Social media analytics can considerably contribute to understanding health conditions beyond clinical practice, by capturing patients’ discussions and feelings about their quality of life in relation to disease treatments. In this article, we propose a methodology to support a detailed analysis of the therapeutic experience in patients affected by a specific disease, as it emerges from health forums. As a use case to test the proposed methodology, we analyze the experience of patients affected by hypothyroidism and their reactions to standard therapies. Our approach is based on a data extraction and filtering pipeline, a novel topic detection model named
Generative Text Compression with Agglomerative Clustering Summarization
), and an in-depth data analytic process. We advance the state of the art on automated detection of
adverse drug reactions
) since, rather than simply detecting and classifying positive or negative reactions to a therapy, we are capable of providing a fine characterization of patients along different dimensions, such as co-morbidities, symptoms, and emotional states.
With the availability of reliable and low-cost DNA sequencing, human genomics is relevant to a growing number of end-users, including biologists and clinicians. Typical interactions require applying comparative data analysis to huge repositories of genomic information for building new knowledge, taking advantage of the latest findings in applied genomics for healthcare. Powerful technology for data extraction and analysis is available, but broad use of the technology is hampered by the complexity of accessing such methods and tools.
This work presents GeCoAgent, a big-data service for clinicians and biologists. GeCoAgent uses a dialogic interface, animated by a chatbot, for supporting the end-users’ interaction with computational tools accompanied by multi-modal support. While the dialogue progresses, the user is accompanied in extracting the relevant data from repositories and then performing data analysis, which often requires the use of statistical methods or machine learning. Results are returned using simple representations (spreadsheets and graphics), while at the end of a session the dialogue is summarized in textual format. The innovation presented in this article is concerned with not only the delivery of a new tool but also our novel approach to conversational technologies, potentially extensible to other healthcare domains or to general data science.
Community science image libraries offer a massive, but largely untapped, source of observational data for phenological research. The iNaturalist platform offers a particularly rich archive, containing more than 49 million verifiable, georeferenced, open access images, encompassing seven continents and over 278,000 species. A critical limitation preventing scientists from taking full advantage of this rich data source is labor. Each image must be manually inspected and categorized by phenophase, which is both time-intensive and costly. Consequently, researchers may only be able to use a subset of the total number of images available in the database. While iNaturalist has the potential to yield enough data for high-resolution and spatially extensive studies, it requires more efficient tools for phenological data extraction. A promising solution is automation of the image annotation process using deep learning. Recent innovations in deep learning have made these open-source tools accessible to a general research audience. However, it is unknown whether deep learning tools can accurately and efficiently annotate phenophases in community science images. Here, we train a convolutional neural network (CNN) to annotate images of Alliaria petiolata into distinct phenophases from iNaturalist and compare the performance of the model with non-expert human annotators. We demonstrate that researchers can successfully employ deep learning techniques to extract phenological information from community science images. A CNN classified two-stage phenology (flowering and non-flowering) with 95.9% accuracy and classified four-stage phenology (vegetative, budding, flowering, and fruiting) with 86.4% accuracy. The overall accuracy of the CNN did not differ from humans (p = 0.383), although performance varied across phenophases. We found that a primary challenge of using deep learning for image annotation was not related to the model itself, but instead in the quality of the community science images. Up to 4% of A. petiolata images in iNaturalist were taken from an improper distance, were physically manipulated, or were digitally altered, which limited both human and machine annotators in accurately classifying phenology. Thus, we provide a list of photography guidelines that could be included in community science platforms to inform community scientists in the best practices for creating images that facilitate phenological analysis.
