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In sentiment analysis, CNN automatically extracts local features (such as vocabulary combinations and phrase patterns) from text through convolutional layers, reduces data dimensionality and preserves important information through pooling layers, and finally classifies through fully connected layers. Its advantage lies in its ability to capture the spatial hierarchy of text, making it suitable for processing fixed length text data. RNN processes text through serialization and can capture temporal dependencies in the text, meaning that the sentiment of the current word may be influenced by the preceding text. However, traditional RNNs suffer from gradient vanishing or exploding problems, which limit their ability to handle long texts. Long short-term memory (LSTM) and gated recurrent Unit (GRU), as improved versions of RNN, effectively alleviate this problem by introducing gating mechanisms, enabling the model to better capture long-range dependencies and be suitable for complex and lengthy text analysis in the medical field. This paper further explores the practical application of deep learning models in the monitoring of public opinion in healthcare networks, including how to monitor public reactions to healthcare policies, health information, and healthcare services through sentiment analysis. In addition, the paper discusses how sentiment analysis results can be used to identify and mitigate group polarization in online public opinion, and how sentiment analysis can be used to optimize healthcare delivery and policy making. This paper presents the challenges and future research directions of deep learning-based sentiment analysis in the monitoring of public opinion in healthcare networks, including the explainability of the model, data privacy protection, and cross-cultural sentiment analysis. Through this study, we expect to provide valuable insights to policymakers and professionals in the healthcare field to better understand and respond to the complex emotional dynamics in online public opinion.

One of the primary objectives of control charts is to accurately detect the occurrence of changes in statistical processes. This enables process analysts to identify the factors responsible for the change and take corrective actions. The clustering method is a practical approach that has been developed to estimate the time of change. In recent years, a new approach called weighted consensus clustering (WCC) has been introduced in the field of clustering analysis. This approach can be utilized to cluster different trends in process parameters, resulting in a final clustering that exhibits higher quality and stability compared to the initial clustering. One of the emerging aspects in the health care field is the use of new statistical approaches to rapid disease diagnosis. In this paper, a novel change point estimation scheme called WCC has been developed to detect changes in the diabetic patients’ processes. In addition, to demonstrate the advantages of the proposed scheme, its performance is compared with an existing method known as maximum likelihood of clusters (CMLE) through simulation studies as well as a real case study in the healthcare system.

Populations become increasingly feminized with age. Since older women are more vulnerable to poverty, they may find it more difficult than men to access health care. This study examines factors that may constrain older persons in Southeast Asia from meeting their health-care needs when sick. Our analysis of household survey data from Cambodia, the Philippines, and Viet Nam shows that women are more likely to have reported sickness or injury than men, a difference that is meaningful and statistically significant. While women in Cambodia and the Philippines are more likely to seek treatment than men, the gender difference is reversed in Viet Nam where the stigma and discrimination associated with some diseases may more strongly deter women. The probability of seeking treatment rises with age more sharply for women than men in all countries. However, for the subsample of elders, the gender difference is not significant.

In this paper, we evaluate the preventive behaviors for middle age people in Taiwan through survey wave comparisons for the period 1993–2007. We first investigate the determinants of preventive behavior and how their effects change over time by using the seemingly unrelated bivariate probit model. Evidence shows that income and education levels do have a significantly positive relation with preventive behavior models, while reported poor health, inpatient and outpatient services, and regions are essential determinants of preventive behaviors. Then, we investigate the impact of Taiwan’s National Health Insurance (NHI) on preventive behaviors based on the difference-in-difference (DID) analysis. The results show a lack of evidence for the moral hazard effect on preventive care and regular exercise panels over the period 1993–2007.

Biofuels: Fact or Fantasy?.

Singapore — The Biopolis of Asia.

Complementary Therapies for Cancer Patients.

Medicine Without Border — Care Before Business.

The SingaporeMedicine Story.

Since Esping-Andersen [(1990). The three worlds of welfare capitalism. New York: Wiley] classified 18 capitalist countries into "three worlds of welfare capitalism", comparative studies of welfare have been dominated by his work and the responses to it. This paper focuses on two important responses. Firstly, there is a criticism that East Asian welfare regimes have not been given sufficient attention in the study by Esping-Andersen (1990). Secondly, analysts have doubts about the assumption that welfare regimes have a high degree of internal policy cohesion. To make contributions to these two responses, two analytical tasks are conducted. Firstly, new health care, education and leave benefits policy domains covering 16 East Asian and European capitalist countries/territories are built. Secondly, evidence generated from these policy domains is used to inform the discussion of two arguments: (i) there is a lack of essential conditions for East Asian countries/territories to form their own unique category of welfare regime in the areas of education, health care and leave benefits; and (ii) welfare regimes have a high degree of internal policy cohesion.

