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Smartphones and personal sensing technologies have made collecting data continuously and in real time feasible. The promise of pervasive sensing technologies in the realm of mental health has recently garnered increased attention. Using Artificial Intelligence methods, it is possible to forecast a person's emotional state based on contextual information such as their current location, movement patterns, and so on. As a result, conditions like anxiety, stress, depression, and others might be tracked automatically and in real‐time. The objective of this research was to survey the state‐of‐the‐art autonomous psychological health monitoring (APHM) approaches, including those that make use of sensor data, virtual chatbot communication, and artificial intelligence methods like Machine learning and deep learning algorithms. We discussed the main processing phases of APHM from the sensing layer to the application layer and an observation taxonomy deals with various observation devices, observation duration, and phenomena related to APHM. Our goal in this study includes research works pertaining to working of APHM to predict the various mental disorders and difficulties encountered by researchers working in this sector and potential application for future clinical use highlighted. This article is categorized under:Technologies > Machine LearningTechnologies > PredictionApplication Areas > Health Care [ABSTRACT FROM AUTHOR]

Gastrointestinal (GI) diseases are prevalent medical conditions that require accurate and timely diagnosis for effective treatment. To address this, we developed the Multi-Fusion Convolutional Neural Network (MF-CNN), a deep learning framework that strategically integrates and adapts elements from six deep learning models, enhancing feature extraction and classification of GI diseases from endoscopic images. The MF-CNN architecture leverages truncated and partially frozen layers from existing models, augmented with novel components such as Auxiliary Fusing Layers (AuxFL), Fusion Residual Block (FuRB), and Alpha Dropouts (αDO) to improve precision and robustness. This design facilitates the precise identification of conditions such as ulcerative colitis, polyps, esophagitis, and healthy colons. Our methodology involved preprocessing endoscopic images sourced from open databases, including KVASIR and ETIS-Larib Polyp DB, using adaptive histogram equalization (AHE) to enhance their quality. The MF-CNN framework supports detailed feature mapping for improved interpretability of the model's internal workings. An ablation study was conducted to validate the contribution of each component, demonstrating that the integration of AuxFL, αDO, and FuRB played a crucial part in reducing overfitting and efficiency saturation and enhancing overall model performance. The MF-CNN demonstrated outstanding performance in terms of efficacy, achieving an accuracy rate of 99.25%. It also excelled in other key performance metrics with a precision of 99.27%, a recall of 99.25%, and an F1-score of 99.25%. These metrics confirmed the model's proficiency in accurate classification and its capability to minimize false positives and negatives across all tested GI disease categories. Furthermore, the AUC values were exceptional, averaging 1.00 for both test and validation sets, indicating perfect discriminative ability. The findings of the P-R curve analysis and confusion matrix further confirmed the robust classification performance of the MF-CNN. This research introduces a technique for medical imaging that can potentially transform diagnostics in gastrointestinal healthcare facilities worldwide. [ABSTRACT FROM AUTHOR]

From the planting base to the consumer's table, agricultural products must go through multiple links such as planting, processing, transportation, warehousing, and sales. The quality and safety of agricultural products have received extensive attention from all walks of life. Based on the block chain technology, this paper will build a traceability system for the quality and safety of agricultural products, refine the research objects, and design solutions from the aspects of overall structure, role authority, operating process, and functional modules according to the characteristics of planted agricultural products, so as to realize the whole process of agricultural product supply chain tracking, traceability to ensure the quality and safety of agricultural products. [ABSTRACT FROM AUTHOR]

