Su1920 DETECTION AND CHARACTERIZATION OF EXTRA-INTESTINAL MANIFESTATIONS OF IBD IN CLINICAL OFFICE NOTES USING NATURAL LANGUAGE PROCESSING

2020 ◽  
Vol 158 (6) ◽  
pp. S-702
Author(s):  
Ryan Stidham ◽  
Deahan Yu ◽  
Shibamouli Lahiri ◽  
Vinod Vydiswaran
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing

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 Characterization of patients with advanced chronic pancreatitis using natural language processing of radiology reports

PLoS ONE ◽  
2020 ◽  
Vol 15 (8) ◽  
pp. e0236817
Author(s):  
Fagen Xie ◽  
Qiaoling Chen ◽  
Yichen Zhou ◽  
Wansu Chen ◽  
Jemianne Bautista ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Chronic Pancreatitis ◽  
Natural Language ◽  
Language Processing ◽  
Radiology Reports

scholarly journals An interactive dashboard to track themes, development maturity, and global equity in clinical artificial intelligence research

2021 ◽  
Author(s):  
Joe Zhang ◽  
Stephen Whebell ◽  
Jack Gallifant ◽  
Sanjay Budhdeo ◽  
Heather Mattie ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Natural Language ◽  
Real Time ◽  
Language Processing ◽  
Artificial Intelligence Research ◽  
End To End ◽  
Monitoring Progress

The global clinical artificial intelligence (AI) research landscape is constantly evolving, with heterogeneity across specialties, disease areas, geographical representation, and development maturity. Continual assessment of this landscape is important for monitoring progress. Taking advantage of developments in natural language processing (NLP), we produce an end-to-end NLP pipeline to automate classification and characterization of all original clinical AI research on MEDLINE, outputting real-time results to a public, interactive dashboard (https://aiforhealth.app/).

Lexical Challenges in the Intersection of Applied Linguistics and ANLP

2012 ◽  
pp. 50-72 ◽  
Author(s):  
Scott Jarvis
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Language Use ◽  
Applied Linguistics ◽  
Lexical Diversity ◽  
Language Knowledge

The investigation of natural language processing in the field of Applied Linguistics is pursued with both theoretical and practical aims, such as arriving at a clearer understanding of the nature of language knowledge, the rules that govern its use, how it is acquired, how unique it is to individual speakers, and how it can best be taught to learners. The purpose of this chapter is to draw attention to some of the prominent areas of overlap between Applied Linguistics and ANLP, highlighting the problems they face in relation to the characterization of lexical deployment, and focusing particularly on challenges related to the measurement of lexical diversity and the representation of the unique lexical signatures of individual samples of natural language use. The bulk of the chapter is devoted to describing preliminary solutions to these challenges.

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