Patient satisfaction in the developing world has not received sufficient interest in the healthcare literature. More importantly, models predicting patient satisfaction seldom incorporates patient perspectives into their measurement providing ill-suited metrics for healthcare administrators to make decisive decisions concerning service quality improvements. The current analysis utilizes original data collected from 336 patients in Jordan. This paper tests whether service quality (SERVQUAL) is suitable to be used in the new context and concluded that SERVQUAL is reliable and valid in supplying stakeholders with reliable and valid information concerning patient satisfaction. Further, the analysis suggests a large gap between perceived and expected services performed by Jordanian hospitals signalling the need to service quality enhancement. The study calls for the adoption of agile and rapid patient screening models in emergency departments to reduce wait times and decrease the number of patients left without being seen. This study constitutes an emerging attempt at better understanding how service quality influences patient satisfaction, and more importantly, introduce affordable and cost-effective solutions capable of improving satisfaction in the short run.

In the last decade or so, there has been a growing interest in an area researchers are calling social entrepreneurship, a movement spearheaded by individuals with a desire to make the world a better place. This paper describes the structure and process of international development in Africa from the perspective of a social entrepreneur. The authors address the opportunities and challenges faced by social entrepreneurs as they attempt to affect large-scale social change. The result of this study is a unique development model that provides tools for the social entrepreneur to address problems and build capacity and sustainability within the African context.

Incumbents’ inertia in the face of disruptive innovations has been emphasised in prior literature. The relevance of inertia is particularly topical in the context of digital transformation. However, incumbents may be able to invest in disruptive digital innovations appropriately if they possess the motivation and ability to do so. In this paper, I use three streams of research in order to investigate contextual, organisational, and individual antecedents of incumbents’ motivation and ability to adopt and use potentially disruptive digital innovations in health care: institutional theory, the resource-based view, and technology acceptance literature. I employ factor analyses and logistic regressions to test the impact on the adoption and usage of telemedicine applications using a dataset of 9,196 European general practitioners. I examine B2B as well as B2C applications in order to determine the effect of the antecedents on different business models. My findings suggest that only isomorphic pressure, complementary assets, and perceived output quality significantly influence both adoption and usage as well as B2B and B2C business models in the same way. Formal institutions and individual factors yield ambiguous results. These findings provide important implications for the understanding of incumbents’ response to potentially disruptive digital innovations in regulated contexts.

In this paper, we develop a new method to determine a fuzzy similarity measure using fuzzy implication operators. We use this method to determine the fuzzy similarity between the two rankings of countries involving health security and health care. We then find a fuzzy similarity of countries involving the two rankings of countries with respect to national disaster and political disaster.

Modern technologies are now available to provide appropriate information and improved services in healthcare delivery. During the COVID-19 pandemic, these technologies have played a vital role in providing advanced and digital solutions. The main objective of this paper is to explore various modern technologies. This study further describes the significant applications of these technologies for the COVID-19 pandemic. In this paper, we have identified research papers on modern technologies applications for COVID-19 from the databases of Scopus, Google Scholar, Science direct, and Research Gate. In this paper, we have provided the significant challenges faced during the ongoing COVID-19 pandemic and the role of various modern technologies that can be used to take the challenges of the COVID-19 pandemic. The substantial benefits and limitations of these technologies are briefly discussed. Finally, the paper would provide essential details of these technologies and their significant role during the COVID-19 pandemic. These technological innovations are increasing rapidly to save human beings’ lives by providing innovative solutions that can convert doctors’ and researchers’ ideas into reality. Various software and digital applications are now available to manage these technologies to provide better healthcare services digitally.

Unintentional falls cause serious health problem and high medical cost, particularly among the elders. Efficient fall detection can ensure fallen subjects with timely rescue, less pain and lower health-care expense. However, the accuracy of the present fall detection system with single accelerometer does not meet the requirement of practical application. In this paper, a fall detection method using three wearable triaxial accelerometers and a decision-tree classifier is proposed. The three triaxial accelerometers are, respectively mounted on the head, the waist and the ankle to capture the acceleration signals of human movement. A Kalman filter is adopted to estimate the body tilt angle. After the features are extracted, the trained decision-tree model is used to predict the fall. The efficiency improvement is evidenced by the scripted and unscripted lateral fall experiments, involving five young healthy volunteers (three males and two females; age: 23.3 ± 1 years). The classification of fall and activities of daily living (ADL) achieve recall, precision and F-value of 93.1%, 95.9%, and 94.5%, respectively, and the system detects all falls during the extended unscripted trials. The experimental results indicate that the complementary movement information coming from three accelerometers can enhance the performance of fall detection. The proposed method is efficient, and it has remarkable improvements in comparison to the method of using one or two accelerometers.