Background: Treatment nonadherence in cancer patients remains high with most interventions having had limited success. Most studies omit the multi-factorial aspects of treatment adherence and refer to medication adherence. The behaviour is rarely defined as intentional or unintentional. Aim: The aim of this Scoping Review is to increase understanding of modifiable factors in treatment nonadherence through the relationships that physicians have with their patients. This knowledge can help define when treatment nonadherence is intentional or unintentional and can assist in predicting cancer patients at risk of nonadherence and in intervention design. The scoping review provides the basis for method triangulation in two subsequent qualitative studies: 1. Sentiment analysis of online cancer support groups in relation to treatment nonadherence; 2. A qualitative validation survey to refute / or validate claims from this scoping review. Thereafter, framework development for a future (cancer patient) online peer support intervention. Methods: A Scoping Review was performed to identify peer reviewed studies that concern treatment / medication nonadherence in cancer patients—published between 2000 to 2021 (and partial 2022). The review was registered in the Prospero database CRD42020210340 and follows the PRISMA-S: an extension to the PRISMA Statement for Reporting Literature Searches in Systematic Searches. The principles of meta-ethnography are used in a synthesis of qualitative findings that preserve the context of primary data. An aim of meta-ethnography is to identify common and refuted themes across studies. This is not a mixed methods study, but due to a limited qualitativevidence base and to broaden findings, the qualitative elements (author interpretations) found within relevant quantitative studies have been included. Results: Of 7510 articles identified, 240 full texts were reviewed with 35 included. These comprise 15 qualitative and 20 quantitative studies. One major theme, that embraces 6 sub themes has emerged: 'Physician factors can influence patient factors in treatment nonadherence'. The six (6) subthemes are: 1. Suboptimal Communication; 2. The concept of Information differs between Patient and Physician; 3.Inadequate time. 4. The need for Treatment Concordance is vague or missing from concepts; 5. The importance of Trust in the physician / patient relationship is understated in papers; 6. Treatment concordance as a concept is rarely defined and largely missing from studies. Line of argument was drawn: Treatment (or medication) nonadherence that is intentional or unintentional is often attributed to patient factors—with far less attention to the potential influence of physician communication factors. The differentation between intentional or unintentional nonadherence is missing from most qualitative and quantitative studies. The holistic inter-dimensional / multi-factorial concept of 'treatment adherence' receives scant attention. The main focus is on medication adherence / nonadherence in the singular context. Nonadherence that is unintentional is not necessarily passive behaviour and may overlap with intentional nonadherence. The absence of treatment concordance is a barrier to treatment adherence and is rarely articulated or defined in studies. Conclusion: This review demonstrates how cancer patient treatment nonadherence is often a shared outcome. An equal focus on physican and patient factors can increase understanding of the two main types of nonadherence (intentional or unintentional). This differentation should help improve the fundamentals of intervention design. [ABSTRACT FROM AUTHOR]

Informatics paradigms for brain and mental health research have seen significant advances in recent years. These developments can largely be attributed to the emergence of new technologies such as machine learning, deep learning, and artificial intelligence. Data-driven methods have the potential to support mental health care by providing more precise and personalised approaches to detection, diagnosis, and treatment of depression. In particular, precision psychiatry is an emerging field that utilises advanced computational techniques to achieve a more individualised approach to mental health care. This survey provides an overview of the ways in which artificial intelligence is currently being used to support precision psychiatry. Advanced algorithms are being used to support all phases of the treatment cycle. These systems have the potential to identify individuals suffering from mental health conditions, allowing them to receive the care they need and tailor treatments to individual patients who are mostly to benefit. Additionally, unsupervised learning techniques are breaking down existing discrete diagnostic categories and highlighting the vast disease heterogeneity observed within depression diagnoses. Artificial intelligence also provides the opportunity to shift towards evidence-based treatment prescription, moving away from existing methods based on group averages. However, our analysis suggests there are several limitations currently inhibiting the progress of data-driven paradigms in care. Significantly, none of the surveyed articles demonstrate empirically improved patient outcomes over existing methods. Furthermore, greater consideration needs to be given to uncertainty quantification, model validation, constructing interdisciplinary teams of researchers, improved access to diverse data and standardised definitions within the field. Empirical validation of computer algorithms via randomised control trials which demonstrate measurable improvement to patient outcomes are the next step in progressing models to clinical implementation. [ABSTRACT FROM AUTHOR]

The adoption of artificial intelligence (AI) in healthcare is growing rapidly. Remote patient monitoring (RPM) is one of the common healthcare applications that assist doctors to monitor patients with chronic or acute illness at remote locations, elderly people in‐home care, and even hospitalized patients. The reliability of manual patient monitoring systems depends on staff time management which is dependent on their workload. Conventional patient monitoring involves invasive approaches which require skin contact to monitor health status. This study aims to do a comprehensive review of RPM systems including adopted advanced technologies, AI impact on RPM, challenges and trends in AI‐enabled RPM. This review explores the benefits and challenges of patient‐centric RPM architectures enabled with Internet of Things wearable devices and sensors using the cloud, fog, edge, and blockchain technologies. The role of AI in RPM ranges from physical activity classification to chronic disease monitoring and vital signs monitoring in emergency settings. This review results show that AI‐enabled RPM architectures have transformed healthcare monitoring applications because of their ability to detect early deterioration in patients' health, personalize individual patient health parameter monitoring using federated learning, and learn human behavior patterns using techniques such as reinforcement learning. This review discusses the challenges and trends to adopt AI to RPM systems and implementation issues. The future directions of AI in RPM applications are analyzed based on the challenges and trends. This article is categorized under:Application Areas > Health CareTechnologies > Artificial IntelligenceTechnologies > Internet of Things [ABSTRACT FROM AUTHOR]

