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PloS One 2017Automatic detection or classification of adventitious sounds is useful to assist physicians in diagnosing or monitoring diseases such as asthma, Chronic Obstructive... (Review)
Review
BACKGROUND
Automatic detection or classification of adventitious sounds is useful to assist physicians in diagnosing or monitoring diseases such as asthma, Chronic Obstructive Pulmonary Disease (COPD), and pneumonia. While computerised respiratory sound analysis, specifically for the detection or classification of adventitious sounds, has recently been the focus of an increasing number of studies, a standardised approach and comparison has not been well established.
OBJECTIVE
To provide a review of existing algorithms for the detection or classification of adventitious respiratory sounds. This systematic review provides a complete summary of methods used in the literature to give a baseline for future works.
DATA SOURCES
A systematic review of English articles published between 1938 and 2016, searched using the Scopus (1938-2016) and IEEExplore (1984-2016) databases. Additional articles were further obtained by references listed in the articles found. Search terms included adventitious sound detection, adventitious sound classification, abnormal respiratory sound detection, abnormal respiratory sound classification, wheeze detection, wheeze classification, crackle detection, crackle classification, rhonchi detection, rhonchi classification, stridor detection, stridor classification, pleural rub detection, pleural rub classification, squawk detection, and squawk classification.
STUDY SELECTION
Only articles were included that focused on adventitious sound detection or classification, based on respiratory sounds, with performance reported and sufficient information provided to be approximately repeated.
DATA EXTRACTION
Investigators extracted data about the adventitious sound type analysed, approach and level of analysis, instrumentation or data source, location of sensor, amount of data obtained, data management, features, methods, and performance achieved.
DATA SYNTHESIS
A total of 77 reports from the literature were included in this review. 55 (71.43%) of the studies focused on wheeze, 40 (51.95%) on crackle, 9 (11.69%) on stridor, 9 (11.69%) on rhonchi, and 18 (23.38%) on other sounds such as pleural rub, squawk, as well as the pathology. Instrumentation used to collect data included microphones, stethoscopes, and accelerometers. Several references obtained data from online repositories or book audio CD companions. Detection or classification methods used varied from empirically determined thresholds to more complex machine learning techniques. Performance reported in the surveyed works were converted to accuracy measures for data synthesis.
LIMITATIONS
Direct comparison of the performance of surveyed works cannot be performed as the input data used by each was different. A standard validation method has not been established, resulting in different works using different methods and performance measure definitions.
CONCLUSION
A review of the literature was performed to summarise different analysis approaches, features, and methods used for the analysis. The performance of recent studies showed a high agreement with conventional non-automatic identification. This suggests that automated adventitious sound detection or classification is a promising solution to overcome the limitations of conventional auscultation and to assist in the monitoring of relevant diseases.
Topics: Asthma; Automation; Humans; Pneumonia; Pulmonary Disease, Chronic Obstructive; Respiratory Sounds
PubMed: 28552969
DOI: 10.1371/journal.pone.0177926 -
Work (Reading, Mass.) 2023The construction industry is an important productive sector worldwide. However, the industry is also responsible for high numbers of work-related accidents, which... (Review)
Review
BACKGROUND
The construction industry is an important productive sector worldwide. However, the industry is also responsible for high numbers of work-related accidents, which highlights the necessity for improving safety management on construction sites. In parallel, technological applications such as machine learning (ML) are used in many productive sectors, including construction, and have proved significant in process optimizations and decision-making. Thus, advanced studies are required to comprehend the best way of using this technology to enhance construction site safety.
OBJECTIVE
This research developed a systematic literature review using ten scientific databases to retrieve relevant publications and fill the knowledge gaps regarding ML applications in construction accident prevention.
METHODS
This study examined 73 scientific articles through bibliometric research and descriptive analysis.
RESULTS
The results showed the publications timeline and the most recurrent journals, authors, institutions, and countries-regions. In addition, the review discovered information about the developed models, such as the research goals, the ML methods used, and the data features. The research findings revealed that USA and China are the leading countries regarding publications. Also, Support Vector Machine - SVM was the most used ML method. Furthermore, most models used textual data as a source, generally related to inspection reports and accident narratives. The data approach was usually related to facts before an accident (proactive data).
CONCLUSION
The review highlighted improvement proposals for future works and provided insights into the application of ML in construction safety management.
