scholarly journals How Artificial Intelligence Can Improve Our Understanding of the Genes Associated with Endometriosis: Natural Language Processing of the PubMed Database

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
J. Bouaziz ◽  
R. Mashiach ◽  
S. Cohen ◽  
A. Kedem ◽  
A. Baron ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Text Mining ◽  
Natural Language ◽  
Language Processing ◽  
Data Extraction ◽  
Endometrial Tissue ◽  
Endometrial Cells ◽  
Pubmed Database ◽  
Using Data

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.

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.

scholarly journals Natural Language Processing for Rapid Response to Emergent Diseases: Case Study of Calcium Channel Blockers and Hypertension in the COVID-19 Pandemic

10.2196/20773 ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. e20773 ◽  
Author(s):  
Antoine Neuraz ◽  
Ivan Lerner ◽  
William Digan ◽  
Nicolas Paris ◽  
Rosy Tsopra ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Text Mining ◽  
Natural Language ◽  
Calcium Channel ◽  
Language Processing ◽  
Calcium Channel Blockers ◽  
Structured Data ◽  
Channel Blockers ◽  
Knowledge Model ◽  
Long Term Treatment

Background A novel disease poses special challenges for informatics solutions. Biomedical informatics relies for the most part on structured data, which require a preexisting data or knowledge model; however, novel diseases do not have preexisting knowledge models. In an emergent epidemic, language processing can enable rapid conversion of unstructured text to a novel knowledge model. However, although this idea has often been suggested, no opportunity has arisen to actually test it in real time. The current coronavirus disease (COVID-19) pandemic presents such an opportunity. Objective The aim of this study was to evaluate the added value of information from clinical text in response to emergent diseases using natural language processing (NLP). Methods We explored the effects of long-term treatment by calcium channel blockers on the outcomes of COVID-19 infection in patients with high blood pressure during in-patient hospital stays using two sources of information: data available strictly from structured electronic health records (EHRs) and data available through structured EHRs and text mining. Results In this multicenter study involving 39 hospitals, text mining increased the statistical power sufficiently to change a negative result for an adjusted hazard ratio to a positive one. Compared to the baseline structured data, the number of patients available for inclusion in the study increased by 2.95 times, the amount of available information on medications increased by 7.2 times, and the amount of additional phenotypic information increased by 11.9 times. Conclusions In our study, use of calcium channel blockers was associated with decreased in-hospital mortality in patients with COVID-19 infection. This finding was obtained by quickly adapting an NLP pipeline to the domain of the novel disease; the adapted pipeline still performed sufficiently to extract useful information. When that information was used to supplement existing structured data, the sample size could be increased sufficiently to see treatment effects that were not previously statistically detectable.

scholarly journals Wave2Vec: Vectorizing Electroencephalography Bio-Signal for Prediction of Brain Disease

2018 ◽  
Vol 15 (8) ◽  
pp. 1750 ◽  
Author(s):  
Seonho Kim ◽  
Jungjoon Kim ◽  
Hong-Woo Chun
Keyword(s):  
Artificial Intelligence ◽  
Time Series ◽  
Feature Selection ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Data Analysis ◽  
Natural Language ◽  
Real Number ◽  
Real Time ◽  
Language Processing

Interest in research involving health-medical information analysis based on artificial intelligence, especially for deep learning techniques, has recently been increasing. Most of the research in this field has been focused on searching for new knowledge for predicting and diagnosing disease by revealing the relation between disease and various information features of data. These features are extracted by analyzing various clinical pathology data, such as EHR (electronic health records), and academic literature using the techniques of data analysis, natural language processing, etc. However, still needed are more research and interest in applying the latest advanced artificial intelligence-based data analysis technique to bio-signal data, which are continuous physiological records, such as EEG (electroencephalography) and ECG (electrocardiogram). Unlike the other types of data, applying deep learning to bio-signal data, which is in the form of time series of real numbers, has many issues that need to be resolved in preprocessing, learning, and analysis. Such issues include leaving feature selection, learning parts that are black boxes, difficulties in recognizing and identifying effective features, high computational complexities, etc. In this paper, to solve these issues, we provide an encoding-based Wave2vec time series classifier model, which combines signal-processing and deep learning-based natural language processing techniques. To demonstrate its advantages, we provide the results of three experiments conducted with EEG data of the University of California Irvine, which are a real-world benchmark bio-signal dataset. After converting the bio-signals (in the form of waves), which are a real number time series, into a sequence of symbols or a sequence of wavelet patterns that are converted into symbols, through encoding, the proposed model vectorizes the symbols by learning the sequence using deep learning-based natural language processing. The models of each class can be constructed through learning from the vectorized wavelet patterns and training data. The implemented models can be used for prediction and diagnosis of diseases by classifying the new data. The proposed method enhanced data readability and intuition of feature selection and learning processes by converting the time series of real number data into sequences of symbols. In addition, it facilitates intuitive and easy recognition, and identification of influential patterns. Furthermore, real-time large-capacity data analysis is facilitated, which is essential in the development of real-time analysis diagnosis systems, by drastically reducing the complexity of calculation without deterioration of analysis performance by data simplification through the encoding process.

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