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JMIR Medical Informatics Jan 2020Sentiment analysis (SA) is a subfield of natural language processing whose aim is to automatically classify the sentiment expressed in a free text. It has found... (Review)
Review
BACKGROUND
Sentiment analysis (SA) is a subfield of natural language processing whose aim is to automatically classify the sentiment expressed in a free text. It has found practical applications across a wide range of societal contexts including marketing, economy, and politics. This review focuses specifically on applications related to health, which is defined as "a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity."
OBJECTIVE
This study aimed to establish the state of the art in SA related to health and well-being by conducting a systematic review of the recent literature. To capture the perspective of those individuals whose health and well-being are affected, we focused specifically on spontaneously generated content and not necessarily that of health care professionals.
METHODS
Our methodology is based on the guidelines for performing systematic reviews. In January 2019, we used PubMed, a multifaceted interface, to perform a literature search against MEDLINE. We identified a total of 86 relevant studies and extracted data about the datasets analyzed, discourse topics, data creators, downstream applications, algorithms used, and their evaluation.
RESULTS
The majority of data were collected from social networking and Web-based retailing platforms. The primary purpose of online conversations is to exchange information and provide social support online. These communities tend to form around health conditions with high severity and chronicity rates. Different treatments and services discussed include medications, vaccination, surgery, orthodontic services, individual physicians, and health care services in general. We identified 5 roles with respect to health and well-being among the authors of the types of spontaneously generated narratives considered in this review: a sufferer, an addict, a patient, a carer, and a suicide victim. Out of 86 studies considered, only 4 reported the demographic characteristics. A wide range of methods were used to perform SA. Most common choices included support vector machines, naïve Bayesian learning, decision trees, logistic regression, and adaptive boosting. In contrast with general trends in SA research, only 1 study used deep learning. The performance lags behind the state of the art achieved in other domains when measured by F-score, which was found to be below 60% on average. In the context of SA, the domain of health and well-being was found to be resource poor: few domain-specific corpora and lexica are shared publicly for research purposes.
CONCLUSIONS
SA results in the area of health and well-being lag behind those in other domains. It is yet unclear if this is because of the intrinsic differences between the domains and their respective sublanguages, the size of training datasets, the lack of domain-specific sentiment lexica, or the choice of algorithms.
PubMed: 32012057
DOI: 10.2196/16023 -
World Neurosurgery Aug 2022While clinical guidelines provide a framework for hospital management of spontaneous intracerebral hemorrhage (ICH), variation in the resource use and costs of these... (Review)
Review
BACKGROUND
While clinical guidelines provide a framework for hospital management of spontaneous intracerebral hemorrhage (ICH), variation in the resource use and costs of these services exists. We sought to perform a systematic literature review to assess the evidence on hospital resource use and costs associated with management of adult patients with ICH, as well as identify factors that impact variation in such hospital resource use and costs, regarding clinical characteristics and delivery of services.
METHODS
A systematic literature review was performed using PubMed, Cochrane Central Register of Controlled Trials, and Ovid MEDLINE(R) 1946 to present. Articles were assessed against inclusion and exclusion criteria. Study design, ICH sample size, population, setting, objective, hospital characteristics, hospital resource use and cost data, and main study findings were abstracted.
RESULTS
In total, 43 studies met the inclusion criteria. Pertinent clinical characteristics that increased hospital resource use included presence of comorbidities and baseline ICH severity. Aspects of service delivery that greatly impacted hospital resource consumption included intensive care unit length of stay and performance of surgical procedures and intensive care procedures.
CONCLUSIONS
Hospital resource use and costs for patients with ICH were high and differed widely across studies. Making concrete conclusions on hospital resources and costs for ICH care was constrained, given methodologic and patient variation in the studies. Future research should evaluate the long-term cost-effectiveness of ICH treatment interventions and use specific economic evaluation guidelines and common data elements to mitigate study variation.
