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Breast cancer is an important medical disorder, which is not a single disease but a cluster more than 200 different serious medical complications.

The new artificial bee colony (ABC) implementation has been applied to probabilistic neural network (PNN) for training and testing purpose to classify the breast cancer data set.

The new ABC algorithm along with PNN has been successfully applied to breast cancers data set for prediction purpose with minimum iteration consuming.

The new implementation of ABC along PNN can be easily applied to times series problems for accurate prediction or classification.

The study explores how Taiwan’s electronic health data systems can be used to build algorithms that reduce or eliminate medical errors and to advance precision medicine.

This study is a narrative review of the literature.

The body of medical knowledge has grown far too large for human clinicians to parse. In theory, electronic health records could augment clinical decision-making with electronic clinical decision support systems (CDSSs). However, computer scientists and clinicians have made remarkably little progress in building CDSSs, because health data tend to be siloed across many different systems that are not interoperable and cannot be linked using common identifiers. As a result, medicine in the USA is often practiced inconsistently with poor adherence to the best preventive and clinical practices. Poor information technology infrastructure contributes to medical errors and waste, resulting in suboptimal care and tens of thousands of premature deaths every year. Taiwan’s national health system, in contrast, is underpinned by a coordinated system of electronic data systems but remains underutilized. In this paper, the authors present a theoretical path toward developing artificial intelligence (AI)-driven CDSS systems using Taiwan’s National Health Insurance Research Database. Such a system could in theory not only optimize care and prevent clinical errors but also empower patients to track their progress in achieving their personal health goals.

While research teams have previously built AI systems with limited applications, this study provides a framework for building global AI-based CDSS systems using one of the world’s few unified electronic health data systems.

Background: Quality of life is a core concern for cancer patients, which can be negatively affected by illness-related death anxiety; yet understanding of how to appropriately target psycho-oncological interventions remains lacking. We aimed to explore experiential acceptance in cancer patients, and whether acceptance – as an alternative to avoidant coping – was related to and predictive of better quality of life and death anxiety outcomes.

Methods: We used a longitudinal, quantitative design with a follow-up after three months. Seventy-two participants completed a questionnaire-battery measuring illness appraisals, acceptance and non-acceptance coping-styles, quality of life, and death anxiety; 31 participants repeated the battery after three months.

Results: Acceptance was an independent explanatory and predictive variable for quality of life and death anxiety, in the direction of psychological health. Acceptance had greater explanatory power for outcomes than either cancer appraisals or avoidant response styles. Avoidant response styles were associated with greater death anxiety and poorer quality of life.

Conclusions: The findings support the role of an accepting response-style in favourable psychological outcomes, identifying a possible target for future psychological intervention. Response styles that might be encouraged in other therapies, such as active coping, planning, and positive reframing, were not associated with beneficial outcomes.

The purpose of this paper is to assess the levels of awareness and knowledge about oral cancer, its causes and or risk factors among Ugandan patients seeking oral healthcare.

This was a cross-sectional study on adult patients who attended a free dental camp. An assistant-administered questionnaire either in English or Luganda was provided to every even-numbered registered adult who consented to participate in the study. Information on demographics and known risk factors for oral cancer were captured. The two knowledge questions on oral cancers were scored by adding up all the correctly identified causes, non-causes and risk factors then scored out of the total. Data analysis was done by calculating proportions, Student’s’ t-tests and χ² tests with significant p-value set at 0.05.

The results showed a low level of awareness/knowledge about oral cancer in studied population. In total, 60 percent and less than 50 percent of respondents identified smoking and alcohol use as risk factors for oral cancer, respectively. Majority of respondents (88.8 percent) would seek help from medical personnel if diagnosed with oral cancer. Screening for cancer was low despite awareness and knowledge that it improves the chances of successful treatment.

Emphasis on risk factors including alcohol use in public health messages, use of mass media, religious and community leaders to disseminate messages to the communities and further research were recommended.

We need to emphasize the role of alcohol in oral cancer causation just as we do for tobacco consumption.

No study has been conducted in Uganda on the level of awareness yet the incidence of the disease and use of high-risk products are rising.

The purpose of this study is to identify best practices for innovative primary care models and to describe a potential future primary care (PC) model for Texas to address the burden of chronic disease in a population-based approach.

A systematic literature review was conducted and identified 1,880 published records through PubMed using 26 search terms. After abstract and full-text review, 70 articles remained as potential models.

