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European Respiratory Review : An... Jun 2023COPD and adult-onset asthma (AOA) are the most common noncommunicable respiratory diseases. To improve early identification and prevention, an overview of risk factors... (Review)
Review
BACKGROUND
COPD and adult-onset asthma (AOA) are the most common noncommunicable respiratory diseases. To improve early identification and prevention, an overview of risk factors is needed. We therefore aimed to systematically summarise the nongenetic (exposome) risk factors for AOA and COPD. Additionally, we aimed to compare the risk factors for COPD and AOA.
METHODS
In this umbrella review, we searched PubMed for articles from inception until 1 February 2023 and screened the references of relevant articles. We included systematic reviews and meta-analyses of observational epidemiological studies in humans that assessed a minimum of one lifestyle or environmental risk factor for AOA or COPD.
RESULTS
In total, 75 reviews were included, of which 45 focused on risk factors for COPD, 28 on AOA and two examined both. For asthma, 43 different risk factors were identified while 45 were identified for COPD. For AOA, smoking, a high body mass index (BMI), wood dust exposure and residential chemical exposures, such as formaldehyde exposure or exposure to volatile organic compounds, were amongst the risk factors found. For COPD, smoking, ambient air pollution including nitrogen dioxide, a low BMI, indoor biomass burning, childhood asthma, occupational dust exposure and diet were amongst the risk factors found.
CONCLUSIONS
Many different factors for COPD and asthma have been found, highlighting the differences and similarities. The results of this systematic review can be used to target and identify people at high risk for COPD or AOA.
Topics: Adult; Humans; Child; Pulmonary Disease, Chronic Obstructive; Asthma; Risk Factors; Air Pollution; Dust; Environmental Exposure
PubMed: 37137510
DOI: 10.1183/16000617.0009-2023 -
Health Expectations : An International... Aug 2019Numerous frameworks for supporting, evaluating and reporting patient and public involvement in research exist. The literature is diverse and theoretically heterogeneous.
BACKGROUND
Numerous frameworks for supporting, evaluating and reporting patient and public involvement in research exist. The literature is diverse and theoretically heterogeneous.
OBJECTIVES
To identify and synthesize published frameworks, consider whether and how these have been used, and apply design principles to improve usability.
SEARCH STRATEGY
Keyword search of six databases; hand search of eight journals; ancestry and snowball search; requests to experts.
INCLUSION CRITERIA
Published, systematic approaches (frameworks) designed to support, evaluate or report on patient or public involvement in health-related research.
DATA EXTRACTION AND SYNTHESIS
Data were extracted on provenance; collaborators and sponsors; theoretical basis; lay input; intended user(s) and use(s); topics covered; examples of use; critiques; and updates. We used the Canadian Centre for Excellence on Partnerships with Patients and Public (CEPPP) evaluation tool and hermeneutic methodology to grade and synthesize the frameworks. In five co-design workshops, we tested evidence-based resources based on the review findings.
RESULTS
Our final data set consisted of 65 frameworks, most of which scored highly on the CEPPP tool. They had different provenances, intended purposes, strengths and limitations. We grouped them into five categories: power-focused; priority-setting; study-focused; report-focused; and partnership-focused. Frameworks were used mainly by the groups who developed them. The empirical component of our study generated a structured format and evidence-based facilitator notes for a "build your own framework" co-design workshop.
CONCLUSION
The plethora of frameworks combined with evidence of limited transferability suggests that a single, off-the-shelf framework may be less useful than a menu of evidence-based resources which stakeholders can use to co-design their own frameworks.
Topics: Community Participation; Empowerment; Group Processes; Humans; Patient Participation; Research
PubMed: 31012259
DOI: 10.1111/hex.12888 -
Sensors (Basel, Switzerland) Jul 2023Data provenance means recording data origins and the history of data generation and processing. In healthcare, data provenance is one of the essential processes that... (Review)
Review
Data provenance means recording data origins and the history of data generation and processing. In healthcare, data provenance is one of the essential processes that make it possible to track the sources and reasons behind any problem with a user's data. With the emergence of the General Data Protection Regulation (GDPR), data provenance in healthcare systems should be implemented to give users more control over data. This SLR studies the impacts of data provenance in healthcare and GDPR-compliance-based data provenance through a systematic review of peer-reviewed articles. The SLR discusses the technologies used to achieve data provenance and various methodologies to achieve data provenance. We then explore different technologies that are applied in the healthcare domain and how they achieve data provenance. In the end, we have identified key research gaps followed by future research directions.
