scholarly journals Ocular toxicity and Hydroxychloroquine: A Rapid Meta-Analysis

2020 ◽  
Author(s):  
Matthew Michelson ◽  
Tiffany Chow ◽  
Neil Martin ◽  
Mike Ross ◽  
Amelia Tee ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Data Quality ◽  
Side Effect ◽  
Meta Analysis ◽  
Clinical Question ◽  
Ocular Toxicity ◽  
Individual Study ◽  
Rapid Access ◽  
The Individual ◽  
Clinical Queries

AbstractRapid access to evidence is crucial in times of evolving clinical crisis. To that end, we propose a novel mechanism to answer clinical queries: Rapid Meta-Analysis (RMA). Unlike traditional meta-analysis, RMA balances quick time-to-production with reasonable data quality assurances, leveraging Artificial Intelligence to strike this balance. This article presents an example RMA to a currently relevant clinical question: Is ocular toxicity and vision compromise a side effect with hydroxychloroquine therapy?As of this writing, hydroxychloroquine is a leading candidate in the treatment of COVID-19. By combining AI with human analysis, our RMA identified 11 studies looking at ocular toxicity as a side effect and estimated the incidence to be 3.4% (95% CI: 1.11-9.96%). The heterogeneity across the individual study findings was high, and interpretation of the result should take this into account. Importantly, this RMA, from search to screen to analysis, took less than 30 minutes to produce.

scholarly journals Artificial Intelligence for Rapid Meta-Analysis: Case Study on Ocular Toxicity of Hydroxychloroquine (Preprint)

2020 ◽  
Author(s):  
Matthew Michelson ◽  
Tiffany Chow ◽  
Neil A Martin ◽  
Mike Ross ◽  
Amelia Tee Qiao Ying ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Side Effect ◽  
Processing Time ◽  
Meta Analysis ◽  
Ocular Toxicity ◽  
Individual Study ◽  
Novel Approach ◽  
Time Period ◽  
Clinical Queries

BACKGROUND Rapid access to evidence is crucial in times of an evolving clinical crisis. To that end, we propose a novel approach to answer clinical queries, termed rapid meta-analysis (RMA). Unlike traditional meta-analysis, RMA balances a quick time to production with reasonable data quality assurances, leveraging artificial intelligence (AI) to strike this balance. OBJECTIVE We aimed to evaluate whether RMA can generate meaningful clinical insights, but crucially, in a much faster processing time than traditional meta-analysis, using a relevant, real-world example. METHODS The development of our RMA approach was motivated by a currently relevant clinical question: is ocular toxicity and vision compromise a side effect of hydroxychloroquine therapy? At the time of designing this study, hydroxychloroquine was a leading candidate in the treatment of coronavirus disease (COVID-19). We then leveraged AI to pull and screen articles, automatically extract their results, review the studies, and analyze the data with standard statistical methods. RESULTS By combining AI with human analysis in our RMA, we generated a meaningful, clinical result in less than 30 minutes. The RMA identified 11 studies considering ocular toxicity as a side effect of hydroxychloroquine and estimated the incidence to be 3.4% (95% CI 1.11%-9.96%). The heterogeneity across individual study findings was high, which should be taken into account in interpretation of the result. CONCLUSIONS We demonstrate that a novel approach to meta-analysis using AI can generate meaningful clinical insights in a much shorter time period than traditional meta-analysis.

scholarly journals Artificial Intelligence for Rapid Meta-Analysis: Case Study on Ocular Toxicity of Hydroxychloroquine

10.2196/20007 ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. e20007
Author(s):  
Matthew Michelson ◽  
Tiffany Chow ◽  
Neil A Martin ◽  
Mike Ross ◽  
Amelia Tee Qiao Ying ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Side Effect ◽  
Processing Time ◽  
Meta Analysis ◽  
Ocular Toxicity ◽  
Individual Study ◽  
Novel Approach ◽  
Time Period ◽  
Clinical Queries

