scholarly journals Computational Exposure Science: An Emerging Discipline to Support 21st-Century Risk Assessment

2016 ◽  
Vol 124 (6) ◽  
pp. 697-702 ◽  
Author(s):  
Peter P. Egeghy ◽  
Linda S. Sheldon ◽  
Kristin K. Isaacs ◽  
Halûk Özkaynak ◽  
Michael-Rock Goldsmith ◽  
...  
Keyword(s):  
Risk Assessment ◽  
21St Century ◽  
Exposure Science

scholarly journals Diabetic Patients Who are Amateur Divers

2018 ◽  
Vol 64 (3) ◽  
pp. 39-44
Author(s):  
Daria Ługowska ◽  
Tomasz Ługowski ◽  
Olga Krzywińska ◽  
Mariusz Kozakiewicz ◽  
Paweł Grzelakowski
Keyword(s):  
Diabetes Mellitus ◽  
Risk Assessment ◽  
21St Century ◽  
Diabetic Patients ◽  
Sport Practice ◽  
High Prevalence ◽  
Prevalence Of Diabetes Mellitus

Abstract Diabetes is an “epidemic of the 21st century” and it is estimated that in 2011 6.7% of Poland’s population suffered from diabetes, one quarter of which was unaware of the disease. Such a high prevalence of diabetes mellitus results in an increasing number of physicians being confronted with the problem of qualifying patient with diabetes to partake in amateur sports, including diving. This publication presents the most important information concerning risk assessment, admission of diabetic patients to amateur diving and some proposed protocols of management before and during sport practice.

Toxicity Testing in the 21st Century: Implications for Human Health Risk Assessment

Risk Analysis ◽  
2009 ◽  
Vol 29 (4) ◽  
pp. 474-479 ◽  
Author(s):  
Daniel Krewski ◽  
Melvin E. Andersen ◽  
Ellen Mantus ◽  
Lauren Zeise
Keyword(s):  
Risk Assessment ◽  
Health Risk ◽  
Human Health ◽  
Health Risk Assessment ◽  
21St Century ◽  
Human Health Risk ◽  
Toxicity Testing ◽  
Human Health Risk Assessment

scholarly journals FutureTox: Building the Road for 21st Century Toxicology and Risk Assessment Practices

2013 ◽  
Vol 137 (2) ◽  
pp. 269-277 ◽  
Author(s):  
J. Craig Rowlands ◽  
Miriam Sander ◽  
James S. Bus ◽  
Keyword(s):  
Risk Assessment ◽  
21St Century ◽  
Assessment Practices ◽  
The Road

Cardiovascular Risk Assessment in the 21st Century

JAMA ◽  
2012 ◽  
Vol 308 (8) ◽  
pp. 816 ◽  
Author(s):  
J. Michael Gaziano ◽  
Peter W. F. Wilson
Keyword(s):  
Risk Assessment ◽  
Cardiovascular Risk ◽  
21St Century ◽  
Cardiovascular Risk Assessment

scholarly journals Automatic Document Classification for Environmental Risk Assessment

2014 ◽  
Author(s):  
Kyle Painter ◽  
Steven Dutton ◽  
Elizabeth O Owens ◽  
Lyle Burgoon
Keyword(s):  
Risk Assessment ◽  
Environmental Risk ◽  
Environmental Risk Assessment ◽  
Environmental Protection Agency ◽  
Single Class ◽  
Potential Health ◽  
Exposure Science ◽  
Human Labor ◽  
Science Assessments ◽  
Class Models

Motivation: In environmental risk assessment, information about potential health risks of chemicals released into the environment is compiled and distilled for use in informing public policy. The U.S. Environmental Protection Agency (EPA) produces Integrated Science Assessments (ISA) that provide a review of literature on air pollutants, including nitrogen oxides (NOx). That review process currently requires much human labor to evaluate thousands of potentially-relevant documents published each year, a problem this study seeks to alleviate by using automated topic classification methods. Results: For this study, abstracts and titles of scientific documents about NOx were labeled by subject matter experts in four domains relevant to ISAs: toxicology, atmospheric science, epidemiology, and exposure science. In addition, documents not relevant to the four domains were included to simulate the background literature that we want to filter out of consideration. The labeled documents were used to train models using a Naive Bayes Multinomial classifier, via the Weka data mining platform. Separate tests were performed using multi-class or single-class models, and including background literature or not including it. For the multi-class models, recall (% of all documents in a class that are classified correctly) for scientific domains ranged between 74% and 94%, with precision (% of classified documents that are in the desired class) between 38% and 93%, with models created with background literature performing worse than models without the background documents. Single-class models had precision that ranged from 31% to 90%, and recall that ranged from 84% to 98%, with better precision for models not using background literature, but better overall recall for models using background literature. Single-class models generally performed better than multi-class models in recall, though multi-class models without the background screen tended to be best for precision.

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