Topics: Adult; Cerebral Hemorrhage; Cost-Benefit Analysis; Hospitals; Humans; Intensive Care Units
PubMed: 35489599
DOI: 10.1016/j.wneu.2022.04.055 -
Physical and Engineering Sciences in... Mar 2022To conduct a systematic survey of published techniques for automated diagnosis and prognosis of COVID-19 diseases using medical imaging, assessing the validity of... (Review)
Review
OBJECTIVES
To conduct a systematic survey of published techniques for automated diagnosis and prognosis of COVID-19 diseases using medical imaging, assessing the validity of reported performance and investigating the proposed clinical use-case. To conduct a scoping review into the authors publishing such work.
METHODS
The Scopus database was queried and studies were screened for article type, and minimum source normalized impact per paper and citations, before manual relevance assessment and a bias assessment derived from a subset of the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). The number of failures of the full CLAIM was adopted as a surrogate for risk-of-bias. Methodological and performance measurements were collected from each technique. Each study was assessed by one author. Comparisons were evaluated for significance with a two-sided independent t-test.
FINDINGS
Of 1002 studies identified, 390 remained after screening and 81 after relevance and bias exclusion. The ratio of exclusion for bias was 71%, indicative of a high level of bias in the field. The mean number of CLAIM failures per study was 8.3 ± 3.9 [1,17] (mean ± standard deviation [min,max]). 58% of methods performed diagnosis versus 31% prognosis. Of the diagnostic methods, 38% differentiated COVID-19 from healthy controls. For diagnostic techniques, area under the receiver operating curve (AUC) = 0.924 ± 0.074 [0.810,0.991] and accuracy = 91.7% ± 6.4 [79.0,99.0]. For prognostic techniques, AUC = 0.836 ± 0.126 [0.605,0.980] and accuracy = 78.4% ± 9.4 [62.5,98.0]. CLAIM failures did not correlate with performance, providing confidence that the highest results were not driven by biased papers. Deep learning techniques reported higher AUC (p < 0.05) and accuracy (p < 0.05), but no difference in CLAIM failures was identified.
INTERPRETATION
A majority of papers focus on the less clinically impactful diagnosis task, contrasted with prognosis, with a significant portion performing a clinically unnecessary task of differentiating COVID-19 from healthy. Authors should consider the clinical scenario in which their work would be deployed when developing techniques. Nevertheless, studies report superb performance in a potentially impactful application. Future work is warranted in translating techniques into clinical tools.
Topics: Artificial Intelligence; COVID-19; COVID-19 Testing; Humans; Publishing; Radiography; SARS-CoV-2
PubMed: 34919204
DOI: 10.1007/s13246-021-01093-0 -
Translational Vision Science &... Jul 2023The purpose of this study was to perform a systematic review and meta-analysis to synthesize evidence from studies using deep learning (DL) to predict cardiovascular... (Meta-Analysis)
Meta-Analysis
PURPOSE
The purpose of this study was to perform a systematic review and meta-analysis to synthesize evidence from studies using deep learning (DL) to predict cardiovascular disease (CVD) risk from retinal images.
METHODS
A systematic literature search was performed in MEDLINE, Scopus, and Web of Science up to June 2022. We extracted data pertaining to predicted outcomes, model development, and validation and model performance metrics. Included studies were graded using the Quality Assessment of Diagnostic Accuracies Studies 2 tool. Model performance was pooled across eligible studies using a random-effects meta-analysis model.
RESULTS
A total of 26 studies were included in the analysis. There were 42 CVD risk-related outcomes predicted from retinal images were identified, including 33 CVD risk factors, 4 cardiac imaging biomarkers, 2 CVD risk scores, the presence of CVD, and incident CVD. Three studies that aimed to predict the development of future CVD events reported an area under the receiver operating curve (AUROC) between 0.68 and 0.81. Models that used retinal images as input data had a pooled mean absolute error of 3.19 years (95% confidence interval [CI] = 2.95-3.43) for age prediction; a pooled AUROC of 0.96 (95% CI = 0.95-0.97) for gender classification; a pooled AUROC of 0.80 (95% CI = 0.73-0.86) for diabetes detection; and a pooled AUROC of 0.86 (95% CI = 0.81-0.92) for the detection of chronic kidney disease. We observed a high level of heterogeneity and variation in study designs.
