scholarly journals Natural language processing of prehospital emergency medical services trauma records allows for automated characterization of treatment appropriateness

2020 ◽  
Vol 88 (5) ◽  
pp. 607-614 ◽  
Author(s):  
Christopher J. Tignanelli ◽  
Greg M. Silverman ◽  
Elizabeth A. Lindemann ◽  
Alexander L. Trembley ◽  
Jon C. Gipson ◽  
...  
2020 ◽  
Vol 231 (4) ◽  
pp. S305-S306
Author(s):  
Jacob Andrew Swann ◽  
Greg M. Silvermann ◽  
Elizabeth A. Lindemann ◽  
Lori Boland ◽  
Jon C. Gibson ◽  
...  

2021 ◽  
pp. 1-13
Author(s):  
Lamiae Benhayoun ◽  
Daniel Lang

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.


Diabetologia ◽  
2019 ◽  
Vol 62 (10) ◽  
pp. 1868-1879 ◽  
Author(s):  
Melanie Villani ◽  
Arul Earnest ◽  
Karen Smith ◽  
Dimitra Giannopoulos ◽  
Georgia Soldatos ◽  
...  

2007 ◽  
Vol 32 (4) ◽  
pp. 415-421 ◽  
Author(s):  
N.A.R. Nik Hisamuddin ◽  
M. Shah Hamzah ◽  
C. James Holliman

2016 ◽  
Vol 31 (6) ◽  
pp. 608-613 ◽  
Author(s):  
Bruno Schnegg ◽  
Mathieu Pasquier ◽  
Pierre-Nicolas Carron ◽  
Bertrand Yersin ◽  
Fabrice Dami

AbstractIntroductionThe concept of response time with minimal interval is intimately related to the practice of emergency medicine. The factors influencing this time interval are poorly understood.ProblemIn a process of improvement of response time, the impact of the patient’s age on ambulance departure intervals was investigated.MethodThis was a 3-year observational study. Departure intervals of ambulances, according to age of patients, were analyzed and a multivariate analysis, according to time of day and suspected medical problem, was performed.ResultsA total of 44,113 missions were included, 2,417 (5.5%) in the pediatric group. Mean departure delay for the adult group was 152.9 seconds, whereas it was 149.3 seconds for the pediatric group (P =.018).ConclusionA statistically significant departure interval difference between missions for children and adults was found. The difference, however, probably was not significant from a clinical point of view (four seconds).SchneggB, PasquierM, CarronPN, YersinB, DamiF. Prehospital Emergency Medical Services departure interval: does patient age matter?Prehosp Disaster Med. 2016;31(6):608–613.


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