Name
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/MIS.2014.48 | Intelligent Systems, IEEE |
Keywords | Field | DocType |
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,industrial health,intelligent systems,machine learning,meta-analysis,nanomaterial toxicity,risk assessment,safety management | Toxicity,Intelligent decision support system,Computer science,Risk assessment,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
29 | 3 | 1541-1672 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeremy M. Gernand | 1 | 0 | 0.34 |
| Elizabeth A. Casman | 2 | 0 | 0.34 |