BackgroundSarcoidosis-associated pulmonary hypertension (SAPH) is associated with poor prognosis, conferring up to a 10-fold increase in mortality in patients with sarcoidosis, but the actual prevalence of SAPH is unknown.MethodsThe PubMed, Embase, and Cochrane Library databases were systematically searched for epidemiological studies reporting the prevalence of SAPH up to July 2021. Two reviewers independently performed the study selection, data extraction, and quality assessment. Studies were pooled using random-effects meta-analysis.ResultsThis meta-analysis included 25 high-quality studies from 12 countries, with a pooled sample of 632,368 patients with sarcoidosis. The prevalence of SAPH by transthoracic echocardiography in Europe, the United States and Asia was 18.8% [95% confidence interval (CI): 11.1–26.5%], 13.9% (95% CI: 5.4–22.4%) and 16.2% (95% CI: 7.1–25.4%) separately, and the overall pooled prevalence was 16.4% (95%CI: 12.2–20.5%). By right heart catheterization (RHC), the pooled prevalence of SAPH was 6.4% (95% CI: 3.6–9.1%) in general sarcoidosis population, and subgroup analyses showed that the prevalence of SAPH was 6.7% (95% CI: 2.4–11.0%) in Europe and 8.6% (95% CI: −4.1 to 21.3%) in the United States. Further, the prevalence of pre-capillary PH was 6.5% (95% CI: 2.9–10.2%). For the population with advanced sarcoidosis, the pooled prevalence of SAPH and pre-capillary PH by RHC was as high as 62.3% (95% CI: 46.9–77.6%) and 55.9% (95% CI: 20.1–91.7%), respectively. Finally, the pooled prevalence of SAPH in large databases with documented diagnoses (6.1%, 95% CI: 2.6–9.5%) was similar to that of RHC. Substantial heterogeneity across studies was observed for all analyses (I2 > 80%, P < 0.001).ConclusionsThe sarcoidosis population has a relatively low burden of PH, mainly pre-capillary PH. However, as the disease progresses to advanced sarcoidosis, the prevalence of SAPH increases significantly.
Systematic review is an indispensable tool for optimal evidence collection and evaluation in evidence-based medicine. However, the explosive increase of the original literatures makes it difficult to accomplish critical appraisal and regular update. Artificial intelligence (AI) algorithms have been applied to automate the literature screening procedure in medical systematic reviews. In these studies, different algorithms were used and results with great variance were reported. It is therefore imperative to systematically review and analyse the developed automatic methods for literature screening and their effectiveness reported in current studies.
An electronic search will be conducted using PubMed, Embase, ACM Digital Library, and IEEE Xplore Digital Library databases, as well as literatures found through supplementary search in Google scholar, on automatic methods for literature screening in systematic reviews. Two reviewers will independently conduct the primary screening of the articles and data extraction, in which nonconformities will be solved by discussion with a methodologist. Data will be extracted from eligible studies, including the basic characteristics of study, the information of training set and validation set, and the function and performance of AI algorithms, and summarised in a table. The risk of bias and applicability of the eligible studies will be assessed by the two reviewers independently based on Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). Quantitative analyses, if appropriate, will also be performed.
Automating systematic review process is of great help in reducing workload in evidence-based practice. Results from this systematic review will provide essential summary of the current development of AI algorithms for automatic literature screening in medical evidence synthesis and help to inspire further studies in this field.
Systematic review registration
PROSPERO CRD42020170815 (28 April 2020).
Objective:A growing body of evidence supports the effectiveness of body-oriented interventions (BOI) in educational contexts, showing positive influences on social-emotional competence. Nevertheless, there is a lack of systematization of the evidence regarding preschool years. This is a two-part systematic review. In this first part, we aim to examine the effects of BOI on preschoolers' social-emotional competence outcomes.Data Sources:Searches were conducted in Pubmed, Scopus, PsycInfo, ERIC, Web of Science, Portal Regional da BVS and CINAHL.Eligibility Criteria:English, French and Portuguese language articles published between January 2000 and October 2020, that evaluated the effects of BOI implemented in educational contexts on social-emotional competence of preschool children. Only randomized controlled trials (RCT) or quasi-RCT were included.Data Extraction and Synthesis:Two reviewers independently completed data extraction and risk-of-bias assessment. The level of scientific evidence was measured through the Best Evidence Synthesis.Results:Nineteen studies were included. There was strong evidence that BOI do not improve anger/aggression, delay of gratification and altruism. Nevertheless, there was moderate evidence that BOI effectively improve other social-emotional outcomes, such as empathy, social interaction, social independence, general internalizing behaviors, and general externalizing behaviors. The lack of scientific evidence was compromised by the methodological quality of the studies.Conclusion:BOI effectively improve specific social-emotional competences of preschool children.Systematic Review Registration:PROSPERO, identifier CRD42020172248.
Animal-assisted therapy with dogs is regularly used in children with behavioural and developmental disorders. Aims of this systematic review were threefold: to analyse the methodological quality of studies on dog-assisted therapy (DAT) for children with behavioural and developmental disorders, to determine to which extent the studies on DAT adhere to the quality criteria developed by the International Association of Human Animal Interaction Organisation (IAHAIO) and to describe the characteristics of the participants, the intervention and the outcomes.
Three databases (i.e. PsycInfo, MedLine and Eric) were searched, and 14 studies on DAT were included. The Joanna Briggs Institute checklist (JBIC) and the quality criteria developed by the IAHAIO were used during data extraction. Characteristics of the participants, the intervention, the therapy dogs and the outcomes of the studies were summarised.
