Mobile devices are increasingly becoming an indispensable part of people's daily life, facilitating to perform a variety of useful tasks. Mobile cloud computing integrates mobile and cloud computing ...to expand their capabilities and benefits and overcomes their limitations, such as limited memory, CPU power, and battery life. Big data analytics technologies enable extracting value from data having four Vs: volume, variety, velocity, and veracity. This paper discusses networked healthcare and the role of mobile cloud computing and big data analytics in its enablement. The motivation and development of networked healthcare applications and systems is presented along with the adoption of cloud computing in healthcare. A cloudlet-based mobile cloud-computing infrastructure to be used for healthcare big data applications is described. The techniques, tools, and applications of big data analytics are reviewed. Conclusions are drawn concerning the design of networked healthcare systems using big data and mobile cloud-computing technologies. An outlook on networked healthcare is given.
Oral health is one of the most important components of health that can affect the quality of life of children. Oral problems affect over 600 million children, globally. Evidence showed that ...mobile-based applications have the potential to facilitate self-care processes for oral health in children. Despite the importance of children's oral health and the role of mobile health in facilitating parental education, few systematic reviews were conducted regarding child oral health and mobile-based applications.
This study aimed to investigate the characteristics and capabilities of m-health applications in children's oral health.
A systematic search was completed using keywords alongside thesaurus and MeSH terms on Web of Science, Scopus, and PubMed databases from 2015 to 2020. The present study was completed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist based on inclusion and exclusion criteria. A synthesis was done to report study findings and patterns identified across studies. All results were analyzed and summarized into general and specific categories.
Out of 23 articles included, only nine studies were RCTs (Randomized controlled trials), two studies were cross-sectional studies, one study was evidence-based, and one study was a cluster-randomized trial. According to the findings, most studies were conducted in Brazil, the United Kingdom, and the United States. In terms of the field of dentistry and the application of studies related to dental science, studies fall into three domains, including oral health (61%), orthodontics (4%), and periodontology (26%). Our analysis showed that the developed mobile health applications are equipped with some features to help patients improve their oral health. The most popular features include educational multimedia, game-based stories, reminders, and sending SMS.
This study could be the first step towards enhancing our understanding and knowledge regarding applications of mobile health applications in children's oral health. These applications should be developed by involving the end users in the design phase and should be evaluated in terms of usability and effects on the oral health of children.
The aim of this article is to discuss how different factors affect the decision of intention to use and adopt mobile health applications using the extended technology acceptance model (TAM) among ...older adults in Iraq. “Perceived usefulness (PU), perceived ease of use (PEU), subjective norm (SN), and facilitating conditions (FC)” were four key predictors. Gender and age were included as factors for moderating the impact of two key TAM components in the proposed model (PU and PEU) on intention to use and adoption behaviors. The results of the past studies indicated that PU, PEU and SN were important predictors of adoption of mobile health applications among older adults in Iraq, While PU, SN, and FC were important predictors of the intention to use mobile health applications. Previous studies highlighted a strong impact of PEU on the intention to use mobile health applications on older adults than for younger adults. Implications are discussed for future research and practices.
This review centers upon two-dimensional (2D) nanomaterials beyond graphene and their utilization in the development of novel electrochemical (bio)sensors and analytical devices. The text is ...organized in three main sections including the presentation of the most important families of 2D nanomaterials, an outline of “top-down” and hydro(solvo)thermal methods which are commonly employed for the production of 2D nanomaterials, and finally a detailed overview on the progress had been made the last three years on the use of 2D nanomaterials on the development of electrochemical (bio)sensors and analytical devices in water & food analysis, drug sensing, diabetes monitoring, cancer diagnostics, and virus sensing. Critical discussion on the effect of the 2D nanomaterials on various sensing buildups along with the perspectives of further improving the utility of 2D nanomaterials in (bio)sensing applications, in real world samples, are discussed.
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•Recent advances of 2D materials beyond graphene in (bio)sensing are reviewed.•Focus is on water & food analysis and health applications (H.Ap).•H.Ap include drug analysis, diabetes monitoring, cancer diagnostics and virus sensing.•MXenes is the most extensively used 2D material among those considered in the review.•Further exploration of the antifouling properties of 2D nanomaterials is warranted.
Disease Prediction via Graph Neural Networks Sun, Zhenchao; Yin, Hongzhi; Chen, Hongxu ...