PubMed: 36938767
DOI: 10.3233/WOR-220533 -
International Journal of Critical... 2019The purpose of this systematic review was to identify the antecedent factors of workplace injuries in small- and medium-sized enterprises (SMEs). A customized systematic... (Review)
Review
The purpose of this systematic review was to identify the antecedent factors of workplace injuries in small- and medium-sized enterprises (SMEs). A customized systematic review protocol included the research question, literature search, quality appraisal, data management and extraction, and evidence synthesis. The evidence was evaluated using the Critical Appraisal Skills Programme checklists and the Cochrane Collaboration "Risk of Bias" assessment tools. A total of 1355 articles were identified before duplicate removal. Ten articles were relevant to the study objective. Of these, two articles examined antecedents related to physical injuries, three examined those related to psychological injuries, and four focused on a combination. Antecedent factors included older workers, unsafe acts, unsafe working conditions, accident type and type of work performed, trips and falls, loss in productivity, social isolation, financial stress, and lack of employer support during the return to the workplace. The findings of this systematic review support the need for increased research to identify antecedent factors associated with injury in SMEs. Research should focus on interventions to mitigate injury rates that associate employees with employers, thus promoting collaboration in augmenting health and safety in SMEs.
PubMed: 31334046
DOI: 10.4103/IJCIIS.IJCIIS_78_18 -
Malaria Research and Treatment 2019Malaria is a protozoan disease caused by the species. Among the five species. Among the five and malaria are by far the most predominant and widely Malaria is one... (Review)
Review
BACKGROUND
Malaria is a protozoan disease caused by the species. Among the five species. Among the five and malaria are by far the most predominant and widely Malaria is one of the leading causes of morbidity and mortality globally, particularly in the sub-Saharan countries including Ethiopia. It is also a major obstacle to socio-economic development in the country.
METHODS
Articles were searched from PubMed, Google Scholar, and Science Direct databases. The pooled prevalence estimates were analyzed using the DerSimonian-Laird random-effects model and the possible sources of heterogeneity were evaluated through subgroup analysis, metaregression, and sensitivity analysis. Publication bias was analyzed using funnel plots and Egger's test statistics. The data management and analysis were done using STATA 15.1 version software.
RESULTS
Among 922 studies initially identified, thirty-five full-text articles fulfilled the inclusion criteria and included in the study. The combined, and malaria are by far the most predominant and widely.
CONCLUSIONS
This systematic review and meta-analysis showed a high malaria prevalence in Ethiopia. Therefore, previous prevention and control measures should be revised and/or strengthened as appropriate and new strategies should be implemented. In addition, technical, financial and material support, and coordination of the regional capacity building and logistics should be adequately implemented.
PubMed: 32089818
DOI: 10.1155/2019/7065064 -
Use of Mobile Crowdsensing in Disaster Management: A Systematic Review, Challenges, and Open Issues.Sensors (Basel, Switzerland) Feb 2023With the increasing efforts to utilize information and communication technologies (ICT) in disaster management, the massive amount of heterogeneous data that is... (Review)
Review
With the increasing efforts to utilize information and communication technologies (ICT) in disaster management, the massive amount of heterogeneous data that is generated through ubiquitous sensors paves the way for fast and informed decisions in the case of disasters. Utilization of the big "sensed" data leads to an effective and efficient management of disaster situations so as to prevent human and economic losses. The advancement of built-in sensing technologies in smart mobile devices enables crowdsourcing of sensed data, which is known as mobile crowdsensing (MCS). This systematic literature review investigates the use of mobile crowdsensing in disaster management on the basis of the built-in sensor types in smart mobile devices, disaster management categories, and the disaster management cycle phases (i.e., mitigation, preparedness, response, and recovery activities). Additionally, this work seeks to unveil the frameworks or models that can potentially guide disaster management authorities towards integrating crowd-sensed data with their existing decision-support systems. The vast majority of the existing studies are conceptual as they highlight a challenge in experimental testing of the disaster management solutions in real-life settings, and there is little emphasis on the use cases of crowdsensing through smartphone sensors in disaster incidents. In light of a thorough review, we provide and discuss future directions and open issues for mobile crowdsensing-aided disaster management.
PubMed: 36772738
DOI: 10.3390/s23031699 -
Journal of Alzheimer's Disease : JAD 2015Frontotemporal dementia (FTD) is a neurodegenerative disorder characterized by changes in behavior and language caused by focal degeneration of the frontal and anterior... (Review)
Review
Frontotemporal dementia (FTD) is a neurodegenerative disorder characterized by changes in behavior and language caused by focal degeneration of the frontal and anterior temporal lobes. The behavioral symptoms are distressing to patients and their caregivers. Non-pharmacological management is important as no disease-specific pharmacological treatment for FTD is currently available. The primary objective is to review the literature on non-pharmacological management for FTD and to propose directions for future research, with reference to findings. A search was performed using PubMed, MEDLINE, and EMBASE. Search terms included "frontotemporal dementia", and words related to non-pharmacological management, and it identified a total of 858 articles. Results revealed that very few randomized controlled trials exist on non-pharmacological management interventions for FTD. These interventions have been proposed by literature based on clinical experience. A small number of studies have supported behavioral management techniques that exploit disease-specific behaviors and preserved functions in patients with FTD, along with the management of caregivers' distress. These limitations warrant well-designed large-scale research to examine effects of non-pharmacological interventions on behavioral symptoms of FTD.
