Artificial Intelligence for Diagnosis of Acute Coronary Syndromes: A Meta-analysis of Machine Learning Approaches

2020 ◽  
Vol 36 (4) ◽  
pp. 577-583 ◽  
Author(s):  
Patrick A. Iannattone ◽  
Xun Zhao ◽  
Jacob VanHouten ◽  
Akhil Garg ◽  
Thao Huynh
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Acute Coronary Syndromes ◽  
Meta Analysis ◽  
Learning Approaches ◽  
Coronary Syndromes

Is there Sex-related Outcome Difference According to oral P2Y12 Inhibitors in Patients with Acute Coronary Syndromes? A Systematic Review and Meta-Analysis of 107,126 Patients

2019 ◽  
Vol 17 (2) ◽  
pp. 191-203
Author(s):  
Oliver Brown ◽  
Jennifer Rossington ◽  
Gill Louise Buchanan ◽  
Giuseppe Patti ◽  
Angela Hoye
Keyword(s):  
Systematic Review ◽  
Acute Coronary Syndromes ◽  
Meta Analysis ◽  
Indirect Comparison ◽  
Bleeding Risk ◽  
Cardiovascular Death ◽  
P2y12 Inhibitors ◽  
Significant Difference ◽  
Coronary Syndromes ◽  
Randomised Controlled

Background and Objectives: The majority of patients included in trials of anti-platelet therapy are male. This systematic review and meta-analysis aimed to determine whether, in addition to aspirin, P2Y12 blockade is beneficial in both women and men with acute coronary syndromes. </P><P> Methods: Electronic databases were searched and nine eligible randomised controlled studies were identified that had sex-specific clinical outcomes (n=107,126 patients). Risk Ratios (RR) and 95% Confidence Intervals (CI) were calculated for a composite of cardiovascular death, myocardial infarction or stroke (MACE), and a safety endpoint of major bleeding for each sex. Indirect comparison analysis was performed to statistically compare ticagrelor against prasugrel. </P><P> Results: Compared to aspirin alone, clopidogrel reduced MACE in men (RR, 0.79; 95% CI, 0.68 to 0.92; p=0.003), but was not statistically significant in women (RR, 0.88; 95% CI, 0.75 to 1.02, p=0.08). Clopidogrel therapy significantly increased bleeding in women but not men. Compared to clopidogrel, prasugrel was beneficial in men (RR, 0.84; 95% CI, 0.73 to 0.97; p=0.02) but not statistically significant in women (RR, 0.94; 95% CI, 0.83 to 1.06; p=0.30); ticagrelor reduced MACE in both men (RR, 0.85; 95% CI, 0.77 to 0.94; p=0.001) and women (RR, 0.84; 95% CI, 0.73 to 0.97; p=0.02). Indirect comparison demonstrated no significant difference between ticagrelor and prasugrel in either sex. Compared to clopidogrel, ticagrelor and prasugrel increased bleeding risk in both women and men. </P><P> Conclusion: In summary, in comparison to monotherapy with aspirin, P2Y12 inhibitors reduce MACE in women and men. Ticagrelor was shown to be superior to clopidogrel in both sexes. Prasugrel showed a statistically significant benefit only in men; however indirect comparison did not demonstrate superiority of ticagrelor over prasugrel in women.

scholarly journals Artificial Intelligence and Behavioral Science Through the Looking Glass: Challenges for Real-World Application

2020 ◽  
Vol 54 (12) ◽  
pp. 942-947
Author(s):  
Pol Mac Aonghusa ◽  
Susan Michie
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Behavior Change ◽  
Behavioral Science ◽  
Lessons Learned ◽  
Learning Approaches ◽  
Intervention Evaluation ◽  
Research Activities ◽  
Behavior Change Interventions