Six of the 14 included studies reported significant outcomes of DAT, whereof six in the social domain and two in the psychological domain. However, scores on the JBIC indicated low to moderate methodological quality and only three of the included studies adhered to the IAHAIO quality criteria.
DAT is a promising intervention for children with behavioural and developmental disorders, especially for children with autism spectrum disorder. A clear description of the therapy’s components, the role of the therapy dog and analysis of the treatment integrity and procedural fidelity would improve the methodological quality of the studies and the field of dog-assisted interventions.
BackgroundIn an attempt to aggregate observations from clinical trials, several meta-analyses have been published examining the effectiveness of systemic, non-opioid, pharmacological interventions to reduce the incidence of chronic postsurgical pain.ObjectiveTo inform the design and reporting of future studies, the purpose of our study was to examine the quality of these meta-analyses.Evidence reviewWe conducted an electronic literature search in Embase, MEDLINE, and the Cochrane Database of Systematic Reviews. Published meta-analyses, from the years 2010 to 2020, examining the effect of perioperative, systemic, non-opioid pharmacological treatments on the incidence of chronic postsurgical pain in adult patients were identified. Data extraction focused on methodological details. Meta-analysis quality was assessed using the A Measurement Tool to Assess Systematic Reviews 2 (AMSTAR 2) critical appraisal tool.FindingsOur search yielded 17 published studies conducting 58 meta-analyses for gabapentinoids (gabapentin and pregabalin), ketamine, lidocaine, non-steroidal anti-inflammatory drugs, and mexiletine. According to AMSTAR 2, 88.2% of studies (or 15/17) were low or critically low in quality. The most common critical element missing was an analysis of publication bias. Trends indicated an improvement in quality over time and association with journal impact factor.ConclusionsWith few individual trials adequately powered to detect treatment effects, meta-analyses play a crucial role in informing the perioperative management of chronic postsurgical pain. In light of this inherent value and despite a number of attempts, high-quality meta-analyses are still needed.PROSPERO registration numberCRD42021230941.
Introduction: Psychotropic medications are commonly prescribed among adults with intellectual disability (ID), often in the absence of a psychiatric diagnosis. As such, there is great disparity between the estimated prevalence of mental illness and the rates of psychotropic medication use amongst people with ID. ‘Off-label’ use of these medications may account for much of this discrepancy, in particular their use in the management of challenging behaviour. This has come under scrutiny due to the myriad of side effects and the deficiency of high-quality data supporting their use for this indication. Understanding the causes and justifications for such disparity is essential in discerning the efficacy of current prescription practice. Objective: To explore the existing evidence base regarding the prescription and management of psychotropic medications in adults with ID. The aim will be achieved through identifying the psychotropic medications commonly prescribed, the underlying rationale(s) for their prescription and the evidence available that demonstrates their appropriateness and effectiveness. Additionally, the paper will seek to evaluate the availability of any existing guidance that informs the management of these medications, and the evidence and outcomes of psychotropic medication dose reduction and/or cessation interventions. Inclusion criteria: This review will consider studies that focus on the use of psychotropic medications amongst patients with ID. Methods: Research studies (qualitative, quantitative and mixed design) and Grey Literature (English) will be included. The search will be conducted without time restrictions. Databases will include: Ovid MEDLINE, Embase, CINAHL, JBI Evidence Synthesis, Cochrane Central Register of Controlled Trials, Cochrane Databased of Systematic Reviews, PsycINFO and Scopus. A three-step search strategy will be followed, with results screened by two independent reviewers. Data will be extracted independently by two reviewers using a data extraction tool with results mapped and presented using a narrative form supported by tables and diagrams.
The main indicators of higher education (HE) internationalization in the field of awarding degrees are the international development of disciplines and interdisciplinary sciences, new educational and learning methods, new and updated curricula, and their correct ways of sharing. This study aimed to examine the system of awarding degrees in health HE of Iran and the United Kingdom. This descriptive-comparative study focused on the field of medical sciences and its related disciplines. The vital information about the variables was collected by visiting the official websites of the UK universities and related or joint organizations. The related information to the Kerman University of Medical Sciences as a sample of Iran medical universities was obtained from the university’s Farabar system. All data extraction steps were performed manually. There were differences in the mechanism of setting up a new discipline and the process of students’ admission, diversity of degrees’ titles and curriculums, stability of disciplines over the time, creativity in creating competition between different disciplines, the reason for establishing a discipline and the requirements for certification and awarding of degrees in health sciences disciplines in Iran and the United Kingdom were described. Propelling of medical education in the health sciences area towards standard awarding degree systems can be responsible for the requirements of internationalization of higher education.