Topics: Databases, Bibliographic; Disease Management; Frontotemporal Dementia; Humans
PubMed: 25737152
DOI: 10.3233/JAD-142109 -
Journal of Orthopaedic Trauma Jan 2021The aim of this comparative effectiveness study was to perform a meta-analysis of adverse events and outcomes in closed geriatric olecranon fractures, without elbow... (Meta-Analysis)
Meta-Analysis
OBJECTIVES
The aim of this comparative effectiveness study was to perform a meta-analysis of adverse events and outcomes in closed geriatric olecranon fractures, without elbow instability, after treatment with surgical or nonoperative management.
DATA SOURCES
PubMed, Web of Science, and Embase databases.
STUDY SELECTION
Articles were included if they contained clinical data evaluating outcomes in patients ≥65 years of age with closed olecranon fractures, without elbow instability, treated surgically, or with nonoperative management.
DATA EXTRACTION
Data regarding patient age, olecranon fracture type, fracture union, adverse events, reoperation, elbow range of motion, and surgeon and patient reported outcome measures were recorded according to intervention. The interventions included for analysis were tension band wire fixation, plate fixation, or nonoperative management.
DATA SYNTHESIS
Separate random effects meta-analyses were conducted for each outcome according to intervention. Prevalence and 95% confidence intervals were calculated for dichotomous variables, whereas weighted means and confidence intervals were calculated for continuous variables.
CONCLUSIONS
Comparable outcomes were achieved with surgical or nonoperative management of olecranon fractures in geriatric patients. Surgical intervention carried a high risk of reoperation regardless of whether plate or tension band wire fixation was used. Functional nonunion can be anticipated if nonoperative treatment is elected in low-demand elderly patients.
LEVEL OF EVIDENCE
Therapeutic Level IV. See Instructions for Authors for a complete description of levels of evidence.
Topics: Aged; Bone Plates; Elbow Joint; Fracture Fixation, Internal; Humans; Joint Instability; Olecranon Process; Treatment Outcome; Ulna Fractures
PubMed: 32569071
DOI: 10.1097/BOT.0000000000001865 -
Nursing Open Mar 2023To summarize the use of machine learning (ML) for hospital-acquired pressure injury (HAPI) prediction and to systematically assess the performance and construction... (Review)
Review
AIMS AND OBJECTIVES
To summarize the use of machine learning (ML) for hospital-acquired pressure injury (HAPI) prediction and to systematically assess the performance and construction process of ML models to provide references for establishing high-quality ML predictive models.
BACKGROUND
As an adverse event, HAPI seriously affects patient prognosis and quality of life, and causes unnecessary medical investment. At present, the performance of various scales used to predict HAPIs is still unsatisfactory. As a new statistical tool, ML has been applied to predict HAPIs. However, its performance has varied in different studies; moreover, some deficiencies in the model construction process were observed in each study.
DESIGN
Systematic review.
METHODS
Relevant articles published between 2010-2021 were identified in the PubMed, Web of Science, Scopus, Embase and CINHAL databases. Study selection was performed in accordance with the preferred reporting items for systematic reviews and meta-analysis guidelines. The quality of the included articles was assessed using the prediction model risk of bias assessment tool.
RESULTS
Twenty-three studies out of 1793 articles were considered in this systematic review. The sample size of each study ranged from 149-75353; the prevalence of pressure injuries ranged from 0.5%-49.8%. ML showed good performance for HAPI prediction. However, some deficiencies were observed in terms of data management, data pre-processing and model validation.
CONCLUSIONS
ML, as a powerful decision-making assistance tool, is helpful for the prediction of HAPIs. However, existing studies have been insufficient in terms of data management, data pre-processing and model validation. Future studies should address these issues to establish ML models for HAPI prediction that can be widely used in clinical practice.
RELEVANCE TO CLINICAL PRACTICE
This review highlights that ML is helpful in predicting HAPI; however, in the process of data management, data pre-processing and model validation, some deficiencies still need to be addressed. The ultimate goal of integrating ML into HAPI prediction is to develop a practical clinical decision-making tool. A complete and rigorous model construction process should be followed in future studies to develop high-quality ML models that can be applied in clinical practice.