Topics: Biomedical Research; Delivery of Health Care
PubMed: 37514788
DOI: 10.3390/s23146495 -
JMIR Medical Informatics Mar 2020Clinical narratives represent the main form of communication within health care, providing a personalized account of patient history and assessments, and offering rich... (Review)
Review
BACKGROUND
Clinical narratives represent the main form of communication within health care, providing a personalized account of patient history and assessments, and offering rich information for clinical decision making. Natural language processing (NLP) has repeatedly demonstrated its feasibility to unlock evidence buried in clinical narratives. Machine learning can facilitate rapid development of NLP tools by leveraging large amounts of text data.
OBJECTIVE
The main aim of this study was to provide systematic evidence on the properties of text data used to train machine learning approaches to clinical NLP. We also investigated the types of NLP tasks that have been supported by machine learning and how they can be applied in clinical practice.
METHODS
Our methodology was based on the guidelines for performing systematic reviews. In August 2018, we used PubMed, a multifaceted interface, to perform a literature search against MEDLINE. We identified 110 relevant studies and extracted information about text data used to support machine learning, NLP tasks supported, and their clinical applications. The data properties considered included their size, provenance, collection methods, annotation, and any relevant statistics.
RESULTS
The majority of datasets used to train machine learning models included only hundreds or thousands of documents. Only 10 studies used tens of thousands of documents, with a handful of studies utilizing more. Relatively small datasets were utilized for training even when much larger datasets were available. The main reason for such poor data utilization is the annotation bottleneck faced by supervised machine learning algorithms. Active learning was explored to iteratively sample a subset of data for manual annotation as a strategy for minimizing the annotation effort while maximizing the predictive performance of the model. Supervised learning was successfully used where clinical codes integrated with free-text notes into electronic health records were utilized as class labels. Similarly, distant supervision was used to utilize an existing knowledge base to automatically annotate raw text. Where manual annotation was unavoidable, crowdsourcing was explored, but it remains unsuitable because of the sensitive nature of data considered. Besides the small volume, training data were typically sourced from a small number of institutions, thus offering no hard evidence about the transferability of machine learning models. The majority of studies focused on text classification. Most commonly, the classification results were used to support phenotyping, prognosis, care improvement, resource management, and surveillance.
CONCLUSIONS
We identified the data annotation bottleneck as one of the key obstacles to machine learning approaches in clinical NLP. Active learning and distant supervision were explored as a way of saving the annotation efforts. Future research in this field would benefit from alternatives such as data augmentation and transfer learning, or unsupervised learning, which do not require data annotation.
PubMed: 32229465
DOI: 10.2196/17984 -
NPJ Precision Oncology Aug 2023This study evaluates the quality of published research using artificial intelligence (AI) for ovarian cancer diagnosis or prognosis using histopathology data. A... (Review)
Review
This study evaluates the quality of published research using artificial intelligence (AI) for ovarian cancer diagnosis or prognosis using histopathology data. A systematic search of PubMed, Scopus, Web of Science, Cochrane CENTRAL, and WHO-ICTRP was conducted up to May 19, 2023. Inclusion criteria required that AI was used for prognostic or diagnostic inferences in human ovarian cancer histopathology images. Risk of bias was assessed using PROBAST. Information about each model was tabulated and summary statistics were reported. The study was registered on PROSPERO (CRD42022334730) and PRISMA 2020 reporting guidelines were followed. Searches identified 1573 records, of which 45 were eligible for inclusion. These studies contained 80 models of interest, including 37 diagnostic models, 22 prognostic models, and 21 other diagnostically relevant models. Common tasks included treatment response prediction (11/80), malignancy status classification (10/80), stain quantification (9/80), and histological subtyping (7/80). Models were developed using 1-1375 histopathology slides from 1-776 ovarian cancer patients. A high or unclear risk of bias was found in all studies, most frequently due to limited analysis and incomplete reporting regarding participant recruitment. Limited research has been conducted on the application of AI to histopathology images for diagnostic or prognostic purposes in ovarian cancer, and none of the models have been demonstrated to be ready for real-world implementation. Key aspects to accelerate clinical translation include transparent and comprehensive reporting of data provenance and modelling approaches, and improved quantitative evaluation using cross-validation and external validations. This work was funded by the Engineering and Physical Sciences Research Council.