Being pregnant and giving birth are big life stages that occur for women. The physical and mental effects of pregnancy and childbirth, like those of many other fleeting life experiences, have the significant potential to influence a mother's overall health and well-being. They have also been known to trigger Postpartum Depression (PPD) in many cases. PPD can be exhausting for the mother and it may have a negative impact on her capacity to care for herself and her kid if it is not treated. For this reason, in this study, initially, physiological questionnaire Edinburgh Postnatal Depression Scale (EPDS) data were collected from delivered mothers for one week, the score was evaluated by medical experts, and participants with PDD symptoms were identified. As a part of multistage progress, further, follow-up was carried out by collecting the Patient Health Questionnaire-9 (PHQ-9), Postpartum Depression Screening Scale (PDSS) questionnaires for the above-predicted participants until six weeks. As the second step, correlated risk factors with PPD symptoms were identified using statistical analysis. Finally, data were analyzed and used to train and test machine learning algorithms in order to predict postpartum depression from one to six weeks. The extremely Randomized Trees (XRT) algorithm with (Background Information + PHQ-9 + PDSS) data offers the most accurate and efficient prediction. Pregnant women with these features could be identified and treated properly. Moreover, it reduces prolonged complications and remains cost-effective in future clinical models. [ABSTRACT FROM AUTHOR]

Objective: This paper applies graph methods to distinguish major depression disorder (MDD) and healthy (H) subjects using the graph features of single-channel electroencephalogram (EEG) signals. Methods: Four network features—graph entropy, mean degree, degree two, and degree three—were extracted from the 19-channel EEG signals of 64 subjects (26 females and 38 males), and then these features were forwarded to a support vector machine to conduct depression classification based on the eyes-open and eyes-closed statuses, respectively. Results: Statistical analysis showed that graph features with degree of two and three, the graph entropy of MDD was significantly lower than that for H (p < 0.0001). Additionally, the accuracy of detecting MDD using single-channel T4 EEG with leave-one-out cross-validation from H was 89.2% and 92.0% for the eyes-open and eyes-closed statuses, respectively. Conclusion: This study shows that the graph features of a short-term EEG can help assess and evaluate MDD. Thus, single-channel EEG signals can be used to detect depression in subjects. Significance: Graph feature analysis discovered that MDD is more related to the temporal lobe than the frontal lobe. [ABSTRACT FROM AUTHOR]

Background: The purpose of this paper is to explore the underlying challenges in the telehealth environment in India and Pakistan that impede the delivery of high-quality images between a patient and health care professional. Method: An exploratory study was conducted among healthcare professionals in India and Pakistan to assess their perceptions regarding image quality, which is used for the diagnosis and treatment decision making. This cross-sectional qualitative study used semi-structured interviews with healthcare professionals in both India and Pakistan. Results: The interviews were analyzed using a thematic analysis, which revealed three major themes. These themes being: ICT infrastructure and connectivity, expertise of persons taking images, and multiple transmission degrading image quality. Conclusions: Findings indicate that in both countries, the main underlying challenge is the lack of consistency in the network infrastructure between urban, rural and remote areas. Additionally, training patients to use hand-held devices to take high-quality images could hold the key to improving the reliability and, consequently, the quality of images transmitted between patients and health care professionals. [ABSTRACT FROM AUTHOR]

As the networking infrastructure is becoming complex, there is a parallel increase in the networking scenario to become more vulnerable to various types of sophisticated malicious attacks. This leads to the data and information loss. The most feasible way to reduce the vulnerable and malicious attacks is by evaluating trust of the interacting agents in a network. The primary objective of this paper is to analyze the flooding attacks caused due to the communication protocols like TCP and UDP in software defined spine leaf topology. The trust analysis is done using Bayesian networks for a reliable communication network. The probability and the causal effect of the denial of service in the proposed distributed networking scenario are reasoned out. The trust analysis will help to increase the confidence over a dedicated communication at the time of malicious attack over the distributed network. This analysis will enable the software defined control plane to make a crucial and needed decision over its communication with other nodes during compromised security attack through communication protocols. [ABSTRACT FROM AUTHOR]