Topics: Humans; Pressure Ulcer; Quality of Life; Prognosis; Machine Learning; Hospitals
PubMed: 36310417
DOI: 10.1002/nop2.1429 -
JMIR Human Factors Mar 2023Technological advancements have opened the path for many technology providers to easily develop and introduce eHealth tools to the public. The use of these tools is... (Review)
Review
BACKGROUND
Technological advancements have opened the path for many technology providers to easily develop and introduce eHealth tools to the public. The use of these tools is increasingly recognized as a critical quality driver in health care; however, choosing a quality tool from the myriad of tools available for a specific health need does not come without challenges.
OBJECTIVE
This review aimed to systematically investigate the literature to understand the different approaches and criteria used to assess the quality and impact of eHealth tools by considering sociotechnical factors (from technical, social, and organizational perspectives).
METHODS
A structured search was completed following the participants, intervention, comparators, and outcomes framework. We searched the PubMed, Cochrane, Web of Science, Scopus, and ProQuest databases for studies published between January 2012 and January 2022 in English, which yielded 675 results, of which 40 (5.9%) studies met the inclusion criteria. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and the Cochrane Handbook for Systematic Reviews of Interventions were followed to ensure a systematic process. Extracted data were analyzed using NVivo (QSR International), with a thematic analysis and narrative synthesis of emergent themes.
RESULTS
Similar measures from the different papers, frameworks, and initiatives were aggregated into 36 unique criteria grouped into 13 clusters. Using the sociotechnical approach, we classified the relevant criteria into technical, social, and organizational assessment criteria. Technical assessment criteria were grouped into 5 clusters: technical aspects, functionality, content, data management, and design. Social assessment criteria were grouped into 4 clusters: human centricity, health outcomes, visible popularity metrics, and social aspects. Organizational assessment criteria were grouped into 4 clusters: sustainability and scalability, health care organization, health care context, and developer.
CONCLUSIONS
This review builds on the growing body of research that investigates the criteria used to assess the quality and impact of eHealth tools and highlights the complexity and challenges facing these initiatives. It demonstrates that there is no single framework that is used uniformly to assess the quality and impact of eHealth tools. It also highlights the need for a more comprehensive approach that balances the social, organizational, and technical assessment criteria in a way that reflects the complexity and interdependence of the health care ecosystem and is aligned with the factors affecting users' adoption to ensure uptake and adherence in the long term.
PubMed: 36843321
DOI: 10.2196/45143 -
Journal of Medical Internet Research Nov 2021Bipolar disorder (BD) is the 10th most common cause of frailty in young individuals and has triggered morbidity and mortality worldwide. Patients with BD have a life... (Review)
Review
BACKGROUND
Bipolar disorder (BD) is the 10th most common cause of frailty in young individuals and has triggered morbidity and mortality worldwide. Patients with BD have a life expectancy 9 to 17 years lower than that of normal people. BD is a predominant mental disorder, but it can be misdiagnosed as depressive disorder, which leads to difficulties in treating affected patients. Approximately 60% of patients with BD are treated for depression. However, machine learning provides advanced skills and techniques for better diagnosis of BD.
OBJECTIVE
This review aims to explore the machine learning algorithms used for the detection and diagnosis of bipolar disorder and its subtypes.
METHODS
The study protocol adopted the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. We explored 3 databases, namely Google Scholar, ScienceDirect, and PubMed. To enhance the search, we performed backward screening of all the references of the included studies. Based on the predefined selection criteria, 2 levels of screening were performed: title and abstract review, and full review of the articles that met the inclusion criteria. Data extraction was performed independently by all investigators. To synthesize the extracted data, a narrative synthesis approach was followed.
RESULTS
We retrieved 573 potential articles were from the 3 databases. After preprocessing and screening, only 33 articles that met our inclusion criteria were identified. The most commonly used data belonged to the clinical category (19, 58%). We identified different machine learning models used in the selected studies, including classification models (18, 55%), regression models (5, 16%), model-based clustering methods (2, 6%), natural language processing (1, 3%), clustering algorithms (1, 3%), and deep learning-based models (3, 9%). Magnetic resonance imaging data were most commonly used for classifying bipolar patients compared to other groups (11, 34%), whereas microarray expression data sets and genomic data were the least commonly used. The maximum ratio of accuracy was 98%, whereas the minimum accuracy range was 64%.
CONCLUSIONS
This scoping review provides an overview of recent studies based on machine learning models used to diagnose patients with BD regardless of their demographics or if they were compared to patients with psychiatric diagnoses. Further research can be conducted to provide clinical decision support in the health industry.
Topics: Algorithms; Bipolar Disorder; Data Management; Humans; Machine Learning; Natural Language Processing
PubMed: 34806996
DOI: 10.2196/29749