PubMed: 37653025
DOI: 10.1038/s41698-023-00432-6 -
Journal of Personalized Medicine Jun 2023This article aims to perform a Systematic Literature Review (SLR) to better understand the structures of different methods, techniques, models, methodologies, and... (Review)
Review
AIMS
This article aims to perform a Systematic Literature Review (SLR) to better understand the structures of different methods, techniques, models, methodologies, and technologies related to provenance data management in health information systems (HISs). The SLR developed here seeks to answer the questions that contribute to describing the results.
METHOD
An SLR was performed on six databases using a search string. The backward and forward snowballing technique was also used. Eligible studies were all articles in English that presented on the use of different methods, techniques, models, methodologies, and technologies related to provenance data management in HISs. The quality of the included articles was assessed to obtain a better connection to the topic studied.
RESULTS
Of the 239 studies retrieved, 14 met the inclusion criteria described in this SLR. In order to complement the retrieved studies, 3 studies were included using the backward and forward snowballing technique, totaling 17 studies dedicated to the construction of this research. Most of the selected studies were published as conference papers, which is common when involving computer science in HISs. There was a more frequent use of data provenance models from the PROV family in different HISs combined with different technologies, among which blockchain and middleware stand out. Despite the advantages found, the lack of technological structure, data interoperability problems, and the technical unpreparedness of working professionals are still challenges encountered in the management of provenance data in HISs.
CONCLUSION
It was possible to conclude the existence of different methods, techniques, models, and combined technologies, which are presented in the proposal of a taxonomy that provides researchers with a new understanding about the management of provenance data in HISs.
PubMed: 37373980
DOI: 10.3390/jpm13060991 -
Foods (Basel, Switzerland) Aug 2023With the rise of globalization and technological competition, the food supply chain has grown more complex due to the multiple players and factors involved in the chain.... (Review)
Review
With the rise of globalization and technological competition, the food supply chain has grown more complex due to the multiple players and factors involved in the chain. Traditional systems fail to offer effective and reliable traceability solutions considering the increasing requirement for accountability and transparency in the food supply chain. Blockchain technology has been claimed to offer the food industry a transformative future. The inherent features of blockchain, including immutability and transparency, create a dependable and secure system for tracking food products across the whole supply chain, ensuring total control over their traceability from the origin to the final consumer. This research offers a comprehensive overview of multiple models to understand how the integration of blockchain and other digital technologies has transformed the food supply chain. This comprehensive systematic review of blockchain-based food-supply-chain frameworks aimed to uncover the capability of blockchain technology to revolutionize the industry and examined the current landscape of blockchain-based food traceability solutions to identify areas for improvement. Furthermore, the research investigates recent advancements and investigates how blockchain aligns with other emerging technologies of Industry 4.0 and Web 3.0. Blockchain technology plays an important role in improving food traceability and supply-chain operations. Potential synergies between blockchain and other emerging technologies of Industry 4.0 and Web 3.0 are digitizing food supply chains, which results in better management, automation, efficiencies, sustainability, verifiability, auditability, accountability, traceability, transparency, tracking, monitoring, response times and provenance across food supply chains.
PubMed: 37628025
DOI: 10.3390/foods12163026 -
Journal of Medical Internet Research May 2021Digital health technologies (DHTs) generate a large volume of information used in health care for administrative, educational, research, and clinical purposes. The... (Review)
Review
BACKGROUND
Digital health technologies (DHTs) generate a large volume of information used in health care for administrative, educational, research, and clinical purposes. The clinical use of digital information for diagnostic, therapeutic, and prognostic purposes has multiple patient safety problems, some of which result from poor information quality (IQ).
OBJECTIVE
This systematic review aims to synthesize an IQ framework that could be used to evaluate the extent to which digital health information is fit for clinical purposes.
METHODS
The review was conducted according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines. We searched Embase, MEDLINE, PubMed, CINAHL, Maternity and Infant Care, PsycINFO, Global Health, ProQuest Dissertations and Theses Global, Scopus, and HMIC (the Health Management Information Consortium) from inception until October 2019. Multidimensional IQ frameworks for assessing DHTs used in the clinical context by health care professionals were included. A thematic synthesis approach was used to synthesize the Clinical Information Quality (CLIQ) framework for digital health.
RESULTS
We identified 10 existing IQ frameworks from which we developed the CLIQ framework for digital health with 13 unique dimensions: accessibility, completeness, portability, security, timeliness, accuracy, interpretability, plausibility, provenance, relevance, conformance, consistency, and maintainability, which were categorized into 3 meaningful categories: availability, informativeness, and usability.
