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Chronic diseases are considered as one of the serious concerns and threats to public health across the globe. Diseases such as chronic diabetes mellitus (CDM), cardio vasculardisease (CVD) and chronic kidney disease (CKD) are major chronic diseases responsible for millions of death. Each of these diseases is considered as a risk factor for the other two diseases. Therefore, noteworthy attention is being paid to reduce the risk of these diseases. A gigantic amount of medical data is generated in digital form from smart healthcare appliances in the current era. Although numerous machine learning (ML) algorithms are proposed for the early prediction of chronic diseases, these algorithmic models are neither generalized nor adaptive when the model is imposed on new disease datasets. Hence, these algorithms have to process a huge amount of disease data iteratively until the model converges. This limitation may make it difficult for ML models to fit and produce imprecise results. A single algorithm may not yield accurate results. Nonetheless, an ensemble of classifiers built from multiple models, that works based on a voting principle has been successfully applied to solve many classification tasks. The purpose of this paper is to make early prediction of chronic diseases using hybrid generative regression based deep intelligence network (HGRDIN) model.

In the proposed paper generative regression (GR) model is used in combination with deep neural network (DNN) for the early prediction of chronic disease. The GR model will obtain prior knowledge about the labelled data by analyzing the correlation between features and class labels. Hence, the weight assignment process of DNN is influenced by the relationship between attributes rather than random assignment. The knowledge obtained through these processes is passed as input to the DNN network for further prediction. Since the inference about the input data instances is drawn at the DNN through the GR model, the model is named as hybrid generative regression-based deep intelligence network (HGRDIN).

The credibility of the implemented approach is rigorously validated using various parameters such as accuracy, precision, recall, F score and area under the curve (AUC) score. During the training phase, the proposed algorithm is constantly regularized using the elastic net regularization technique and also hyper-tuned using the various parameters such as momentum and learning rate to minimize the misprediction rate. The experimental results illustrate that the proposed approach predicted the chronic disease with a minimal error by avoiding the possible overfitting and local minima problems. The result obtained with the proposed approach is also compared with the various traditional approaches.

Usually, the diagnostic data are multi-dimension in nature where the performance of the ML algorithm will degrade due to the data overfitting, curse of dimensionality issues. The result obtained through the experiment has achieved an average accuracy of 95%. Hence, analysis can be made further to improve predictive accuracy by overcoming the curse of dimensionality issues.

The proposed ML model can mimic the behavior of the doctor's brain. These algorithms have the capability to replace clinical tasks. The accurate result obtained through the innovative algorithms can free the physician from the mundane care and practices so that the physician can focus more on the complex issues.

Utilizing the proposed predictive model at the decision-making level for the early prediction of the disease is considered as a promising change towards the healthcare sector. The global burden of chronic disease can be reduced at an exceptional level through these approaches.

In the proposed HGRDIN model, the concept of transfer learning approach is used where the knowledge acquired through the GR process is applied on DNN that identified the possible relationship between the dependent and independent feature variables by mapping the chronic data instances to its corresponding target class before it is being passed as input to the DNN network. Hence, the result of the experiments illustrated that the proposed approach obtained superior performance in terms of various validation parameters than the existing conventional techniques.

According to the World Health Organization, by 2025, the contribution of chronic disease is expected to rise by 73% compared to all deaths and it is considered as global burden of disease with a rate of 60%. These diseases persist for a longer duration of time, which are almost incurable and can only be controlled. Cardiovascular disease, chronic kidney disease (CKD) and diabetes mellitus are considered as three major chronic diseases that will increase the risk among the adults, as they get older. CKD is considered a major disease among all these chronic diseases, which will increase the risk among the adults as they get older. Overall 10% of the population of the world is affected by CKD and it is likely to double in the year 2030. The paper aims to propose novel feature selection approach in combination with the machine-learning algorithm which can early predict the chronic disease with utmost accuracy. Hence, a novel feature selection adaptive probabilistic divergence-based feature selection (APDFS) algorithm is proposed in combination with the hyper-parameterized logistic regression model (HLRM) for the early prediction of chronic disease.

