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JMIR Medical Education Jun 2020The use of artificial intelligence (AI) in medicine will generate numerous application possibilities to improve patient care, provide real-time data analytics, and... (Review)
Review
BACKGROUND
The use of artificial intelligence (AI) in medicine will generate numerous application possibilities to improve patient care, provide real-time data analytics, and enable continuous patient monitoring. Clinicians and health informaticians should become familiar with machine learning and deep learning. Additionally, they should have a strong background in data analytics and data visualization to use, evaluate, and develop AI applications in clinical practice.
OBJECTIVE
The main objective of this study was to evaluate the current state of AI training and the use of AI tools to enhance the learning experience.
METHODS
A comprehensive systematic review was conducted to analyze the use of AI in medical and health informatics education, and to evaluate existing AI training practices. PRISMA-P (Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols) guidelines were followed. The studies that focused on the use of AI tools to enhance medical education and the studies that investigated teaching AI as a new competency were categorized separately to evaluate recent developments.
RESULTS
This systematic review revealed that recent publications recommend the integration of AI training into medical and health informatics curricula.
CONCLUSIONS
To the best of our knowledge, this is the first systematic review exploring the current state of AI education in both medicine and health informatics. Since AI curricula have not been standardized and competencies have not been determined, a framework for specialized AI training in medical and health informatics education is proposed.
PubMed: 32602844
DOI: 10.2196/19285 -
Sleep Medicine Reviews Feb 2023Cognitive models of insomnia highlight internal and external cognitive-biases for sleep-related "threat" in maintaining the disorder. This systematic review of the... (Meta-Analysis)
Meta-Analysis Review
Cognitive models of insomnia highlight internal and external cognitive-biases for sleep-related "threat" in maintaining the disorder. This systematic review of the sleep-related attentional and interpretive-bias literature includes meta-analytic calculations of each construct. Searches identified N = 21 attentional-bias and N = 8 interpretive-bias studies meeting the inclusion/exclusion criteria. Seventeen attentional-bias studies compared normal-sleepers and poor-sleepers/insomnia patients. Using a random effects model, meta-analytic data based on standardized mean differences of attentional-bias studies determined the weighted pooled effect size to be moderate at 0.60 (95%CI:0.26-0.93). Likewise, seven of eight interpretive-bias studies involved group comparisons. Meta-analytic data determined the weighted pooled effect size as moderate at .44 (95%CI:0.19-0.69). Considering these outcomes, disorder congruent cognitive-biases appear to be a key feature of insomnia. Despite statistical support, absence of longitudinal data limits causal inference concerning the relative role cognitive-biases in the development and maintenance of insomnia. Methodological factors pertaining to task design, sample and stimuli are discussed in relation to outcome variation. Finally, we discuss the next steps in advancing the understanding of sleep-related biases in insomnia.
Topics: Humans; Sleep Initiation and Maintenance Disorders; Sleep; Attention; Attentional Bias; Bias
PubMed: 36459947
DOI: 10.1016/j.smrv.2022.101713 -
Cancer Informatics 2019Visual analytics and visualisation can leverage the human perceptual system to interpret and uncover hidden patterns in big data. The advent of next-generation... (Review)
Review
Visual analytics and visualisation can leverage the human perceptual system to interpret and uncover hidden patterns in big data. The advent of next-generation sequencing technologies has allowed the rapid production of massive amounts of genomic data and created a corresponding need for new tools and methods for visualising and interpreting these data. Visualising genomic data requires not only simply plotting of data but should also offer a decision or a choice about what the message should be conveyed in the particular plot; which methodologies should be used to represent the results must provide an easy, clear, and accurate way to the clinicians, experts, or researchers to interact with the data. Genomic data visual analytics is rapidly evolving in parallel with advances in high-throughput technologies such as artificial intelligence (AI) and virtual reality (VR). Personalised medicine requires new genomic visualisation tools, which can efficiently extract knowledge from the genomic data and speed up expert decisions about the best treatment of individual patient's needs. However, meaningful visual analytics of such large genomic data remains a serious challenge. This article provides a comprehensive systematic review and discussion on the tools, methods, and trends for visual analytics of cancer-related genomic data. We reviewed methods for genomic data visualisation including traditional approaches such as scatter plots, heatmaps, coordinates, and networks, as well as emerging technologies using AI and VR. We also demonstrate the development of genomic data visualisation tools over time and analyse the evolution of visualising genomic data.