IEEE journal of biomedical and health informatics,
2021-March, 2021-Mar, 2021-3-00, 20210301, Letnik:
25, Številka:
3
Journal Article
Recenzirano
With the increasingly available electronic medical records (EMRs), disease prediction has recently gained immense research attention, where an accurate classifier needs to be trained to map the input ...prediction signals (e.g., symptoms, patient demographics, etc.) to the estimated diseases for each patient. However, existing machine learning-based solutions heavily rely on abundant manually labeled EMR training data to ensure satisfactory prediction results, impeding their performance in the existence of rare diseases that are subject to severe data scarcity. For each rare disease, the limited EMR data can hardly offer sufficient information for a model to correctly distinguish its identity from other diseases with similar clinical symptoms. Furthermore, most existing disease prediction approaches are based on the sequential EMRs collected for every patient and are unable to handle new patients without historical EMRs, reducing their real-life practicality. In this paper, we introduce an innovative model based on Graph Neural Networks (GNNs) for disease prediction, which utilizes external knowledge bases to augment the insufficient EMR data, and learns highly representative node embeddings for patients, diseases and symptoms from the medical concept graph and patient record graph respectively constructed from the medical knowledge base and EMRs. By aggregating information from directly connected neighbor nodes, the proposed neural graph encoder can effectively generate embeddings that capture knowledge from both data sources, and is able to inductively infer the embeddings for a new patient based on the symptoms reported in her/his EMRs to allow for accurate prediction on both general diseases and rare diseases. Extensive experiments on a real-world EMR dataset have demonstrated the state-of-the-art performance of our proposed model.
Β-glucan is a strongly hydrophilic non-starchy polysaccharide, which, when incorporated in food, is renowned for its ability to alter functional characteristics such as viscosity, rheology, texture, ...and sensory properties of the food product. The functional properties of β-glucans are directly linked to their origin/source, molecular weight, and structural features. The molecular weight and structural/conformational features are in turn influenced by method of extraction and modification of the β-glucan. For example, whereas physical modification techniques influence only the spatial structures, modification by chemical agents, enzyme hydrolysis, mechanical treatment, and irradiation affect both spatial conformation and primary structures of β-glucan. Consequently, β-glucan can be modified (via one or more of the aforementioned techniques) into forms that have desired morphological, rheological, and (bio)functional properties. This review describes how various modification techniques affect the structure, properties, and applications of β-glucans in the food industry.
•Using speech materials recorded from speakers with different voice disorders this paper shows: how continuous speech recordings recorded in voice clinics can be automatically annotated to identify ...different phonetic regions.•That consideration of how voice disorders differentially affect phonetic regions can improve the discrimination between types of disorder.•That automated differential diagnosis of voice disorders from a read passage is possible at sensitivities and specificities typical of diagnostic screening tests.
In this paper we evaluate the hypothesis that automated methods for diagnosis of voice disorders from speech recordings would benefit from contextual information found in continuous speech. Rather than basing a diagnosis on how disorders affect the average acoustic properties of the speech signal, the idea is to exploit the possibility that different disorders will cause different acoustic changes within different phonetic contexts. Any differences in the pattern of effects across contexts would then provide additional information for discrimination of pathologies. We evaluate this approach using two complementary studies: the first uses a short phrase which is automatically annotated using a phonetic transcription, the second uses a long reading passage which is automatically annotated from text. The first study uses a single sentence recorded from 597 speakers in the Saarbrucken Voice Database to discriminate structural from neurogenic disorders. The results show that discrimination performance for these broad pathology classes improves from 59% to 67% unweighted average recall when classifiers are trained for each phone-label and the results fused. Although the phonetic contexts improved discrimination, the overall sensitivity and specificity of the method seems insufficient for clinical application. We hypothesise that this is because of the limited contexts in the speech audio and the heterogeneous nature of the disorders. In the second study we address these issues by processing recordings of a long reading passage obtained from clinical recordings of 60 speakers with either Spasmodic Dysphonia or Vocal fold Paralysis. We show that discrimination performance increases from 80% to 87% unweighted average recall if classifiers are trained for each phone-labelled region and predictions fused. We also show that the sensitivity and specificity of a diagnostic test with this performance is similar to other diagnostic procedures in clinical use. In conclusion, the studies confirm that the exploitation of contextual differences in the way disorders affect speech improves automated diagnostic performance, and that automated methods for phonetic annotation of reading passages are robust enough to extract useful diagnostic information.
