DocumentCode
14074
Title
Machine Learning for Nanomaterial Toxicity Risk Assessment
Author
Gernand, Jeremy M. ; Casman, Elizabeth A.
Volume
29
Issue
3
fYear
2014
fDate
May-June 2014
Firstpage
84
Lastpage
88
Abstract
Many questions about the mechanisms of nanomaterial toxicity are unanswered and an applicable general theory of nanomaterial toxicity doesn´t seem to be on the horizon. To help with this problem, the authors use machine learning algorithms with quantitative analytical capabilities in a meta-analysis of carbon nanotube pulmonary toxicity studies. Such analyses can identify the material varieties most likely to be the riskiest and guide future development towards those most likely to pose the least risk.
Keywords
carbon nanotubes; learning (artificial intelligence); risk management; toxicology; carbon nanotube pulmonary toxicity studies; machine learning algorithms; material varieties; meta-analysis; nanomaterial toxicity risk assessment; quantitative analytical capabilities; Carbon nanotubes; Learning systems; Machine learning; Nanobioscience; Nanomaterials; Proteins; industrial health; intelligent systems; machine learning; meta-analysis; nanomaterial toxicity; risk assessment; safety management;
fLanguage
English
Journal_Title
Intelligent Systems, IEEE
Publisher
ieee
ISSN
1541-1672
Type
jour
DOI
10.1109/MIS.2014.48
Filename
6871719
Link To Document