PubMed: 30890859
DOI: 10.1177/1176935119835546 -
Acta Psychiatrica Scandinavica Jan 2022Major depressive disorder (MDD) and anxiety disorders are both common and especially challenging during pregnancy. Considering possible risks of intrauterine drug... (Meta-Analysis)
Meta-Analysis Review
OBJECTIVE
Major depressive disorder (MDD) and anxiety disorders are both common and especially challenging during pregnancy. Considering possible risks of intrauterine drug exposure of the child, the role of psychopharmacological treatment is ambiguous and various negative obstetric outcomes were inconsistently associated with medication. Consequently, a critical examination of peri- and postnatal phenomena associated with intrauterine exposure to antidepressants based on serotonin reuptake inhibition (SRI) and subsumed under the term "poor neonatal adaptation syndrome" (PNAS) is urgently called for.
METHODS
A comprehensive literature search was conducted, revealing a total number of 33 relevant studies and 69 individual outcomes among 3025 screened studies. Seventeen outcomes allowed meta-analytic evaluation (random effects model). Measures for heterogeneity (I ) and contour-enhanced funnel plots were generated.
RESULTS
Single studies showed increased risks for deficits in neurological functioning and autonomous adaptation in SRI exposed infants. Meta-analytical evaluation showed increased symptom occurrence or severity in exposed neonates for low APGAR scores, birth weight, size for gestational age, preterm delivery, neuromuscular and autonomous regulation, and higher rates of admission to specialized care. Mostly, increased risk after SRI exposure was supported by comparison to unexposed infants born to mothers diagnosed with depression.
CONCLUSION
Whereas statistically significant evidence for various effects of intrauterine exposure to SRI was found, the clinical relevance remains unresolved because of inherently low data quality in this research domain and insufficiently defined samples and outcomes. More systematic research under ethical considerations is required to improve multiprofessional counseling in the many women dealing with MDD during pregnancy and the peripartum.
Topics: Antidepressive Agents; Anxiety Disorders; Child; Depressive Disorder, Major; Female; Gestational Age; Humans; Infant, Newborn; Pregnancy; Pregnancy Complications; Selective Serotonin Reuptake Inhibitors
PubMed: 34486740
DOI: 10.1111/acps.13367 -
Contraception Nov 2023This study aimed to update our 2019 systematic review of data on the effectiveness and safety of misoprostol-only for first-trimester abortion. (Meta-Analysis)
Meta-Analysis Review
OBJECTIVES
This study aimed to update our 2019 systematic review of data on the effectiveness and safety of misoprostol-only for first-trimester abortion.
STUDY DESIGN
We searched PubMed on December 18, 2022, to find published articles describing the outcomes of treatment with misoprostol-only for abortion of viable intrauterine pregnancy at ≤91 days of gestation. From each article identified, two authors independently abstracted relevant data about each group of patients treated with a distinct regimen. We assessed the risk of bias using four defined indicators. We estimated the proportion of patients with treatment failure using meta-analytic methods as well as the proportion hospitalized or transfused after treatment. We examined associations between treatment failure and selected characteristics of the groups.
RESULTS
We identified 49 papers with 66 groups that collectively included 16,354 evaluable patients, of whom 2960 (meta-analytic estimate 15%, 95% CI 12%, 19%) had treatment failures. Of 9228 patients assessed for ongoing pregnancy after treatment, 521 (meta-analytic estimate 6%, 95% CI 5%, 8%) had that condition. Failure risk was significantly associated with misoprostol dose, the total allowed number of doses, the maximum duration of dosing, and certain indicators of risk of bias. Among 11,007 patients allowed to take at least three misoprostol doses, the first consisting of misoprostol 800 mcg administered vaginally, sublingually, or buccally, the meta-analytic estimate of the failure risk was 11% (95% CI 8%, 14%). At most, 0.2% of 15,679 evaluable patients were hospitalized or received transfusions.
CONCLUSIONS
Although some studies in this updated review were adjudicated to have a high risk of bias, the results continue to support the key conclusion of our 2019 analysis: misoprostol-only is effective and safe for the termination of first-trimester intrauterine pregnancy.
IMPLICATIONS
Misoprostol-only is a safe and effective option for medication abortion in the first trimester if mifepristone is unavailable or inaccessible.
