Applications of the Capability Approach in the Health Field: A Literature Review.
Ontology highlight
ABSTRACT:
SUBMITTER: Mitchell PM
PROVIDER: S-EPMC5511308 | biostudies-other | 2017
REPOSITORIES: biostudies-other
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Project description:Large-scale transcriptome and methylome data analyses obtained by high-throughput technologies have been enabling the identification of novel imprinted genes. We investigated genome-wide DNA methylation patterns in multiple human tissues, using a high-resolution microarray to uncover hemimethylated CpGs located in promoters overlapping CpG islands, aiming to identify novel candidate imprinted genes. Using our approach, we recovered ~30% of the known human imprinted genes, further 168 candidates were identified, 61 of which with at least three hemimethylated CpGs shared by more than two tissue types. Thirty-four of these candidate genes are members of the protocadherins cluster on 5q31.3; in mice, protocadherin genes have non-imprinted monoallelic randomic expression, which might be the case in humans. Among the remaining 27 genes, ZNF331 was recently validated as an imprinted gene, and six of them have been reported as candidates, supporting our prediction. Five candidates (CCDC166, ARC, PLEC, TONSL and VPS28) map to 8q24.3, and might constitute a novel imprinted cluster. Additionally, we performed a comprehensive compilation of known human and mice imprinted genes from literature and databases, and a comparison among high-throughput imprinting studies in humans. The screening for hemimethylated CpGs shared by multiple human tissues, together with the extensive review, appears as a useful approach to reveal candidate imprinted genes.
2017-09-26 | GSE103413 | GEO
Project description:BackgroundMachine learning has attracted considerable research interest toward developing smart digital health interventions. These interventions have the potential to revolutionize health care and lead to substantial outcomes for patients and medical professionals.ObjectiveOur objective was to review the literature on applications of machine learning in real-life digital health interventions, aiming to improve the understanding of researchers, clinicians, engineers, and policy makers in developing robust and impactful data-driven interventions in the health care domain.MethodsWe searched the PubMed and Scopus bibliographic databases with terms related to machine learning, to identify real-life studies of digital health interventions incorporating machine learning algorithms. We grouped those interventions according to their target (ie, target condition), study design, number of enrolled participants, follow-up duration, primary outcome and whether this had been statistically significant, machine learning algorithms used in the intervention, and outcome of the algorithms (eg, prediction).ResultsOur literature search identified 8 interventions incorporating machine learning in a real-life research setting, of which 3 (37%) were evaluated in a randomized controlled trial and 5 (63%) in a pilot or experimental single-group study. The interventions targeted depression prediction and management, speech recognition for people with speech disabilities, self-efficacy for weight loss, detection of changes in biopsychosocial condition of patients with multiple morbidity, stress management, treatment of phantom limb pain, smoking cessation, and personalized nutrition based on glycemic response. The average number of enrolled participants in the studies was 71 (range 8-214), and the average follow-up study duration was 69 days (range 3-180). Of the 8 interventions, 6 (75%) showed statistical significance (at the P=.05 level) in health outcomes.ConclusionsThis review found that digital health interventions incorporating machine learning algorithms in real-life studies can be useful and effective. Given the low number of studies identified in this review and that they did not follow a rigorous machine learning evaluation methodology, we urge the research community to conduct further studies in intervention settings following evaluation principles and demonstrating the potential of machine learning in clinical practice.
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Project description:BackgroundWith the rapid adoption of electronic health records (EHRs), it is desirable to harvest information and knowledge from EHRs to support automated systems at the point of care and to enable secondary use of EHRs for clinical and translational research. One critical component used to facilitate the secondary use of EHR data is the information extraction (IE) task, which automatically extracts and encodes clinical information from text.ObjectivesIn this literature review, we present a review of recent published research on clinical information extraction (IE) applications.MethodsA literature search was conducted for articles published from January 2009 to September 2016 based on Ovid MEDLINE In-Process & Other Non-Indexed Citations, Ovid MEDLINE, Ovid EMBASE, Scopus, Web of Science, and ACM Digital Library.ResultsA total of 1917 publications were identified for title and abstract screening. Of these publications, 263 articles were selected and discussed in this review in terms of publication venues and data sources, clinical IE tools, methods, and applications in the areas of disease- and drug-related studies, and clinical workflow optimizations.ConclusionsClinical IE has been used for a wide range of applications, however, there is a considerable gap between clinical studies using EHR data and studies using clinical IE. This study enabled us to gain a more concrete understanding of the gap and to provide potential solutions to bridge this gap.