CONCLUSIONS
Although DL models appear to have reasonably good performance when it comes to predicting CVD risk, further work is necessary to evaluate the real-world applicability and predictive accuracy.
TRANSLATIONAL RELEVANCE
DL-based CVD risk assessment from retinal images holds great promise to be translated to clinical practice as a novel approach for CVD risk assessment, given its simple, quick, and noninvasive nature.
Topics: Humans; Cardiovascular Diseases; Deep Learning
PubMed: 37440249
DOI: 10.1167/tvst.12.7.14 -
BMC Medical Informatics and Decision... Jun 2020Early warning scores (EWS) have been developed as clinical prognostication tools to identify acutely deteriorating patients. In the past few years, there has been a...
BACKGROUND
Early warning scores (EWS) have been developed as clinical prognostication tools to identify acutely deteriorating patients. In the past few years, there has been a proliferation of studies that describe the development and validation of novel machine learning-based EWS. Systematic reviews of published studies which focus on evaluating performance of both well-established and novel EWS have shown conflicting conclusions. A possible reason is the heterogeneity in validation methods applied. In this review, we aim to examine the methodologies and metrics used in studies which perform EWS validation.
METHODS
A systematic review of all eligible studies from the MEDLINE database and other sources, was performed. Studies were eligible if they performed validation on at least one EWS and reported associations between EWS scores and inpatient mortality, intensive care unit (ICU) transfers, or cardiac arrest (CA) of adults. Two reviewers independently did a full-text review and performed data abstraction by using standardized data-worksheet based on the TRIPOD (Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) checklist. Meta-analysis was not performed due to heterogeneity.
RESULTS
The key differences in validation methodologies identified were (1) validation dataset used, (2) outcomes of interest, (3) case definition, time of EWS use and aggregation methods, and (4) handling of missing values. In terms of case definition, among the 48 eligible studies, 34 used the patient episode case definition while 12 used the observation set case definition, and 2 did the validation using both case definitions. Of those that used the patient episode case definition, 18 studies validated the EWS at a single point of time, mostly using the first recorded observation. The review also found more than 10 different performance metrics reported among the studies.
CONCLUSIONS
Methodologies and performance metrics used in studies performing validation on EWS were heterogeneous hence making it difficult to interpret and compare EWS performance. Standardizing EWS validation methodology and reporting can potentially address this issue.
Topics: Adult; Benchmarking; Early Warning Score; Heart Arrest; Humans; Intensive Care Units; Prognosis; Prospective Studies
PubMed: 32552702
DOI: 10.1186/s12911-020-01144-8 -
Diabetes Care Feb 2024Diabetic macular edema (DME) is the leading cause of vision loss in people with diabetes. Application of artificial intelligence (AI) in interpreting fundus photography... (Meta-Analysis)
Meta-Analysis Review
Performance of Artificial Intelligence in Detecting Diabetic Macular Edema From Fundus Photography and Optical Coherence Tomography Images: A Systematic Review and Meta-analysis.
BACKGROUND
Diabetic macular edema (DME) is the leading cause of vision loss in people with diabetes. Application of artificial intelligence (AI) in interpreting fundus photography (FP) and optical coherence tomography (OCT) images allows prompt detection and intervention.
PURPOSE
To evaluate the performance of AI in detecting DME from FP or OCT images and identify potential factors affecting model performances.
DATA SOURCES
We searched seven electronic libraries up to 12 February 2023.
STUDY SELECTION
We included studies using AI to detect DME from FP or OCT images.