CONCLUSIONS
This systematic review highlights the importance of the IQ of DHTs and its relevance to patient safety. The CLIQ framework for digital health will be useful in evaluating and conceptualizing IQ issues associated with digital health, thus forestalling potential patient safety problems.
TRIAL REGISTRATION
PROSPERO International Prospective Register of Systematic Reviews CRD42018097142; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=97142.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)
RR2-10.1136/bmjopen-2018-024722.
Topics: Delivery of Health Care; Female; Humans; Pregnancy
PubMed: 33835034
DOI: 10.2196/23479 -
European Respiratory Review : An... Sep 2022There is growing interest in a "treatable traits" approach to pulmonary rehabilitation in chronic airways disease. The frequency with which pulmonary rehabilitation... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
There is growing interest in a "treatable traits" approach to pulmonary rehabilitation in chronic airways disease. The frequency with which pulmonary rehabilitation programmes address treatable traits is unknown.
METHODS
Randomised controlled trials of pulmonary rehabilitation compared to usual care in patients with stable chronic airways disease were included. The components of pulmonary rehabilitation delivered were extracted and mapped to treatable traits in pulmonary, extrapulmonary and behavioural/lifestyle domains. Meta-analysis was used to evaluate the impact of addressing >1 treatable trait on exercise capacity and health-related quality of life (HRQoL).
RESULTS
116 trials were included (6893 participants). Almost all pulmonary rehabilitation programmes addressed deconditioning (97% of trials). The most commonly addressed extrapulmonary traits were nutritional status (obesity and cachexia, 18% each) and mood disturbance (anxiety and depression, 10% each). Behavioural/lifestyle traits most frequently addressed were nonadherence (46%), poor inhalation technique (24%) and poor family/social support (19%). Exercise capacity and HRQoL outcomes did not differ between studies that addressed deconditioning alone and those that targeted additional traits, but heterogeneity was high.
CONCLUSION
Aside from deconditioning, treatable traits are infrequently addressed in existing trials of pulmonary rehabilitation. The potential of the treatable traits approach to improve pulmonary rehabilitation outcomes remains to be explored.
Topics: Humans; Phenotype; Pulmonary Disease, Chronic Obstructive; Quality of Life
PubMed: 36002168
DOI: 10.1183/16000617.0042-2022 -
Breast Cancer (Dove Medical Press) 2017Stress has been extensively studied as a psychosomatic factor associated with breast cancer. This study aims to review the prevalence of post-traumatic stress disorder... (Review)
Review
A systematic literature review exploring the prevalence of post-traumatic stress disorder and the role played by stress and traumatic stress in breast cancer diagnosis and trajectory.
Stress has been extensively studied as a psychosomatic factor associated with breast cancer. This study aims to review the prevalence of post-traumatic stress disorder (PTSD), its associated risk factors, the role of predicting factors for its early diagnosis/prevention, the implications for co-treatment, and the potential links by which stress could impact cancer risk, by closely examining the literature on breast cancer survivors. The authors systematically reviewed studies published from 2002 to 2016 pertaining to PTSD, breast cancer and PTSD, and breast cancer and stress. The prevalence of PTSD varies between 0% and 32.3% mainly as regards the disease phase, the stage of disease, and the instruments adopted to detect prevalence. Higher percentages were observed when the Clinician Administered PTSD Scale was administered. In regard to PTSD-associated risk factors, no consensus has been reached to date; younger age, geographic provenance with higher prevalence in the Middle East, and the presence of previous cancer diagnosis in the family or relational background emerged as the only variables that were unanimously found to be associated with higher PTSD prevalence. Type C personality can be considered a risk factor, together with low social support. In light of the impact of PTSD on cognitive, social, work-related, and physical functioning, co-treatment of cancer and PTSD is warranted and a multidisciplinary perspective including specific training for health care professionals in communication and relational issues with PTSD patients is mandatory. However, even though a significant correlation was found between stressful life events and breast cancer incidence, an unequivocal implication of distress in breast cancer is hard to demonstrate. For the future, overcoming the methodological heterogeneity represents one main focus. Efficacy studies could help when evaluating the effect of co-treating breast cancer and post-traumatic stress symptoms, even if all the criteria for a Diagnostic and Statistical Manual of Mental Disorders diagnosis are not fulfilled.
PubMed: 28740430
DOI: 10.2147/BCTT.S111101