Abstract Background Artificial Intelligence (AI) is transforming the process of scientific research. AI, coupled with availability of large datasets and increasing computational power, is accelerating progress in areas such as genetics, climate change and astronomy [NeurIPS 2019 Workshop Tackling Climate Change with Machine Learning, Vancouver, Canada; Hausen R, Robertson BE. Morpheus: A deep learning framework for the pixel-level analysis of astronomical image data. Astrophys J Suppl Ser. 2020;248:20; Dias R, Torkamani A. AI in clinical and genomic diagnostics. Genome Med. 2019;11:70.]. The application of AI in behavioral science is still in its infancy and realizing the promise of AI requires adapting current practices. Purposes By using AI to synthesize and interpret behavior change intervention evaluation report findings at a scale beyond human capability, the HBCP seeks to improve the efficiency and effectiveness of research activities. We explore challenges facing AI adoption in behavioral science through the lens of lessons learned during the Human Behaviour-Change Project (HBCP). Methods The project used an iterative cycle of development and testing of AI algorithms. Using a corpus of published research reports of randomized controlled trials of behavioral interventions, behavioral science experts annotated occurrences of interventions and outcomes. AI algorithms were trained to recognize natural language patterns associated with interventions and outcomes from the expert human annotations. Once trained, the AI algorithms were used to predict outcomes for interventions that were checked by behavioral scientists. Results Intervention reports contain many items of information needing to be extracted and these are expressed in hugely variable and idiosyncratic language used in research reports to convey information makes developing algorithms to extract all the information with near perfect accuracy impractical. However, statistical matching algorithms combined with advanced machine learning approaches created reasonably accurate outcome predictions from incomplete data. Conclusions AI holds promise for achieving the goal of predicting outcomes of behavior change interventions, based on information that is automatically extracted from intervention evaluation reports. This information can be used to train knowledge systems using machine learning and reasoning algorithms.

scholarly journals TIMI, GRACE and alternative risk scores in Acute Coronary Syndromes: A meta-analysis of 40 derivation studies on 216,552 patients and of 42 validation studies on 31,625 patients

2012 ◽  
Vol 33 (3) ◽  
pp. 507-514 ◽  
Author(s):  
Fabrizio D'Ascenzo ◽  
Giuseppe Biondi-Zoccai ◽  
Claudio Moretti ◽  
Mario Bollati ◽  
Pierluigi Omedè ◽  
...  
Keyword(s):  
Validation Studies ◽  
Acute Coronary Syndromes ◽  
Meta Analysis ◽  
Risk Scores ◽  
Coronary Syndromes

scholarly journals A Comprehensive Study of Artificial Intelligence and Machine Learning Approaches in Confronting the Coronavirus (COVID-19) Pandemic

2021 ◽  
pp. 002073142110174
Author(s):  
Md Mijanur Rahman ◽  
Fatema Khatun ◽  
Ashik Uzzaman ◽  
Sadia Islam Sami ◽  
Md Al-Amin Bhuiyan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Care ◽  
Language Processing ◽  
Probabilistic Models ◽  
Health Care Systems ◽  
Learning Approaches ◽  
Care Systems ◽  
Novel Coronavirus ◽  
Comprehensive Study

The novel coronavirus disease (COVID-19) has spread over 219 countries of the globe as a pandemic, creating alarming impacts on health care, socioeconomic environments, and international relationships. The principal objective of the study is to provide the current technological aspects of artificial intelligence (AI) and other relevant technologies and their implications for confronting COVID-19 and preventing the pandemic’s dreadful effects. This article presents AI approaches that have significant contributions in the fields of health care, then highlights and categorizes their applications in confronting COVID-19, such as detection and diagnosis, data analysis and treatment procedures, research and drug development, social control and services, and the prediction of outbreaks. The study addresses the link between the technologies and the epidemics as well as the potential impacts of technology in health care with the introduction of machine learning and natural language processing tools. It is expected that this comprehensive study will support researchers in modeling health care systems and drive further studies in advanced technologies. Finally, we propose future directions in research and conclude that persuasive AI strategies, probabilistic models, and supervised learning are required to tackle future pandemic challenges.