DATA EXTRACTION
We extracted study characteristics and performance parameters.
DATA SYNTHESIS
Fifty-three studies were included in the meta-analysis. FP-based algorithms of 25 studies yielded pooled area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity of 0.964, 92.6%, and 91.1%, respectively. OCT-based algorithms of 28 studies yielded pooled AUROC, sensitivity, and specificity of 0.985, 95.9%, and 97.9%, respectively. Potential factors improving model performance included deep learning techniques, larger size, and more diversity in training data sets. Models demonstrated better performance when validated internally than externally, and those trained with multiple data sets showed better results upon external validation.
LIMITATIONS
Analyses were limited by unstandardized algorithm outcomes and insufficient data in patient demographics, OCT volumetric scans, and external validation.
CONCLUSIONS
This meta-analysis demonstrates satisfactory performance of AI in detecting DME from FP or OCT images. External validation is warranted for future studies to evaluate model generalizability. Further investigations may estimate optimal sample size, effect of class balance, patient demographics, and additional benefits of OCT volumetric scans.
Topics: Humans; Diabetic Retinopathy; Macular Edema; Artificial Intelligence; Tomography, Optical Coherence; Photography; Diabetes Mellitus
PubMed: 38241500
DOI: 10.2337/dc23-0993 -
EFSA Journal. European Food Safety... May 2022Climate change is a phenomenon that includes different dramatic events that deeply affect the physiology of animal species both directly and indirectly with...
Climate change is a phenomenon that includes different dramatic events that deeply affect the physiology of animal species both directly and indirectly with qualitative-quantitative impacts on livestock performances and health. The implications of climate change on animal welfare and on production demand are complex and call for a multidisciplinary approach which involved both animal science and economic sciences. The current technical report will describe the activities performed by the fellow while placed at the University of Foggia, Department of Agriculture, Natural Resources and Engineering, in Italy. Furthermore, the work programme offered by the hosting site consisted in performing a systematic literature review, following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) Statement, and a quantitative synthesis of the literature on the impact of climate change events (e.g. heat stress) on livestock welfare and productivity and the effect of heat relieving strategies on the animals' performance.
PubMed: 35634557
DOI: 10.2903/j.efsa.2022.e200413 -
Sports (Basel, Switzerland) May 2020The purpose of this article was to review the data on the relationship between multi-joint isometric strength test (IsoTest) force-time characteristics (peak force, rate... (Review)
Review
The purpose of this article was to review the data on the relationship between multi-joint isometric strength test (IsoTest) force-time characteristics (peak force, rate of force development and impulse) and dynamic performance that is available in the current literature. Four electronic databases were searched using search terms related to IsoTest. Studies were considered eligible if they were original research studies that investigated the relationships between multi-joint IsoTest and performance of dynamic movements; published in peer-reviewed journals; had participants who were athletes or active individuals who participate in recreational sports or resistance training, with no restriction on sex; and had full text available. A total of 47 studies were selected. These studies showed significant small to large correlations between isometric bench press (IBP) force-time variables and upper body dynamic performances ( = 0.221 to 0.608, < 0.05) and significant small to very large correlation between isometric squat (ISqT) ( = 0.085 to 0.746, < 0.05) and isometric mid-thigh pull (IMTP) ( = 0.120 to 0.941, < 0.05) force-time variables with lower body dynamic performances. IsoTest force-time characteristics were shown to have small to very large correlations with dynamic performances of the upper and lower limbs as well as performance of sporting movements ( = 0.118 to 0.700, < 0.05). These data suggest that IsoTest force-time characteristics provide insights into the force production capability of athletes which give insight into dynamic performance capabilities.