Anxiety and depressive disorders affect 20% of the population, cause functional impairment, and represent a leading cause of disability. Although evidence-based treatments exist, the shortage of ...trained clinicians and high demand for mental health services have resulted in limited access to evidence-based care. Digital mental health applications (DMHA) present innovative, scalable, and sustainable solutions to address disparities in mental health care.
The present study used meta-analytic techniques to evaluate the therapeutic effect of DMHAs in randomized controlled trials (RCTs) for individuals experiencing anxiety and/or depressive symptoms. Search terms were selected based on concepts related to digital mental health applications, mental health/wellness, intervention type, trial design, and anxiety and/or depression symptoms/diagnosis outcomes to capture all potentially eligible results. Potential demographic, DMHA, and trial design characteristics were examined as moderators of therapeutic effects.
Random effects meta-analyses found that stand-alone DMHAs produced a modest reduction in anxiety (g = 0.31) and depressive (g = 0.35) symptom severity. Several moderators influenced the therapeutic effects of DMHAs for anxiety and/or depressive symptoms including treatment duration, participant inclusion criteria, and outcome measures.
Minimal information was available on DMHA usability and participant engagement with DMHAs within RCTs.
While DMHAs have the potential to be scalable and sustainable solutions to improve access and availability of evidence-based mental healthcare, moderator analyses highlight the considerations for implementation of DMHAs in practice. Further research is needed to understand factors that influence therapeutic effects of DMHAs and investigate strategies to optimize its implementation and overcome the extant research-to-practice gap.
•Digital mental health apps (DMHA) produce modest reductions in anxiety and depression severity as stand-alone treatments.•DMHA trials with longer treatment duration and symptomatic samples exhibit greater therapeutic effects.•Treatment effects differed based on outcome measure used, which has implications for future trials and clinical practice.•Little information on DMHA usability and participant engagement was available in RCTS, but warrants further investigation.
Besides surfactants, which decrease the interfacial tension between two immiscible liquids, also interfacially active particles can successfully stabilize an emulsion system by attaching at the ...liquid–liquid interface. The preparation of the resulting Pickering emulsions has been so far investigated starting from the study of the interactions arising between the dispersed droplets and the stabilizers, till the application of these systems in a wide range of different fields. This work is intended to provide an overall overview about the development of Pickering emulsions by considering the most general aspects and scanning the diverse types of solid stabilizers. Among them, Halloysite nanotubes play a major role as naturally derived clay with emulsifying capability owing to their cheap, abundant, green and biocompatible properties. Therefore, the design of Halloysite stabilized Pickering emulsions is the main content of this review, which will survey the role of nanotubes in providing colloidal stability and will comprehensively sum up the use of these particles in technological and industrial purposes: from environmental to catalytic, from health to cultural heritage related applications.
This review provides meaningful insights about the design of Halloysite stabilized Pickering emulsions. The general aspects about their properties are enlightened by focusing many different factors. Most importantly, the applications of the clay nanotubes stabilized droplets in both technological and industrial fields are considered: from environmental remediation to catalysis, from health science to cultural heritage conservation.
Hypertension is 1 of the major global public health challenges, which means that patients with hypertension need more measures to control their blood pressure. Currently, smart phones and ...applications are developing rapidly, and mobile health applications are used to manage hypertension, but evidences related to effectiveness are limited.
The purpose was to assess the impact of m-Health apps on blood pressure control, medication adherence.
480 participants were randomly assigned to the intervention and control groups. The intervention group used the "Yan Fu" app to manage their blood pressure, and the control group did not use any m-Health apps. The outcomes were changes in blood pressure, the percentage of participants with their blood pressure under control and medication adherence.
At the end of the study, the baseline characteristics between the 2 groups had no statistically differences (P > .05). Participants in the 2 groups all had lower systolic blood pressure and diastolic blood pressure than they did at baseline, and the intervention group demonstrated a significantly greater systolic blood pressure and diastolic blood pressure reduction than the control group (P < .05). Additionally, the percentage of participants with controlled blood pressure was higher in the intervention group (P < .05). The medication adherence of the intervention group was much higher than that of the control group (P < .05).
M-Health apps are effective for hypertension management, it can favor the medication adherence and blood pressure control. Perhaps m-Health apps can be promoted in the blood pressure control.
This study was registered in the Chinese Clinical Trial Registry under the number ChiCTR-IOR-17012069.
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