Background Rapid access to evidence is crucial in times of an evolving clinical crisis. To that end, we propose a novel approach to answer clinical queries, termed rapid meta-analysis (RMA). Unlike traditional meta-analysis, RMA balances a quick time to production with reasonable data quality assurances, leveraging artificial intelligence (AI) to strike this balance. Objective We aimed to evaluate whether RMA can generate meaningful clinical insights, but crucially, in a much faster processing time than traditional meta-analysis, using a relevant, real-world example. Methods The development of our RMA approach was motivated by a currently relevant clinical question: is ocular toxicity and vision compromise a side effect of hydroxychloroquine therapy? At the time of designing this study, hydroxychloroquine was a leading candidate in the treatment of coronavirus disease (COVID-19). We then leveraged AI to pull and screen articles, automatically extract their results, review the studies, and analyze the data with standard statistical methods. Results By combining AI with human analysis in our RMA, we generated a meaningful, clinical result in less than 30 minutes. The RMA identified 11 studies considering ocular toxicity as a side effect of hydroxychloroquine and estimated the incidence to be 3.4% (95% CI 1.11%-9.96%). The heterogeneity across individual study findings was high, which should be taken into account in interpretation of the result. Conclusions We demonstrate that a novel approach to meta-analysis using AI can generate meaningful clinical insights in a much shorter time period than traditional meta-analysis.

Questions for Meta-Analysis

1997 ◽  
Vol 81 (1) ◽  
pp. 3-15 ◽  
Author(s):  
David Sohn
Keyword(s):  
Scientific Discovery ◽  
Meta Analysis ◽  
Rational Analysis ◽  
Individual Study ◽  
Conceptual Foundation ◽  
Psychological Science ◽  
Present Essay ◽  
History Of ◽  
The Individual ◽  
Successful Science

In spite of an abundance of data, the empirical evidence as yet does not make clear whether meta-analysis will bring about progress in psychological science. Therefore, it is still useful and desirable to engage in rational analysis of the methodology. Such analysis is done in the present essay by posing five questions that go to the logical and conceptual foundation of meta-analysis. The questions are (a) What are the grounds for believing that the review of the literature, even a quantitative one, will bring about scientific discovery? (b) Why is the individual study devalued when the history of successful science seems largely the story of the success of the individual study? (c) What is the rationale for believing that data analysis by itself can markedly improve the fortunes of psychological science? (d) Is there a basis for claims made on behalf of meta-analysis that it is more accurate than either the traditional literature review or the individual study? (e) Is there justification for the claim that de facto meta-analysis has been used effectively in physical science?

scholarly journals High Flow Nasal Oxygen versus Conventional Oxygen Therapy in Gastrointestinal Endoscopy with Conscious Sedation: A Systematic Review and Meta-Analysis

2022 ◽  
Author(s):  
Wan-Jie Gu ◽  
◽  
Hao-Tian Wang ◽  
Jiao Huang ◽  
Zhe-Ming Zhao ◽  
...  
Keyword(s):  
Oxygen Therapy ◽  
Gastrointestinal Endoscopy ◽  
Conscious Sedation ◽  
Small Sample Size ◽  
Meta Analysis ◽  
Small Sample ◽  
High Flow ◽  
Individual Study ◽  
Review Question ◽  
The Individual

Review question / Objective: To compare the efficacy of high flow nasal oxygen with conventional oxygen therapy to prevent hypoxemia in gastrointestinal endoscopy with conscious sedation. Condition being studied: High flow nasal oxygen, a novel technique, may be an alternative to conventional oxygen therapy. High flow nasal oxygen can deliver heated and humidified oxygen via special nasal cannula with high flow (up to 70 L/min). It has been applied to improve oxygenation in clinical entities, favored by increasing evidence supporting its efficacy. Recently, the use of high flow nasal oxygen has spreaded to gastrointestinal endoscopy. However, the efficacy of high flow nasal oxygen in gastrointestinal endoscopy has not yet been well evaluated due to small sample size of the individual study and conflicting results.