A novel feature selection APDFS algorithm is proposed which explicitly handles the feature associated with the class label by relevance and redundancy analysis. The algorithm applies the statistical divergence-based information theory to identify the relationship between the distant features of the chronic disease data set. The data set required to experiment is obtained from several medical labs and hospitals in India. The HLRM is used as a machine-learning classifier. The predictive ability of the framework is compared with the various algorithm and also with the various chronic disease data set. The experimental result illustrates that the proposed framework is efficient and achieved competitive results compared to the existing work in most of the cases.

The performance of the proposed framework is validated by using the metric such as recall, precision, F1 measure and ROC. The predictive performance of the proposed framework is analyzed by passing the data set belongs to various chronic disease such as CKD, diabetes and heart disease. The diagnostic ability of the proposed approach is demonstrated by comparing its result with existing algorithms. The experimental figures illustrated that the proposed framework performed exceptionally well in prior prediction of CKD disease with an accuracy of 91.6.

The capability of the machine learning algorithms depends on feature selection (FS) algorithms in identifying the relevant traits from the data set, which impact the predictive result. It is considered as a process of choosing the relevant features from the data set by removing redundant and irrelevant features. Although there are many approaches that have been already proposed toward this objective, they are computationally complex because of the strategy of following a one-step scheme in selecting the features. In this paper, a novel feature selection APDFS algorithm is proposed which explicitly handles the feature associated with the class label by relevance and redundancy analysis. The proposed algorithm handles the process of feature selection in two separate indices. Hence, the computational complexity of the algorithm is reduced to O(n^k+1). The algorithm applies the statistical divergence-based information theory to identify the relationship between the distant features of the chronic disease data set. The data set required to experiment is obtained from several medical labs and hospitals of karkala taluk ,India. The HLRM is used as a machine learning classifier. The predictive ability of the framework is compared with the various algorithm and also with the various chronic disease data set. The experimental result illustrates that the proposed framework is efficient and achieved competitive results are compared to the existing work in most of the cases.

The purpose of this paper is to raise issues about the design of personal health record systems (PHRs) and self-monitoring technology supporting self-care practices of an increasing number of individuals dealing with the management of a chronic disease in everyday life. It discusses the results of an ethnographic study exposing to analysis the intricacies and practicalities of managing diabetes “in the wild”. It then describe and discuss the patient-centric design of a diabetes journaling platform that followed the analysis.

The study includes ethnometodological investigation based on in depth interviews, observations in a support group for adults with type 1 diabetes, home visits, shadowing sessions and semi-structured interviews with a series of medical experts (endocrinologists, general practitioners and diabetes nurses). Findings informed the design of a proof-of-concept PHR called Tag-it-Yourself (TiY): a mobile journaling platform that enables the personalization of self-monitoring practices. The platform is thoroughly described along with an evaluation of its use with real users.

The investigation sheds light on a series of general characters of everyday chronic self-care practices, and how they ask to re-think some of the assumptions and connotations of the current medical model and the traditional sick role of the patient – often unreflectively assumed also in the design of personal technologies (e.g. PHR) to be used by patients in clinically un-controlled settings. In particular, the analysis discusses: the ubiquitous nature of diabetes that is better seen as a lifestyle, the key role of lay expertises and different forms of knowledge developed by the patient in dealing with a disease on a daily basis, and the need of more symmetrical interactions and collaborations with the medical experts.

Reported discussions suggest the need of a more holistic view of self-management of chronic disease in everyday life with more attention being paid on the perspective of the affected individuals. Findings have potential implications on the way PHR and systems to support self-management of chronic disease in everyday life are conceived and designed.

The paper suggests designers and policy makers to look at chronic disease not as a medical condition to be disciplined by a clinical perspective but rather as a complex life-style where the medical cannot be separated by other aspects of everyday life. Such shift in the perspective might suggest new forms of collaborations, new ways of creative evidence and new form of knowledge creation and validation in chronic self-care.

The paper suggests re-thinking the role of the patient in chronic-disease self-management. In particular, it suggests giving more room to the patient voice and concerns and suggest how these can enrich rather than complicate the generation of knowledge about self-care practices, at least in type 1 diabetes.

