Applications of Artificial Intelligence in Non-cardiac Vascular Diseases: A Bibliographic Analysis

Angiology ◽  
2022 ◽  
pp. 000331972110622
Author(s):  
Fabien Lareyre ◽  
Cong Duy Lê ◽  
Ali Ballaith ◽  
Cédric Adam ◽  
Marion Carrier ◽  
...  

Research output related to artificial intelligence (AI) in vascular diseases has been poorly investigated. The aim of this study was to evaluate scientific publications on AI in non-cardiac vascular diseases. A systematic literature search was conducted using the PubMed database and a combination of keywords and focused on three main vascular diseases (carotid, aortic and peripheral artery diseases). Original articles written in English and published between January 1995 and December 2020 were included. Data extracted included the date of publication, the journal, the identity, number, affiliated country of authors, the topics of research, and the fields of AI. Among 171 articles included, the three most productive countries were USA, China, and United Kingdom. The fields developed within AI included: machine learning (n = 90; 45.0%), vision (n = 45; 22.5%), robotics (n = 42; 21.0%), expert system (n = 15; 7.5%), and natural language processing (n = 8; 4.0%). The applications were mainly new tools for: the treatment (n = 52; 29.1%), prognosis (n = 45; 25.1%), the diagnosis and classification of vascular diseases (n = 38; 21.2%), and imaging segmentation (n = 38; 21.2%). By identifying the main techniques and applications, this study also pointed to the current limitations and may help to better foresee future applications for clinical practice.

Author(s):  
Shatakshi Singh ◽  
Kanika Gautam ◽  
Prachi Singhal ◽  
Sunil Kumar Jangir ◽  
Manish Kumar

The recent development in artificial intelligence is quite astounding in this decade. Especially, machine learning is one of the core subareas of AI. Also, ML field is an incessantly growing along with evolution and becomes a rise in its demand and importance. It transmogrified the way data is extracted, analyzed, and interpreted. Computers are trained to get in a self-training mode so that when new data is fed they can learn, grow, change, and develop themselves without explicit programming. It helps to make useful predictions that can guide better decisions in a real-life situation without human interference. Selection of ML tool is always a challenging task, since choosing an appropriate tool can end up saving time as well as making it faster and easier to provide any solution. This chapter provides a classification of various machine learning tools on the following aspects: for non-programmers, for model deployment, for Computer vision, natural language processing, and audio for reinforcement learning and data mining.


2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
J. Bouaziz ◽  
R. Mashiach ◽  
S. Cohen ◽  
A. Kedem ◽  
A. Baron ◽  
...  

Endometriosis is a disease characterized by the development of endometrial tissue outside the uterus, but its cause remains largely unknown. Numerous genes have been studied and proposed to help explain its pathogenesis. However, the large number of these candidate genes has made functional validation through experimental methodologies nearly impossible. Computational methods could provide a useful alternative for prioritizing those most likely to be susceptibility genes. Using artificial intelligence applied to text mining, this study analyzed the genes involved in the pathogenesis, development, and progression of endometriosis. The data extraction by text mining of the endometriosis-related genes in the PubMed database was based on natural language processing, and the data were filtered to remove false positives. Using data from the text mining and gene network information as input for the web-based tool, 15,207 endometriosis-related genes were ranked according to their score in the database. Characterization of the filtered gene set through gene ontology, pathway, and network analysis provided information about the numerous mechanisms hypothesized to be responsible for the establishment of ectopic endometrial tissue, as well as the migration, implantation, survival, and proliferation of ectopic endometrial cells. Finally, the human genome was scanned through various databases using filtered genes as a seed to determine novel genes that might also be involved in the pathogenesis of endometriosis but which have not yet been characterized. These genes could be promising candidates to serve as useful diagnostic biomarkers and therapeutic targets in the management of endometriosis.


2021 ◽  
Vol 13 (19) ◽  
pp. 10856
Author(s):  
I-Cheng Chang ◽  
Tai-Kuei Yu ◽  
Yu-Jie Chang ◽  
Tai-Yi Yu

Facing the big data wave, this study applied artificial intelligence to cite knowledge and find a feasible process to play a crucial role in supplying innovative value in environmental education. Intelligence agents of artificial intelligence and natural language processing (NLP) are two key areas leading the trend in artificial intelligence; this research adopted NLP to analyze the research topics of environmental education research journals in the Web of Science (WoS) database during 2011–2020 and interpret the categories and characteristics of abstracts for environmental education papers. The corpus data were selected from abstracts and keywords of research journal papers, which were analyzed with text mining, cluster analysis, latent Dirichlet allocation (LDA), and co-word analysis methods. The decisions regarding the classification of feature words were determined and reviewed by domain experts, and the associated TF-IDF weights were calculated for the following cluster analysis, which involved a combination of hierarchical clustering and K-means analysis. The hierarchical clustering and LDA decided the number of required categories as seven, and the K-means cluster analysis classified the overall documents into seven categories. This study utilized co-word analysis to check the suitability of the K-means classification, analyzed the terms with high TF-IDF wights for distinct K-means groups, and examined the terms for different topics with the LDA technique. A comparison of the results demonstrated that most categories that were recognized with K-means and LDA methods were the same and shared similar words; however, two categories had slight differences. The involvement of field experts assisted with the consistency and correctness of the classified topics and documents.