In 2020 the world is facing unprecedented challenges due to COVID‐19. To address these challenges, many digital tools are being explored and developed to contain the spread of the disease. With the lack of availability of vaccines, there is an urgent need to avert resurgence of infections by putting some measures, such as contact tracing, in place. While digital tools, such as phone applications are advantageous, they also pose challenges and have limitations (eg, wireless coverage could be an issue in some cases). On the other hand, wearable devices, when coupled with the Internet of Things (IoT), are expected to influence lifestyle and healthcare directly, and they may be useful for health monitoring during the global pandemic and beyond. In this work, we conduct a literature review of contact tracing methods and applications. Based on the literature review, we found limitations in gathering health data, such as insufficient network coverage. To address these shortcomings, we propose a novel intelligent tool that will be useful for contact tracing and prediction of COVID‐19 clusters. The solution comprises a phone application combined with a wearable device, infused with unique intelligent IoT features (complex data analysis and intelligent data visualization) embedded within the system to aid in COVID‐19 analysis. Contact tracing applications must establish data collection and data interpretation. Intelligent data interpretation can assist epidemiological scientists in anticipating clusters, and can enable them to take necessary action in improving public health management. Our proposed tool could also be used to curb disease incidence in future global health crises. [ABSTRACT FROM AUTHOR]

Text classification (a.k.a text categorisation) is an effective and efficient technology for information organisation and management. With the explosion of information resources on the Web and corporate intranets continues to increase, it has being become more and more important and has attracted wide attention from many different research fields. In the literature, many feature selection methods and classification algorithms have been proposed. It also has important applications in the real world. However, the dramatic increase in the availability of massive text data from various sources is creating a number of issues and challenges for text classification such as scalability issues. The purpose of this report is to give an overview of existing text classification technologies for building more reliable text classification applications, to propose a research direction for addressing the challenging problems in text mining. [ABSTRACT FROM AUTHOR]

As China's agricultural output has improved, the national and local monitoring system of agricultural product safety has become much better, and monitoring standards have become increasingly strict. Despite this, there are agricultural product safety incidents which have caused consumer panic. One way to address this is by properly establishing tracking systems so that agricultural product logistics in China can be tracked and monitored. We explored this research objective with agricultural traceability and security in mind. One option that could be considered is the blockchain technology. Blockchain could also be used to ascertain the provenance of agricultural products to increase the quality and safety of the Chinese agricultural supply chain. In this context, this research converged on big data and technology, platforms and other means for product quality and safety of agricultural products traceability. In order to verify the accuracy of these three convergence, regression analysis were used to construct five models for verification of three hypothesis. The results show that based on "Internet+", using big data, big technology and big platform can significantly increase the accuracy of agricultural products traceability system hence improve consumer acceptance of the safety of agricultural products. [ABSTRACT FROM AUTHOR]

Although telehealth has been used in the health sector for many years, there is still the perception that it is far from reaching its potential. Telehealth outcomes can be improved if knowledge is effectively managed, but this has proved complex to achieve in practice. Systems approaches that take a holistic view and address fundamental issues are likely to result in greater success than focusing solely on software systems. Managing knowledge in telehealth projects draws upon literature on knowledge management, team learning and virtual working. We report on the findings from interviews with telehealth professionals of their experiences and insights on managing knowledge. The major finding is that telehealth projects are functional in nature, focusing on patient diagnosis or training but rarely consider improving knowledge acquisition and sharing. We explain the reasons for this including the lack of understanding of knowledge management concepts that serve as a barrier to improvements. An integrated set of requirements for a telehealth knowledge management system is proposed that has potential to be a facilitator of change in this area. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Telehealth literature has grown over the years to be a substantial body of work. However, research still highlights problems in extracting value from telehealth activities. In this paper, we analyse the telehealth literature over the period 2000–2015. The methodology focuses on paradoxes or surprises in the findings. We explain the lack of an operating model for telehealth as a debilitating feature of its history. We also note that many of the barriers cited in the literature have not changed over the period. We call for a focused research agenda on telehealth that provides clear evidence for some of the benefits such as cost savings and patient benefits. If telehealth activities are to progress, fundamental issues need to be addressed both in research and practice. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Healthcare service delivery is moving forward from individual care to population health management, because of the fast growth of health records. However, to improve population health performance, it is necessary to leverage relevant data and information using new technology solutions, such as pervasive diabetes mobile technology solution of Inet International Inc., which offers the potential to facilitate patient empowerment with gestational diabetic care. Hence, this article examines the pilot study outcomes of a small clinical trial focusing on pregnant patients affected by gestational diabetes mellitus, in an Australian not for profit healthcare context. The aims include establishing proof of concept and also assessing the usability, acceptability, and functionality of this mobile solution and thereby generate hypotheses to be tested in a large-scale confirmatory clinical trial. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to examine shelf management practices of grocery retail chains and their category captains (CCs) in the marketing of consumer packaged goods. Design/methodology/approach – This is a qualitative, exploratory study that is set in a duopoly retail environment in the Asia-Pacific region. The study employed 18 in-depth interviews with executives and managers of two umbrella retail organisations and their suppliers/manufacturers. The method of data analysis employed was content analysis. Findings – Despite CC input in merchandising decisions, it is the retail chains that have the final say on shelf matters. There is no risk of strategic loss of power in employing CCs to manage store product categories on behalf of, or in partnership with retailers provided the retail chains closely monitor the activities and decisions/recommendations of the captains. Research limitations/implications – The paper is developed from data obtained from the grocery retail industry of one economy. Future research would need to extend this study to other economies with similar as well as different conditions. Practical implications – The research offers reassurance to grocery retail practitioners who may be contemplating lessening the burden of managing all their store categories by themselves and switching to CC arrangements. The reassurance is that the reported risk associated with loss of power is manageable. Originality/value – The paper has created a typology termed the “category captain arrangement/grocery retail concentration matrix” and specific directions for further research. [ABSTRACT FROM AUTHOR]