The paper sheds light on everyday intricacies and practicalities of dealing with a chronic disease. Studies of self-care practices that shed light on the patient perspectives are sporadic and often assume a clinical perspective, its assumptions (e.g. biomedical knowledge is the only one available to improve health outcome, doctors know best) and implications (e.g. compliance, asymmetry between the specialist and the patient).

Chronic disease services may be improved if care management processes (CMPs), such as disease‐specific flowsheets and chronic disease registries, are used. The newly industrialized Gulf state health service has underdeveloped primary care but higher diabetes prevalence. This paper's aim is to investigate care management processes in United Arab Emirates (UAE) primary care clinics to explore these issues.

A cross‐sectional survey using self‐administered questionnaires given to family physicians and nurses attending a UAE University workshop was used to collect data.

All 38 participants completed the questionnaire: 68 per cent were women and 81 per cent physicians. Care management processes in use included: medical records, 76 per cent; clinical guidelines, 74 per cent; chronic disease care rooms, 74 per cent; disease‐specific flowsheets, 61 per cent; medical record audits, 57 per cent; chronic disease nurse‐educators, 58 per cent; electronic medical records (EMR), 34 per cent; and incentive plans based on clinical performance, 21 per cent. Only 62 per cent and 48 per cent reported that flowsheets and problem lists, respectively, were completed by physicians. Responses to the open‐ended question included using traditional quality improvement (QI) approaches such as continuing education and staff meetings, but not proactive systems such as disease registries and self‐management.

The study used a small, non‐random sample and the survey instrument's psychometric properties were not collected.

Chronic disease care CMPs are present in UAE clinics but use is limited. Quality improvement should include disease registries, reminder‐tracking systems, patient self‐management support and quality incentives.

This report highlights the lag regarding adopting more effective CMPs in developing countries.

This paper aims to examine peer‐to‐peer online communities for people with chronic diseases in order to present a conceptual framework that identifies the needs of members. This framework aims to improve understanding of the role of these communities in the enhancement of people's self‐management of chronic disease care.

A conceptual framework is drawn from the literature and tested against three illustrative case studies using an ethnographic approach. Taking an objective perspective, the data were examined against the proposed framework. The iterative cycle of qualitative analysis supported reflection through the ongoing observation of the case communities over several months.

The research underpins identification of members' needs as presented in the framework. It finds that the constructs of the shared space provide a context for the identified needs of members which are revised to reflect the findings. Social needs are broadened to include the powerful influences of communication through self‐expression, spiritual support and advocacy. Hedonic needs are found to play a significant role in continued participation.

Improved management of chronic disease care benefits both the patient and a range of stakeholders concerned with delivery of care services. Greater recognition of the identified needs of online community members supports the capability to improve the effectiveness of healthcare delivery.

This research provides a framework for enhancing the ability of online communities to empower patients. It identifies specific needs of members and presents a conceptual framework to facilitate continuing research in this significant area.

The purpose of this paper is to analyze the overall morbidity prevalence and their differentials among emigrants’ and non-emigrants’ wives in Kerala, India.

The study is based on the third round of The Kerala Migration Survey-2007 data. The third round of KMS was perhaps the first survey which has collected data on Indian emigration and morbidity scenario during 2007 at the household and individual level. Descriptive statistics, binary logistic regression models, and Oaxaca decomposition models were used to examine the disease differentials among emigrants’ and non-emigrants’ wives.

The paper shows that household size is negatively associated with chronic disease and incidence of morbidity is much lower among emigrants’ wives. The result also shows that among women, those who stay with a husband or whose husbands are elsewhere in India show a higher incidence of morbidity than those whose husbands are abroad, owing to the limited scope of activity as well as freedom in lifestyle and for taking independent decisions. Thus, it can be concluded that for women, the scope of activity and the freedom to live are important factors contributing to the level of morbidity. Blinder-Oaxaca decomposition results show that non-poor households and non-Muslim religion are in a disadvantageous position in terms of chronic morbidity.

This paper is based on the cross-sectional nature of data; this is an obvious limitation on the effect of emigration on morbidity differentials among emigrants’ and non-emigrants’ wives.