Although there is already a severe shortage of physicians in Texas, emerging practice patterns and choices among physicians are likely to erode access to primary care services in the state. Health-care leaders are encouraged to consider models such as complex adaptive systems for team-based care, pharmacist hypertension care management program and combined nurse-led care management with group visit structure.

As with any study, this research has its limitations; for example, models that might work in one state, or under a unique state-funded academic medical center, might not be “do-able” in another state within the nuances of a different funding mechanism.

Results of this research provide a model for implementing IPCM for the state of Texas first and will guide IPCM planning and implementation in other states.

This study is “land grant-centric” and focused on carrying out the mission of a major, top-tier research university with an emerging college of medicine at an academic medical center.

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.

An empirical study investigated the antecedents to perceived usefulness (PU) and its consequences in the context of smoking cessation online health communities (OHCs).

To validate a research model for perceived informational support, perceived emotional support and perceived esteem support, the authors conducted a partial-least-squares analysis of empirical data from an online survey (N = 173) of users of two smoking cessation OHCs. The proposed model articulates these as antecedents to PU from a social support perspective, and knowledge sharing and continuance intention are expressed as consequences of PU.

The empirical study identified that the PU of smoking cessation OHCs is influenced by perceived emotional support and perceived esteem support, and perceived informational support indirectly affects PU via these factors. In turn, PU exerts a positive influence on both knowledge sharing and continuance intention. Also, knowledge sharing positively affects continuance intention.

The study contributes to scholarship on users' postadoption behavior in the context of smoking cessation OHCs by disentangling the antecedents to PU from a social support perspective and pinpointing some important consequences of PU. The research also has practical implications for managing smoking cessation OHCs.

The study aims to summarise the literature on cancer care pathways at the diagnostic and treatment phases. The objectives are to find factors influencing the delivery of cancer care pathways; to highlight any interrelating factors; to find gaps in the literature concerning areas of research; to summarise the strategies and recommendations implemented in the studies.

The study used a qualitative approach and developed a causal loop diagram to summarise the current literature on cancer care pathways, from screening and diagnosis to treatment. A total of 46 papers was finally included in the analysis, which highlights the recurring themes in the literature.

The study highlights the myriad areas of research applied to cancer care pathways. Factors influencing the delivery of cancer care pathways were classified into different albeit interrelated themes. These include access barriers to care, hospital emergency admissions, fast track diagnostics, delay in diagnosis, waiting time to treatment and strategies to increase system efficiency.

As far as the authors know, this is the first study to present a visual representation of the complex relationship between factors influencing the delivery of cancer care pathways.

The purpose of this study describes how individuals; healthcare providers and the government can reduce the burden of noncommunicable diseases (NCDs) in Nigeria.

This commentary paper combines extensive literature searches and experience from public health physicians.

Noncommunicable diseases are chronic diseases. They result from a combination of genetic, physiological, environmental and behavioral factors. NCDs are divided into four classes: cardiovascular diseases, cancers, chronic respiratory diseases and diabetes.

Noncommunicable diseases are responsible for about a quarter of total deaths in Nigeria. These deaths are unnecessary as most NCDs can be prevented if the risk factors are dealt with. Dealing with these risk factors involves everyone (Individuals, government, interdisciplinary and multidisciplinary researchers, policymakers, government, etc.).

Major risk factors for NCDs are tobacco use, harmful use of alcohol, unhealthy diet and physical inactivity. The solution to NCDs, therefore, lies in dealing with these factors, which, fortunately, are modifiable since they have to do with lifestyle practices. There is evidence to support the claim that a reduction of the risks of NCDs can be achieved by engaging in healthy lifestyle practices.

For the prevention and cure of disease, patient use various types of chemical and drug agents. Along with their curative effect, almost all drugs have some destructive effects and side-effects. Due to the minimal and/or none of unwanted side-effect, recently, the use of herbal remedy as the drug of choice becomes the preference choice. The mangosteen, Garcinia mangostana, contains various types of polyphenols. It has been used as a traditional medicine from the ancient times till present days. The purpose of this paper is to investigate the biological properties of mangosteen in relation to health promotion effects.

Several research papers from well-known database (such as PubMed, Google scholar, Scopus and Sciencedirect) were reviewed without considering publication-times to understand the biological properties of mangosteen.

Mangosteen and its xanthone exerted diverse biological activities such as anti-oxidant, anti-inflammatory, anti-allergy, anti-bacteria, anti-fungal, anti-malaria, anticancer and anti-diabetes.

Based on these studies, mangosteen is beneficial dietary supplement of overall human health.