2020 ◽  
Vol 19 (4) ◽  
pp. 493-519
Author(s):  
Yaroslav D. Sovetkin ◽  

Managerial innovations have become the topic of interest for many scholars, but this concept remains underdeveloped and poorly managed among the academy and business community in Russia. This paper offers the composition of approach to definition and classifi cation of managerial innovations, formed on the basis of exploration of the concept “managerial innovation” evolution, and estimation of the relationship with a more general concept “innovation”. The suggested composition of approach is based on the three-stage bibliographic analysis of scientific literature. In course of the bibliographic research, scientific articles were selected according to the key words, period of publication and citation index. 140 scientific publications were identified and collected for the period from 1975 to 2019 covering citation indexes from 0 to 12 476 by Web of Science citation database and from 4 to 2 185 by Scopus database. On the basis of the conducted bibliographic research, the author introduces his definition of innovation and managerial innovation and explains the connection between them. Within the conducted research different approaches to classification of managerial innovations were studied and on their basis a new approach to classification of managerial innovations was proposed. The findings can be useful for different avenues of further research regarding managerial innovations.


BioMedicine ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 5-17
Author(s):  
Priyanka Ramesh ◽  
Ramanathan Karuppasamy ◽  
Shanthi Veerappapillai

Author(s):  
Gleb Danilov ◽  
Timur Ishankulov ◽  
Konstantin Kotik ◽  
Yuriy Orlov ◽  
Mikhail Shifrin ◽  
...  

Automated text classification is a natural language processing (NLP) technology that could significantly facilitate scientific literature selection. A specific topical dataset of 630 article abstracts was obtained from the PubMed database. We proposed 27 parametrized options of PubMedBERT model and 4 ensemble models to solve a binary classification task on that dataset. Three hundred tests with resamples were performed in each classification approach. The best PubMedBERT model demonstrated F1-score = 0.857 while the best ensemble model reached F1-score = 0.853. We concluded that the short scientific texts classification quality might be improved using the latest state-of-art approaches.


Author(s):  
Hamza Chehili ◽  
Salah Eddine Aliouane ◽  
Abdelhafedh Bendahmane ◽  
Mohamed Abdelhafid Hamidechi

<span>Previously, the classification of enzymes was carried out by traditional heuritic methods, however, due to the rapid increase in the number of enzymes being discovered, new methods aimed to classify them are required. Their goal is to increase the speed of processing and to improve the accuracy of predictions. The Purpose of this work is to develop an approach that predicts the enzymes’ classification. This approach is based on two axes of artificial intelligence (AI): natural language processing (NLP) and deep learning (DL). The results obtained in the tests  show the effectiveness of this approach. The combination of these two tools give a model with a great capacity to extract knowledge from enzyme data to predict and classify them. The proposed model learns through intensive training by exploiting enzyme sequences. This work highlights the contribution of this approach to improve the precision of enzyme classification.</span>


2019 ◽  
Vol 8 (3) ◽  
pp. 208-215
Author(s):  
I. V. Buzaev ◽  
V. V. Plechev ◽  
R. M. Galimova ◽  
A. R. Kireev ◽  
L. K. Yuldybaev ◽  
...  

Introduction. The widespread adoption of Artificial Intelligence (AI) technologies forms the core of the so-called Industrial Revolution 4.0.The aim of this study is to examine qualitative changes occurring over the last two years in the development of AI through an examination of trends in PubMed publications.Materials. All abstracts with keyword “artificial intelligence” were downloaded from PubMed database https://www.ncbi.nlm.nih.gov/pubmed/ in the form of .txt files. In order to produce a generalisation of topics, we classified present applications of AI in medicine. To this end, 78,420 abstracts, 5558 reviews, 304 randomised controlled trials, 247 multicentre studies and 4137 other publication types were extracted. (Figure 1). Next, the typical applications were classified.Results. Interest in the topic of AI in publications indexed in the PubMed library is increasing according to general innovation development principles. Along with English publications, the number of non-English publications continued to increase until 2018, represented especially by Chinese, German and French languages. By 2018, the number of non-English publications had started to decrease in favour of English publications. Implementations of AI are already being adopted in contemporary practice. Thus, AI tools have moved out of the theoretical realm to find mainstream application.Conclusions. Tools for machine learning have become widely available to working scientists over the last two years. Since this includes FDA-approved tools for general clinical practice, the change not only affects to researchers but also clinical practitioners. Medical imaging and analysis applications already approved for the most part demonstrate comparable accuracy with the human specialist. A classification of developed AI applications is presented in the article.


PLoS ONE ◽  
2019 ◽  
Vol 14 (9) ◽  
pp. e0222030 ◽  
Author(s):  
Josephine Reismann ◽  
Alessandro Romualdi ◽  
Natalie Kiss ◽  
Maximiliane I. Minderjahn ◽  
Jim Kallarackal ◽  
...  

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