PubMed: 32429176
DOI: 10.3390/sports8050063 -
The American Journal of Emergency... Feb 2023Spurred by the Coronavirus infectious disease 2019 pandemic, aerosol containment devices (ACDs) were developed to capture infectious respiratory aerosols generated by... (Review)
Review
BACKGROUND
Spurred by the Coronavirus infectious disease 2019 pandemic, aerosol containment devices (ACDs) were developed to capture infectious respiratory aerosols generated by patients at their source. Prior reviews indicated that such devices had low evidence of effectiveness, but did not address how ACDs should be evaluated, how well they should perform, nor have clearly defined performance standards. Towards developing design criteria for ACDs, two questions were posed: 1) What characteristics have guided the design of ACDs? 2) How have these characteristics been evaluated?
METHODS
A scoping review was performed consistent with PRISMA guidelines. Data were extracted with respect to general study information, intended use of the device, device design characteristics and evaluation.
RESULTS
Fifty-four articles were included. Evaluation was most commonly performed with respect to device aerosol containment (n = 31, 61%), with only 5 (9%), 3 (6%) and 8 (15%) formally assessing providing experience, patient experience and procedure impact, respectively. Nearly all of the studies that explored provider experience and procedure impact studied intubation. Few studies provided a priori performance criteria for any evaluation metric, or referenced any external guidelines by which to bench mark performance.
CONCLUSION
With respect to aerosol containment, ACDs should reduce exposure among HCP with the device compared with the absence of the device, and provide ≥90% reduction in respirable aerosols, equivalent in performance to N95 filtering facepiece respirators, if the goal is to reduce reliance on personal protective equipment. The ACD should not increase awkward or uncomfortable postures, or adversely impact biomechanics of the procedure itself as this could have implications for procedure outcomes. A variety of standardized instruments exist to assess the experience of patients and healthcare personnel. Integration of ACDs into routine clinical practice requires rigorous studies of aerosol containment and the user experience.
Topics: Humans; Respiratory Aerosols and Droplets; COVID-19; Personal Protective Equipment; Intubation, Intratracheal; Equipment Design
PubMed: 36435005
DOI: 10.1016/j.ajem.2022.11.007 -
Journal of Strength and Conditioning... Dec 2019Ehlert, A and Wilson, PB. A systematic review of golf warm-ups: behaviors, injury, and performance. J Strength Cond Res 33(12): 3444-3462, 2019-Previous literature has...
Ehlert, A and Wilson, PB. A systematic review of golf warm-ups: behaviors, injury, and performance. J Strength Cond Res 33(12): 3444-3462, 2019-Previous literature has demonstrated that warm-ups have the potential to increase physical performance and reduce risk of injury. Warm-ups before golf may have a similar result, but a systematic evaluation of their effects in golf is currently lacking. Three electronic databases (PubMed, SPORTDiscus, and Web of Science) were systematically searched to address 3 primary research questions: (a) What are the current warm-up behaviors of golfers?; (b) Is there an association between warm-up behaviors and golf-related injury?; and (c) What are the effects of various warm-up protocols on measures of golf performance? Twenty-three studies (9 observational and 14 experimental) were identified that included data on warm-ups before golf participation. Overall, the current data suggest that many golfers either do not warm-up regularly or perform a warm-up that is short in duration. Studies on the association between warm-up behaviors and golf-related injury were mixed and inconclusive. Experimental studies suggest that a variety of warm-up methods may be beneficial for golf performance. Specifically, dynamic warm-ups and those with resistance exercise tended to enhance measures of performance, whereas static stretching was inferior to other methods and potentially detrimental to performance. Overall, the results of this systematic review suggest that various warm-up protocols (with the exception of static stretching) may enhance golf performance, but observational data suggest many golfers do not regularly perform them. More data are needed on the warm-up behaviors of competitive golfers, the impact of warm-up behaviors on golf-related injury, and to further identify effective warm-up methods for enhancing golf performance.
Topics: Athletic Performance; Behavior; Golf; Humans; Male; Muscle Stretching Exercises; Warm-Up Exercise
PubMed: 31469762
DOI: 10.1519/JSC.0000000000003329