The advancements in molecular biology have made biotechnology a billion-dollar business over the last two decades. Recent developments in instrumentation, nano-technology and information technology have provided the biomedical research community with enormous amounts of diverse information governing biological systems. Consequently, there is an urgent need for information storage, curation, analysis and retrieval (ISCAR) using bioinformatics tools. Though the very definition of bioinformatics is debatable, there is a general agreement about the importance of certain fundamental concepts. Broadly, bioinformatics is the marriage between modern biology and information technology to glean new knowledge from redundant databases. Bioinformatics helps researchers gather, standardize, combine and manipulate data to tease out the knowledge they contain. In future, it will guide in performing in silico biotechnological experiments to aid biomedical research and application. Hence "SILICO BIOTECH" highlights the simple relationships between different disciplines that govern the complex drug discovery process and its relevance in health care.

In 2002, the Chinese Communist Party (CCP) announced a goal of quadrupling per capita income by the year 2020. Starting at income levels of the year 2000, this would require a growth rate of 7.2 percent per annum in per capita income or close to 8.0 percent in GDP. Such unresolved and emerging problems as growing income disparities, increasing pollution, pressures on infrastructure, the inefficiency of stateowned enterprises, and political instability are often cited as reasons to doubt the attainability of the CCP's goal. However, China's progress in addressing fundamental constraints that might limit rapid economic growth augurs well for the success of its economic goals. Although there are disagreements about economic policy among top leaders, the continued transformation into a market economy and the promotion of increasing local autonomy in economic matters are not in doubt. In education, China has substantially increased the percentage of its workforce receiving a college education, and continuing growth in this investment in human capital could account for a large portion of the desired growth rate. In addition, the value of improvements in the quality of economic output unmeasured by GDP, such as advances in the quality of health care and education, could raise reported growth rates by as much as 60 percent. Finally, the government's increasing sensitivity to public opinion and issues of inequality and corruption, combined with improving living conditions, have resulted in a level of popular confidence in the government that makes political instability unlikely.

We study the endogenous relationship between health care, life expectancy and output in a neoclassical growth model. While health care competes resources away from goods production, it prolongs life expectancy which, in turn, leads to higher capital accumulation through a private annuity market. We show that savings and health care are complements in equilibrium, with both rising with economic development. Our model is therefore consistent with several stylized facts, namely: (i) countries spend more on health care as they prosper, (ii) individuals in rich countries tend to live longer, and (iii) population aging is more pronounced in rich countries. Moreover, via the longevity-enhancing channel, health care and health production technology are found by simulation to be growth- and welfare-promoting.

After describing the institutions for health care in China as they evolved since 1949, this paper presents statistical demand functions for health care. It applies the demand functions to explain the rapid increase in health care demand and the resulting rapid increase in price when supply failed to increase. The failure in increase in supply was traced to the system of public supply of health care in China. The reform experience of Suqian city in the privatization of health care is reported to demonstrate the positive effect of privatization on supply. The government's health care program for the urban and rural population is described and an evaluation of it is provided.

Quality reports about health plans and providers are becoming more prevalent in health care markets. This paper casts the decision about what information to report to consumers about health plans as a policy decision. In a market with adverse selection, complete information about quality leads to inefficient outcomes. In a Rothschild-Stiglitz model, we show that averaging quality information into a summary report can enforce pooling in health insurance, and by choice of the right weights in the averaged report, a payer or regulator can induce first-best quality choices. The optimal quality report is as powerful as optimal risk adjustment in correcting adverse selection inefficiencies. ©2005 Elsevier B.V. All rights reserved.

Virtual reality, a new paradigm for relationship between humans and computers, has been recently well-known and currently investigated for practical use in the various industrial fields. Using three-dimensional computer graphics, interactive devices, and high-resolution display, a virtual world can be realized in which one can pick up imaginary objects as if they were physical world. Using this technology, Matsushita Electric Works, Ltd. has been developing several application systems for industrial use since 1990. This paper details three VR application systems operating in the real world: Virtual Space Decision Support System employing Kansei Engineering which is applied for production and sales mainly in the system kitchen business, a telepresence robot system employing semi-autonomous mobile function which is utilized for security field and a low-cost VR system employing physiological feedback mechanism which is used for health care field.

Public policy makers around the world are concerned with the cost and quality of healthcare. I argue that competition-policy economics can make valuable contributions to solving the healthcare crisis by addressing four fundamental questions:

• Why don't insurance companies offer policies that generate greater consumer incentives to seek low-cost providers and generate increased price competition?
• How can consumers be provided with information that will induce stronger quality competition?
• What organizational structures promote the most efficient coordination among complementary care providers and between insurers and care providers?
• How do structure and conduct in markets for healthcare insurance and healthcare services affect the incentives to create and adopt new healthcare goods and services?