scholarly journals Meta-analysis cum machine learning approaches address the structure and biogeochemical potential of marine copepod associated bacteriobiomes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Balamurugan Sadaiappan ◽  
Chinnamani PrasannaKumar ◽  
V. Uthara Nambiar ◽  
Mahendran Subramanian ◽  
Manguesh U. Gauns
Keyword(s):  
Machine Learning ◽  
Iron Transport ◽  
Meta Analysis ◽  
Zooplankton Community ◽  
Gradient Boosting ◽  
Learning Approaches ◽  
16S Rdna Gene ◽  
Marine Copepod ◽  
Form Of Life ◽  
First Time

AbstractCopepods are the dominant members of the zooplankton community and the most abundant form of life. It is imperative to obtain insights into the copepod-associated bacteriobiomes (CAB) in order to identify specific bacterial taxa associated within a copepod, and to understand how they vary between different copepods. Analysing the potential genes within the CAB may reveal their intrinsic role in biogeochemical cycles. For this, machine-learning models and PICRUSt2 analysis were deployed to analyse 16S rDNA gene sequences (approximately 16 million reads) of CAB belonging to five different copepod genera viz., Acartia spp., Calanus spp., Centropages sp., Pleuromamma spp., and Temora spp.. Overall, we predict 50 sub-OTUs (s-OTUs) (gradient boosting classifiers) to be important in five copepod genera. Among these, 15 s-OTUs were predicted to be important in Calanus spp. and 20 s-OTUs as important in Pleuromamma spp.. Four bacterial s-OTUs Acinetobacter johnsonii, Phaeobacter, Vibrio shilonii and Piscirickettsiaceae were identified as important s-OTUs in Calanus spp., and the s-OTUs Marinobacter, Alteromonas, Desulfovibrio, Limnobacter, Sphingomonas, Methyloversatilis, Enhydrobacter and Coriobacteriaceae were predicted as important s-OTUs in Pleuromamma spp., for the first time. Our meta-analysis revealed that the CAB of Pleuromamma spp. had a high proportion of potential genes responsible for methanogenesis and nitrogen fixation, whereas the CAB of Temora spp. had a high proportion of potential genes involved in assimilatory sulphate reduction, and cyanocobalamin synthesis. The CAB of Pleuromamma spp. and Temora spp. have potential genes accountable for iron transport.

scholarly journals Capabilities and Challenges Using Machine Learning in Tunnelling

2021 ◽  
Author(s):  
Thomas Marcher ◽  
Georg Erharter ◽  
Paul Unterlass
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Sharp Increase ◽  
Tunnel Construction ◽  
Learning Approaches ◽  
Building Information ◽  
Tunnel Design ◽  
Mass Classification ◽  
Design Analyses ◽  
Operational Processes

Digitalization changes the design and operational processes in tunnelling. The way of gathering geological data in the field of tunnelling, the methods of rock mass classification as well as the application of tunnel design analyses, tunnel construction processes and tunnel maintenance will be influenced by this digital transformation. The ongoing digitalization in tunnelling through applications like building information modelling and artificial intelligence, addressing a variety of difficult tasks, is moving forward. Increasing overall amounts of data (big data), combined with the ease to access strong computing powers, are leading to a sharp increase in the successful application of data analytics and techniques of artificial intelligence. Artificial Intelligence now arrives also in the fields of geotechnical engineering, tunnelling and engineering geology. The chapter focuses on the potential for machine learning methods – a branch of Artificial Intelligence - in tunnelling. Examples will show that training artificial neural networks in a supervised manner works and yields valuable information. Unsupervised machine learning approaches will be also discussed, where the final classification is not imposed upon the data, but learned from it. Finally, reinforcement learning seems to be trendsetting but not being in use for specific tunnel applications yet.

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