Topics: Pregnancy; Female; Humans; Misoprostol; Abortifacient Agents; Pregnancy Trimester, First; Mifepristone; Abortion, Induced; Abortifacient Agents, Nonsteroidal
PubMed: 37517447
DOI: 10.1016/j.contraception.2023.110132 -
Journal of Critical Care Feb 2022Existing expert systems have not improved the diagnostic accuracy of ventilator-associated pneumonia (VAP). The aim of this systematic literature review was to review... (Meta-Analysis)
Meta-Analysis Review
PURPOSE
Existing expert systems have not improved the diagnostic accuracy of ventilator-associated pneumonia (VAP). The aim of this systematic literature review was to review and summarize state-of-the-art prediction models detecting or predicting VAP from exhaled breath, patient reports and demographic and clinical characteristics.
METHODS
Both diagnostic and prognostic prediction models were searched from a representative list of multidisciplinary databases. An extensive list of validated search terms was added to the search to cover papers failing to mention predictive research in their title or abstract. Two authors independently selected studies, while three authors extracted data using predefined criteria and data extraction forms. The Prediction Model Risk of Bias Assessment Tool was used to assess both the risk of bias and the applicability of the prediction modelling studies. Technology readiness was also assessed.
RESULTS
Out of 2052 identified studies, 20 were included. Fourteen (70%) studies reported the predictive performance of diagnostic models to detect VAP from exhaled human breath with a high degree of sensitivity and a moderate specificity. In addition, the majority of them were validated on a realistic dataset. The rest of the studies reported the predictive performance of diagnostic and prognostic prediction models to detect VAP from unstructured narratives [2 (10%)] as well as baseline demographics and clinical characteristics [4 (20%)]. All studies, however, had either a high or unclear risk of bias without significant improvements in applicability.
CONCLUSIONS
The development and deployment of prediction modelling studies are limited in VAP and related outcomes. More computational, translational, and clinical research is needed to bring these tools from the bench to the bedside.
REGISTRATION
PROSPERO CRD42020180218, registered on 05-07-2020.
Topics: Bias; Humans; Pneumonia, Ventilator-Associated; Prognosis
PubMed: 34673331
DOI: 10.1016/j.jcrc.2021.10.001 -
JMIR Medical Informatics Nov 2016Big data analytics offers promise in many business sectors, and health care is looking at big data to provide answers to many age-related issues, particularly dementia... (Review)
Review
BACKGROUND
Big data analytics offers promise in many business sectors, and health care is looking at big data to provide answers to many age-related issues, particularly dementia and chronic disease management.
OBJECTIVE
The purpose of this review was to summarize the challenges faced by big data analytics and the opportunities that big data opens in health care.
METHODS
A total of 3 searches were performed for publications between January 1, 2010 and January 1, 2016 (PubMed/MEDLINE, CINAHL, and Google Scholar), and an assessment was made on content germane to big data in health care. From the results of the searches in research databases and Google Scholar (N=28), the authors summarized content and identified 9 and 14 themes under the categories Challenges and Opportunities, respectively. We rank-ordered and analyzed the themes based on the frequency of occurrence.
RESULTS
The top challenges were issues of data structure, security, data standardization, storage and transfers, and managerial skills such as data governance. The top opportunities revealed were quality improvement, population management and health, early detection of disease, data quality, structure, and accessibility, improved decision making, and cost reduction.
CONCLUSIONS
Big data analytics has the potential for positive impact and global implications; however, it must overcome some legitimate obstacles.
PubMed: 27872036
DOI: 10.2196/medinform.5359 -
Psychoneuroendocrinology May 2023There is continued interest in identifying dysregulated biomarkers that mediate associations between adverse childhood experiences (ACEs) and negative long-term health... (Meta-Analysis)
Meta-Analysis
There is continued interest in identifying dysregulated biomarkers that mediate associations between adverse childhood experiences (ACEs) and negative long-term health outcomes. However, little is known regarding how ACE exposure modulates neural biomarkers to influence poorer health outcomes in ACE-exposed children. To address this, we performed a systematic review and meta-analysis of the impact of ACE exposure on Brain Derived Neurotrophic Factor (BDNF) levels - a neural biomarker involved in childhood and adult neurogenesis and long-term memory formation. Twenty-two studies were selected for inclusion within the systematic review, ten of which were included in meta-analysis. Most included studies retrospectively assessed impacts of childhood maltreatment in clinical populations. Sample size, BDNF protein levels in ACE-exposed and unexposed subjects, and standard deviations were extracted from ten publications to estimate the BDNF ratio of means (ROM) across exposure categories. Overall, no significant difference was found in BDNF protein levels between ACE-exposed and unexposed groups (ROM: 1.08; 95 % CI: 0.93-1.26). Age at sampling, analyte type (e.g., sera, plasma, blood), and categories of ACE exposure contributed to high between-study heterogeneity, some of which was minimized in subset-based analyses. These results support continued investigation into the impact of ACE exposure on neural biomarkers and highlight the potential importance of analyte type and timing of sample collection on study results.