N-terminal pro-B-type Natriuretic Peptides’ Prognostic Utility Is Overestimated in Meta-analyses Using Study-specific Optimal Diagnostic Thresholds

2015 ◽  
Vol 123 (2) ◽  
pp. 264-271 ◽  
Author(s):  
Danielle Potgieter ◽  
Dale Simmers ◽  
Lisa Ryan ◽  
Bruce M. Biccard ◽  
Giovanna A. Lurati-Buse ◽  
...  
Keyword(s):  
Meta Analysis ◽  
Individual Study ◽  
Entire Cohort ◽  
Individual Patient Level ◽  
Diagnostic Thresholds ◽  
Level Data ◽  
Prognostic Utility ◽  
The Individual ◽  
Meta Analyses ◽  
Post Hoc

Abstract Background: N-terminal fragment B-type natriuretic peptide (NT-proBNP) prognostic utility is commonly determined post hoc by identifying a single optimal discrimination threshold tailored to the individual study population. The authors aimed to determine how using these study-specific post hoc thresholds impacts meta-analysis results. Methods: The authors conducted a systematic review of studies reporting the ability of preoperative NT-proBNP measurements to predict the composite outcome of all-cause mortality and nonfatal myocardial infarction at 30 days after noncardiac surgery. Individual patient-level data NT-proBNP thresholds were determined using two different methodologies. First, a single combined NT-proBNP threshold was determined for the entire cohort of patients, and a meta-analysis conducted using this single threshold. Second, study-specific thresholds were determined for each individual study, with meta-analysis being conducted using these study-specific thresholds. Results: The authors obtained individual patient data from 14 studies (n = 2,196). Using a single NT-proBNP cohort threshold, the odds ratio (OR) associated with an increased NT-proBNP measurement was 3.43 (95% CI, 2.08 to 5.64). Using individual study-specific thresholds, the OR associated with an increased NT-proBNP measurement was 6.45 (95% CI, 3.98 to 10.46). In smaller studies (<100 patients) a single cohort threshold was associated with an OR of 5.4 (95% CI, 2.27 to 12.84) as compared with an OR of 14.38 (95% CI, 6.08 to 34.01) for study-specific thresholds. Conclusions: Post hoc identification of study-specific prognostic biomarker thresholds artificially maximizes biomarker predictive power, resulting in an amplification or overestimation during meta-analysis of these results. This effect is accentuated in small studies.

The Orchard Plot: Cultivating a Forest Plot for Use in Ecology, Evolution and Beyond

2019 ◽  
Author(s):  
Shinichi Nakagawa ◽  
Malgorzata Lagisz ◽  
Rose E O'Dea ◽  
Joanna Rutkowska ◽  
Yefeng Yang ◽  
...  
Keyword(s):  
Meta Analysis ◽  
R Package ◽  
Effect Sizes ◽  
Forest Plot ◽  
Point Estimates ◽  
Aggregate Effect ◽  
The Individual ◽  
Meta Analyses ◽  
Heterogeneous Effect ◽  
Intuitive Interpretation

‘Classic’ forest plots show the effect sizes from individual studies and the aggregate effect from a meta-analysis. However, in ecology and evolution meta-analyses routinely contain over 100 effect sizes, making the classic forest plot of limited use. We surveyed 102 meta-analyses in ecology and evolution, finding that only 11% use the classic forest plot. Instead, most used a ‘forest-like plot’, showing point estimates (with 95% confidence intervals; CIs) from a series of subgroups or categories in a meta-regression. We propose a modification of the forest-like plot, which we name the ‘orchard plot’. Orchard plots, in addition to showing overall mean effects and CIs from meta-analyses/regressions, also includes 95% prediction intervals (PIs), and the individual effect sizes scaled by their precision. The PI allows the user and reader to see the range in which an effect size from a future study may be expected to fall. The PI, therefore, provides an intuitive interpretation of any heterogeneity in the data. Supplementing the PI, the inclusion of underlying effect sizes also allows the user to see any influential or outlying effect sizes. We showcase the orchard plot with example datasets from ecology and evolution, using the R package, orchard, including several functions for visualizing meta-analytic data using forest-plot derivatives. We consider the orchard plot as a variant on the classic forest plot, cultivated to the needs of meta-analysts in ecology and evolution. Hopefully, the orchard plot will prove fruitful for visualizing large collections of heterogeneous effect sizes regardless of the field of study.

