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Article
Publication date: 11 November 2021

Sandeep Kumar Hegde and Monica R. Mundada

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…

Abstract

Purpose

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.

Design/methodology/approach

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).

Findings

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.

Research limitations/implications

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.

Practical implications

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.

Social implications

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.

Originality/value

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.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 15 no. 1
Type: Research Article
ISSN: 1756-378X

Keywords

Book part
Publication date: 27 November 2023

Cristina Valadas and Ana Matilde Cabral

We are currently experiencing, in western societies, a new reality in health systems, the emergence of an epidemic of chronic diseases, which test and raise new challenges to the…

Abstract

We are currently experiencing, in western societies, a new reality in health systems, the emergence of an epidemic of chronic diseases, which test and raise new challenges to the health systems. This exponential increase in chronic diseases has not been accompanied by updated training of health professionals in this area. The chronic illness implies a bilateral relationship, of commitment and compromise for life, in which the involvement of the sick person must be the rule. The scope of therapeutic education (TE) is making the person autonomous and helping them to maintain or improve their quality of life. To treat patients with chronic disease, health professionals need to adapt their knowledge to their new role in the therapeutic relationship. As for the methodology, a participative observational methodology will be carried out with the training of health professionals who work in this area. It is a descriptive work based on studies and works published by the main schools working in this area, with emphasis on the School of Geneve. The purpose is to identify the problem of chronic diseases, the challenges that patients and health professionals face and how to build educational projects, exploring the use of educational tools, including digital technology.

Details

Technology-Enhanced Healthcare Education: Transformative Learning for Patient-centric Health
Type: Book
ISBN: 978-1-83753-599-6

Keywords

Article
Publication date: 12 June 2020

Sandeepkumar Hegde and Monica R. Mundada

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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(nk+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.

Details

International Journal of Pervasive Computing and Communications, vol. 17 no. 1
Type: Research Article
ISSN: 1742-7371

Keywords

Article
Publication date: 25 March 2024

Yi Wu, Tianxue Long, Jing Huang, Yiyun Zhang, Qi Zhang, Jiaxin Zhang and Mingzi Li

This study aims to synthesize the existing serious games designed to promote mental health in adolescents with chronic illnesses.

Abstract

Purpose

This study aims to synthesize the existing serious games designed to promote mental health in adolescents with chronic illnesses.

Design/methodology/approach

This study conducted a review following the guidelines of Joanna Briggs Institute and Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews. Searches were conducted in databases PubMed, Scopus, Web of Science, Cochrane Library, cumulative index to nursing and allied health literature, PsycINFO, China national knowledge infrastructure Wanfang, VIP Database for Chinese Technical Periodicals and SinoMed from inception to February 12, 2023.

Findings

A total of 14 studies (describing 14 serious games) for improving the mental health of adolescents with chronic diseases were included. Of all the included games, 12 were not described as adopting any theoretical framework or model. The main diseases applicable to serious games are cancer, type 1 diabetes and autism spectrum disorder. For interventional studies, more than half of the study types were feasibility or pilot trials. Furthermore, the dosage of serious games also differs in each experiment. For the game elements, most game elements were in the category “reward and punishment features” (n = 50) and last was “social features” (n = 4).

Originality/value

Adolescence is a critical period in a person’s physical and mental development throughout life. Diagnosed with chronic diseases during this period will cause great trauma to the adolescents and their families. Serious game interventions have been developed and applied to promote the psychological health field of healthy adolescents. To the best of the authors’ knowledge, this study is the first to scope review the serious game of promoting mental health in the population of adolescents with chronically ill. At the same time, the current study also extracted and qualitatively analyzed the elements of the serious game.

Details

Mental Health Review Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1361-9322

Keywords

Article
Publication date: 28 October 2014

Cristiano Storni

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…

1233

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Practical implications

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.

Social implications

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.

Originality/value

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).

Book part
Publication date: 28 March 2022

Nancy G. Kutner

In the context of US kidney disease care in 2020, this chapter highlights challenges of managing COVID-19–related acute pathology, sustaining safe chronic dialysis treatment for…

Abstract

Purpose

In the context of US kidney disease care in 2020, this chapter highlights challenges of managing COVID-19–related acute pathology, sustaining safe chronic dialysis treatment for individuals with kidney failure during a pandemic, and identifying ways to effectively address intersections of race/ethnicity, SES, and health.

Methodology/Approach

Medical literature and American Society of Nephrology (ASN) online member forum review, and Emory School of Medicine Renal Grand Rounds participant observation: April 2020–March 2021.

Findings

Among persons infected with COVID-19, especially persons of African descent, acute kidney injury (AKI) risk was elevated and associated with need for long-term dialysis. Dialysis-dependent chronic kidney disease patients constituted a high-risk group for COVID-19 infection and hospitalization, due to underlying chronic conditions as well as required travel to clinics for multiple weekly dialysis treatments with exposure to possibly infected staff and other patients.

Research Limitations/Implications

Findings that are discussed are based on a limited time frame. The longer-term impact of COVID-19 for patient outcomes and for the structure of kidney disease care is a fertile area for continued study, especially in relation to broad health equity goals.

Originality/Value of Paper

Racial justice activism in 2020 highlighted the imperative to address socioeconomic and racially structured inequities in the United States, and health equity goals and strategies that target kidney disease care have been outlined. The acute/chronic continuum of kidney disease care is a fertile area for research that is informed by the COVID-19 experience and population health inequity challenges.

Details

Health and Health Care Inequities, Infectious Diseases and Social Factors
Type: Book
ISBN: 978-1-80117-940-9

Keywords

Article
Publication date: 4 January 2013

Muhammad Jawad Hashim, Adrianna Prinsloo and Deen M. Mirza

Chronic disease services may be improved if care management processes (CMPs), such as disease‐specific flowsheets and chronic disease registries, are used. The newly…

540

Abstract

Purpose

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.

Design/methodology/approach

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

Findings

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.

Research limitations/implications

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

Practical implications

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.

Originality/value

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

Details

International Journal of Health Care Quality Assurance, vol. 26 no. 1
Type: Research Article
ISSN: 0952-6862

Keywords

Article
Publication date: 23 May 2008

Rosemary Stockdale

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Practical implications

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.

Originality/value

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.

Details

Journal of Systems and Information Technology, vol. 10 no. 1
Type: Research Article
ISSN: 1328-7265

Keywords

Article
Publication date: 4 September 2017

Imtiyaz Ali, Ram B. Bhagat, Geetika Shankar and Raj Kumar Verma

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

Abstract

Purpose

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

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Originality/value

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.

Details

International Journal of Migration, Health and Social Care, vol. 13 no. 3
Type: Research Article
ISSN: 1747-9894

Keywords

Article
Publication date: 30 March 2010

Rebecca M. Sealey, Wade H. Sinclair, Paige Pollock and Anne‐Marie Wright

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.

395

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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

Practical implications

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.

Originality/value

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.

Details

International Journal of Workplace Health Management, vol. 3 no. 1
Type: Research Article
ISSN: 1753-8351

Keywords

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