Background The maturity and usage of wireless technology has influenced health services, and this has raised expectations from users that healthcare services will become more affordable due to technology growth. There is increasing evidence to justify this expectation, as telehealth is becoming more and more prevalent in many countries. Thus, health services are now offered beyond the boundaries of traditional hospitals, giving rise to many external factors dictating their quality. This has led us to investigate the factors that motivate and limit the implementation of ICT applications in the healthcare domain. Methods We used a mixed method approach with the qualitative aspects leading the quantitative aspects. The main reason for this approach was to understand and explore the domain through the qualitative aspects as we could be part of the discussion. Then we conducted a quantitative survey to extract more responses in order to justify the claims explored in the qualitative process. Results We found that there are a number of internal and external factors influencing ICT adoption in the healthcare environment so that services can be provided via ICT tools. These factors were grouped under factors contributing to improved outcomes, efficiency and the management of technology. We conceptualised that these three groups of factors drive ICT implementation to assure health services. Conclusions The main lesson learned from this research was that Information Systems discipline needs to urgently consider health informatics as a serious growth area. We also found that as IS researchers, we need to 'mix' with the health environment in order to understand the environment and then develop suitable methods to answer posited research questions. [ABSTRACT FROM AUTHOR]

Virtual expertise is a critical component of telehealth projects and it needs to be effectively managed if telehealth is to deliver on its potential. There are many issues within telehealth that relate to the management of knowledge, and we identify and classify these using a range of papers on the topic. We introduce a virtual expertise platform that provides basic building blocks for effect-leveraging of expertise in this domain. The platform includes management directives and goals, a collaborative culture, an appropriate information and communication technology platform, a knowledge management layer, and a tools or application layer that interact to facilitate knowledge sharing, and ultimately improve patient care. We also emphasize how social media tools can be used as part of the tools layer to improve virtual knowledge sharing before and after telehealth events. [ABSTRACT FROM PUBLISHER]

Based on the motivations of six Australian organizations, the authors propose an analytical model to help decision makers identify the complex relationships between their internal goals and targeted users when deploying mobile data services. [ABSTRACT FROM AUTHOR]

This study explores the perception and views of healthcare professionals in the subcontinent (India and Pakistan) towards the wireless handheld technology in the healthcare setting. A mixed methodology was adopted to explore the determinants of the wireless handheld devices in the healthcare setting. Interviews were conducted with 30 healthcare professionals to explore the initial themes. This was followed up with a survey instrument, specifically developed for this study, and distributed to 300 healthcare professionals in Pakistan and India. 200 useable surveys from India and 97 from Pakistan were received. The results of the study indicate that healthcare professionals felt that to use the wireless technology, the integration of the clinical and operational process is essential. Factor analysis through SPSS showed that any development of technological solutions for handheld devices would benefit, by considering the clinical, technological, and operational influences of the wireless technology in addition to clinical influences, clinical preference, training, and technical support. The study culminated in the development of an initial conceptual framework. The scope of this study is restricted to wireless handheld devices such as the smart phones, handheld PCs and PDAs. [ABSTRACT FROM AUTHOR]