Topics: Child; Adult; Humans; Brain-Derived Neurotrophic Factor; Retrospective Studies; Adverse Childhood Experiences; Biomarkers
PubMed: 36857833
DOI: 10.1016/j.psyneuen.2023.106071 -
Journal of Experimental Psychology.... Sep 2018Many behaviors posing significant risks to public health are characterized by repeated decisions to forego better long-term outcomes in the face of immediate... (Meta-Analysis)
Meta-Analysis Review
Many behaviors posing significant risks to public health are characterized by repeated decisions to forego better long-term outcomes in the face of immediate temptations. Steeply discounting the value of delayed outcomes often underlies a pattern of impulsive choice. Steep delay discounting is correlated with addictions (e.g., substance abuse, obesity) and behaviors such as seatbelt use and risky sexual activity. As evidence accumulates suggesting steep delay discounting plays a causal role in these maladaptive behaviors, researchers have begun testing methods for reducing discounting. In this first systematic and comprehensive review of this literature, the findings of 92 articles employing different methodologies to reduce discounting are evaluated narratively and meta-analytically. Although most of the methods reviewed produced significant reductions in discounting, they varied in effect sizes. Most methods were ideal for influencing one-off choices (e.g., framing and priming manipulations), although other successful manipulations, such as episodic future thinking, could be incorporated into existing therapies designed to produce longer-lasting changes in decision-making. The largest and longest-lasting effects were produced by learning-based manipulations, although translational research is needed to determine the generality and clinical utility of these methods. Methodological shortcomings in the existing literature and suggestions for ameliorating these issues are discussed. This review reveals a variety of methods with translational potential, which, through continued refinement, may prove effective in reducing impulsive choice and its associated maladaptive decisions that negatively impact quality of life. (PsycINFO Database Record
Topics: Choice Behavior; Delay Discounting; Humans; Impulsive Behavior; Public Health; Risk-Taking
PubMed: 30148386
DOI: 10.1037/xge0000462 -
Perspectives on Behavior Science Mar 2023Resurgence is the return of a previously reinforced response as conditions worsen for an alternative response, such as the introduction of extinction, reductions in... (Review)
Review
UNLABELLED
Resurgence is the return of a previously reinforced response as conditions worsen for an alternative response, such as the introduction of extinction, reductions in reinforcement, or punishment. As a procedure, resurgence has been used to model behavioral treatments and understand behavioral processes contributing both to relapse of problem behavior and flexibility during problem-solving. Identifying existing procedural and analytic methods arranged in basic/preclinical research could be used by basic and preclinical researchers to develop novel approaches to study resurgence, whereas translational and clinical researchers could identify potential approaches to combating relapse during behavioral interventions. Despite the study of resurgence for over half a century, there have been no systematic reviews of the basic/preclinical research on resurgence. To characterize the procedural and analytic methods used in basic/preclinical research on resurgence, we performed a systematic review consistent with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). We identified 120 articles consisting of 200 experiments that presented novel empirical research, examined operant behavior, and included standard elements of a resurgence procedure. We reported prevalence and trends in over 60 categories, including participant characteristics (e.g., species, sample size, disability), designs (e.g., single subject, group), procedural characteristics (e.g., responses, reinforcer types, control conditions), criteria defining resurgence (e.g., single test, multiple tests, relative to control), and analytic strategies (e.g., inferential statistics, quantitative analysis, visual inspection). We make some recommendations for future basic, preclinical, and clinical research based on our findings of this expanding literature.
SUPPLEMENTARY INFORMATION
The online version contains supplementary material available at 10.1007/s40614-022-00361-y.
PubMed: 37006602
DOI: 10.1007/s40614-022-00361-y