A Human/Computer Learning Network to Improve Biodiversity Conservation and Research

AI Magazine ◽  
2012 ◽  
Vol 34 (1) ◽  
pp. 10 ◽  
Author(s):  
Steve Kelling ◽  
Jeff Gerbracht ◽  
Daniel Fink ◽  
Carl Lagoze ◽  
Weng-Keen Wong ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Feedback Loop ◽  
Human Computation ◽  
Computational Power ◽  
Learning Networks ◽  
Learning Network ◽  
Computer Learning ◽  
The Individual ◽  
Citizen Science Project

In this paper we describe eBird, a citizen-science project that takes advantage of the human observational capacity to identify birds to species, which is then used to accurately represent patterns of bird occurrences across broad spatial and temporal extents. eBird employs artificial intelligence techniques such as machine learning to improve data quality by taking advantage of the synergies between human computation and mechanical computation. We call this a Human-Computer Learning Network, whose core is an active learning feedback loop between humans and machines that dramatically improves the quality of both, and thereby continually improves the effectiveness of the network as a whole. In this paper we explore how Human-Computer Learning Networks can leverage the contributions of a broad recruitment of human observers and processes their contributed data with Artificial Intelligence algorithms leading to a computational power that far exceeds the sum of the individual parts.

scholarly journals Does higher education properly prepare graduates for the growing artificial intelligence market? Gaps identification using text mining

2021 ◽  
pp. 1-13
Author(s):  
Lamiae Benhayoun ◽  
Daniel Lang
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Text Mining ◽  
Natural Language ◽  
Language Processing ◽  
Academic Training ◽  
Market Requirements ◽  
Job Advertisements ◽  
The Individual

BACKGROUND: The renewed advent of Artificial Intelligence (AI) is inducing profound changes in the classic categories of technology professions and is creating the need for new specific skills. OBJECTIVE: Identify the gaps in terms of skills between academic training on AI in French engineering and Business Schools, and the requirements of the labour market. METHOD: Extraction of AI training contents from the schools’ websites and scraping of a job advertisements’ website. Then, analysis based on a text mining approach with a Python code for Natural Language Processing. RESULTS: Categorization of occupations related to AI. Characterization of three classes of skills for the AI market: Technical, Soft and Interdisciplinary. Skills’ gaps concern some professional certifications and the mastery of specific tools, research abilities, and awareness of ethical and regulatory dimensions of AI. CONCLUSIONS: A deep analysis using algorithms for Natural Language Processing. Results that provide a better understanding of the AI capability components at the individual and the organizational levels. A study that can help shape educational programs to respond to the AI market requirements.

Trust in Artificial Intelligence: Meta-Analytic Findings

2021 ◽  
pp. 001872082110139
Author(s):  
Alexandra D. Kaplan ◽  
Theresa T. Kessler ◽  
J. Christopher Brill ◽  
P. A. Hancock
Keyword(s):  
Artificial Intelligence ◽  
Empirical Research ◽  
Meta Analysis ◽  
Automated Vehicles ◽  
Factors Influencing ◽  
Moderating Factors ◽  
Shared Context ◽  
Build Systems ◽  
Significant Factors

Objective The present meta-analysis sought to determine significant factors that predict trust in artificial intelligence (AI). Such factors were divided into those relating to (a) the human trustor, (b) the AI trustee, and (c) the shared context of their interaction. Background There are many factors influencing trust in robots, automation, and technology in general, and there have been several meta-analytic attempts to understand the antecedents of trust in these areas. However, no targeted meta-analysis has been performed examining the antecedents of trust in AI. Method Data from 65 articles examined the three predicted categories, as well as the subcategories of human characteristics and abilities, AI performance and attributes, and contextual tasking. Lastly, four common uses for AI (i.e., chatbots, robots, automated vehicles, and nonembodied, plain algorithms) were examined as further potential moderating factors. Results Results showed that all of the examined categories were significant predictors of trust in AI as well as many individual antecedents such as AI reliability and anthropomorphism, among many others. Conclusion Overall, the results of this meta-analysis determined several factors that influence trust, including some that have no bearing on AI performance. Additionally, we highlight the areas where there is currently no empirical research. Application Findings from this analysis will allow designers to build systems that elicit higher or lower levels of trust, as they require.

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