There are few or rare studies conducted so far to investigate the effect of migration on the health of the spouses or families left behind.

The purpose of this paper is to identify health and physical activity status and prevalence of chronic diseases risk factors in a sample of Government office employees.

Quantitative assessment of various health and physical activity measures including blood pressure, BMI, waist‐to‐hip ratio, cholesterol, blood glucose and physical activity in adult male (n=66; age=42 ±9 years) and female (n=262; age=40 ±10 years) Government office employees located in metropolitan, rural and remote areas of central and northern Queensland.

It was found that 54 per cent of females and 77 per cent of males were classified as overweight or obese, while 38 per cent of all participants did not participate in sufficient weekly physical activity. Metropolitan females reported significantly higher blood glucose levels and diastolic blood pressure but significantly lower waist‐to‐hip ratio than rural and remote females.

There was uneven sampling across geographical locations, however the sample size of each group was largely indicative of the workforce in each location.

There is a large prevalence of chronic disease risk factors in male and female Government office employees working throughout metropolitan, rural and remote areas of central and northern Queensland. Workplace personnel should work to improve the health and physical activity status of employees, as this may have positive effects on workplace participation and productivity.

This study provides insight into the prevalence of chronic disease risk factors in Government office workers undertaking similar work duties across a variety of geographical locations, and provides suggestions for workplace interventions.

The purpose of this paper is to estimate the possible impact of the COVID-19 pandemic on the hospitalization and hospital mortality of the patients with gallstone disease and chronic liver diseases (CLD) in the worst pandemic period in Ukraine.

A retrospective comparative analysis of annual reports data of all economy subjects, which conducted economic activity in medical practice for 2019 and 2021. Data was accepted from the Ministry of Health of Ukraine, the National Security and Defense Council of Ukraine (NSDC) and the State Statistics Service of Ukraine (SSSU).

The total hospitalization rates for diffuse liver disease and cholelithiasis significantly decreased during the peak of the COVID-19 pandemic in Ukraine, compared to the values of 2019. At the same time, the rates of in-hospital mortality for these diseases have significantly grown. Also, various proportions of similar trends were described in other countries during the first wave of the pandemic.

This paper highlights the fact that regulatory restrictions and the fear of the population of referring to healthcare facilities considering the high risk of getting an infection had significant disruption to medical care for patients with gallstone disease and CLD. Improving the management of medical resources and strengthening all kinds of institutions in the healthcare system must be thought about if similar challenges appear in the future.

The COVID-19 pandemic has demonstrated the urgency of better chronic disease management and the importance of making it an integral part of the recovery agenda in Europe. This paper aims to explore the shift towards digital and integrated care systems in Europe.

In this viewpoint paper the Expert Group for Integrated Care and Digital Health Europe (EGIDE) group argues that an orchestrated shift towards integrated care holds the solution to the chronic disease pandemic.

The development of integrated care cannot happen without shifting towards a digitalised healthcare system via large-scale initiatives like the European Health Data Space (EHDS) and the involvement of all stakeholders.

The EGIDE group has identified some foundational principles, which can guide the way to realise the full potential of the EHDS for integrated care and can support the involved stakeholders’ thinking.

Despite having inadequate resources, highly impoverished patients tend to seek and share health information over social media groups to improve each other’s well-being. This study aims to focus on access to health-care information for such patients and aims to provide an understanding of how online health-care communities (OHCs), as transformative service mediators, can be platforms for patients with chronic and nonchronic health conditions to share their experiences in a base-of-the-pyramid (BOP) context.

A large-scale survey among 658 respondents was conducted in a very low-income country. Structural equation modeling was used to test the hypotheses.

A model of patients’ experience sharing (PES), motivations and consequences for health-care services are introduced and tested. The result supports the PES model for patients with chronic health conditions, showing that utilitarian, hedonic and social value dimensions directly influence PES and indirectly influence patients’ continuance intention with OHCs and patient efforts. However, a mediating effect of PES was found only between the value dimensions and patients’ efforts. A negative moderation effect of medical mistrust was found in the relationship between utilitarian value and PES for both chronic and nonchronic patient groups.

This study is a pioneering attempt to develop and test a PES model in a BOP market.