 affects the national and international stock markets and reinforces the existing literature by highlighting the factors that are resultant from COVID 19.

The systematic literature review and bibliometric approach have been used in the study covering 585 selected articles published in journals of high repute from January 2020 to January 2022. The process of bibliometric analysis has been divided into three stages, namely, assembling, arranging and assessing. From the Scopus database, one of the most reliable and authentic database total of 585 records were collected, out of which 12 were specifically focused on communities, and information gathered in the comma-separated value documents design was compared and interpreted based on year, document types, subject area, country and research fields with the help of graphs and pie charts. The study has analyzed fact-based and reliable studies to draw inferences from existing literature regarding the pandemic impacting the financial markets. In the extant study, an attempt has been made to explore the factors that are resultant from the COVID-19 pandemic and affects the stock market performance, which can be further classified into a few common factors by using factor analysis.

It originated from the majority of the studies that the stock market retorted destructively to the upsurge in the figure of COVID-19 cases and fatalities. It also emphasized that the market has reacted differently in comparison to earlier catastrophes such as the great depression of 2008 and the Spanish flu. Various factors such as fear of losing capital, standstill economy, lower valuation, increased mortality rate, halt in business operations, retrenchment, trade war, liquidity issues, panic buying and selling, digitalization, negative media coverage, government interference, financial behavior of investors, hoarding of COVID supplies, promotion of start-up in health-care and education sector, news bulletins, prevention campaigns, use of medical devices and COVID-19 vaccination, etc. have been conferred from the studies that have an immediate consequence on the actions of investors in the stock market. It was further highlighted in the study that the Indian stock market has been less explored in respect of implications of COVID-19 contagion as the majority of studies were based on either international stock exchanges or combinations of varied nation’s stock markets. It was witnessed in the interpretation section that the number of studies is increasing at a fast pace as new variants of COVID-19 are emerging over time. Significant contribution has been done in enhancing the literature on COVID-19 and the stock market by China and the USA. The maximum contribution in this domain has been done in the form of articles in the present literature. Few studies were focusing on communities, so the present study will try to fill this research gap to some extent.

This conceptual paper is demarcated by unsatisfactory analyses of writings from multi-discipline to get a comprehensive scope of notional understanding. Furthermore, there is a perchance that some other imperative phenomena or variables that prejudiced trading bustle have not been captured by present reviews of research papers. The influences of other macroeconomic variables should be explored to understand the concrete results of this pandemic.

Most of the studies were based on foreign stock exchanges, so there is an opportunity to explore the Indian stock market concerning the implications of the coronavirus pandemic. In the literature, it was examined that short-term studies have been undertaken, which cannot determine the long-term implications of COVID-19. Over time, besides COVID-19, various other factors have started impacting the stock market, so it has become difficult to examine the influence of COVID-19 on the stock market in isolation.

The study will be helpful for future learnings in the arena of the stock market as it provides vast exposure to the present literature related to the impact of COVID-19 on economic markets. On the other hand, investors will also become aware of factors that are resultant of COVID-19 and will take the right decisions to save their investments in light of pandemic implications. The extensive review of studies will also help enterprising communities to take judicial steps to remain active in the period of economic slowdown.

The paper provides significant implications to the investors in the stock market, and it will provide useful insight to improve their returns on their portfolios. The learning from the study will help investors to take fruitful decisions considering the uncertainty during the pandemic period. The inferences drawn from rich existing literature will be guiding enterprises to take timely actions to avoid the situation of loss in the market and adapt new models to ensure continuity of business operations. Different markets had reacted differently, so investors need to be cautious before taking trading decisions.