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Project description:Objective and methods:Various papers related to the application of adverse outcome pathways (AOPs) for the prevention of occupational disease were reviewed. The Internet was used as the primary tool to search for the necessary research data and information, using such online resources as Google Scholar, ScienceDirect, Scopus, NDSL, and PubMed. The key search terms were "adverse outcome pathway," "toxicology," "risk assessment," "human," "worker," "occupational safety and health," and so on. Results and conclusion:The aim of this paper is to explain the use of AOP for the understanding of chemical toxicity as a conceptual means and to predict the toxic mechanism. The tools of AOP have emerged as a forward-looking alternative to the existing chemical risk assessment paradigm. AOP is being applied to the assessment of acute toxicity and to chronic toxic chemicals in the workplace. Not only can it lead to breakthroughs in occupational and environmental cancer prevention, it is also widely used in chemical risk assessment and has led to breakthroughs in the prevention of occupational disease in the workplace.
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Project description:IntroductionPublic health investigations, including research, in refugee populations are necessary to inform evidence-based interventions and care. The unique challenges refugees face (displacement, limited political protections, economic hardship) can make them especially vulnerable to harm, burden, or undue influence. Acute survival needs, fear of stigma or persecution, and history of trauma may present challenges to ensuring meaningful informed consent and establishing trust. We examined the recently published literature to understand the application of ethics principles in investigations involving refugees.MethodsWe conducted a preliminary review of refugee health literature (research and non-research data collections) published from 2015 through 2018 available in PubMed. Article inclusion criteria were: participants were refugees, topic was health-related, and methods used primary data collection. Information regarding type of investigation, methods, and reported ethics considerations was abstracted.ResultsWe examined 288 articles. Results indicated 33% of investigations were conducted before resettlement, during the displacement period (68% of these were in refugee camps). Common topics included mental health (48%) and healthcare access (8%). The majority (87%) of investigations obtained consent. Incentives were provided less frequently (23%). Most authors discussed the ways in which community stakeholders were engaged (91%), yet few noted whether refugee representatives had an opportunity to review investigational protocols (8%). Cultural considerations were generally limited to gender and religious norms, and 13% mentioned providing some form of post-investigation support.ConclusionsOur analysis is a preliminary assessment of the application of ethics principles reported within the recently published refugee health literature. From this analysis, we have proposed a list of best practices, which include stakeholder engagement, respect for cultural norms, and post-study support. Investigations conducted among refugees require additional diligence to ensure respect for and welfare of the participants. Development of a refugee-specific ethics framework with ethics and refugee health experts that addresses the need for stakeholder involvement, appropriate incentive use, protocol review, and considerations of cultural practices may help guide future investigations in this population.
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Project description:BackgroundDespite a growing body of research into both Artificial intelligence and mental health inpatient flow issues, few studies adequately combine the two. This review summarises findings in the fields of AI in psychiatry and patient flow from the past 5 years, finds links and identifies gaps for future research.MethodsThe OVID database was used to access Embase and Medline. Top journals such as JAMA, Nature and The Lancet were screened for other relevant studies. Selection bias was limited by strict inclusion and exclusion criteria.Research3,675 papers were identified in March 2020, of which a limited number focused on AI for mental health unit patient flow. After initial screening, 323 were selected and 83 were subsequently analysed. The literature review revealed a wide range of applications with three main themes: diagnosis (33%), prognosis (39%) and treatment (28%). The main themes that emerged from AI in patient flow studies were: readmissions (41%), resource allocation (44%) and limitations (91%). The review extrapolates those solutions and suggests how they could potentially improve patient flow on mental health units, along with challenges and limitations they could face.ConclusionResearch widely addresses potential uses of AI in mental health, with some focused on its applicability in psychiatric inpatients units, however research rarely discusses improvements in patient flow. Studies investigated various uses of AI to improve patient flow across specialities. This review highlights a gap in research and the unique research opportunity it presents.
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Project description:Q fever, a zoonotic disease transmitted from animals to humans, is a significant public health problem with a potential for outbreaks to occur. Q fever prevention strategies should incorporate human, animal, and environmental domains. A One Health approach, which engages cross-sectoral collaboration among multiple stakeholders, may be an appropriate framework and has the underlying principles to control Q fever holistically. To assess whether components of One Health for Q fever prevention and control have been applied, a comprehensive literature review was undertaken. We found 16 studies that had practiced or recommended a One Health approach. Seven emerging themes were identified: Human risk assessment, human and animal serology, integrated human?animal surveillance, vaccination for at-risk groups, environmental management, multi-sectoral collaboration, and education and training. Within the multi-sectoral theme, we identified five subthemes: Policy and practice guidelines, information sharing and intelligence exchange, risk communication, joint intervention, and evaluation. One Health practices varied between studies possibly due to differences in intercountry policy, practice, and feasibility. However, the key issue of the need for multi-sectoral collaboration was highlighted across most of the studies. Further research is warranted to explore the barriers and opportunities of adopting a One Health approach in Q fever prevention and control.
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2024-11-13 | GSE278926 | GEO