Risk Classification of Food Incidents Using a Risk Evaluation Matrix for Use in Artificial Intelligence-Supported Risk Identification
Sina Röhrs, Sascha Rohn, Yvonne Pfeifer · 2024
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Description
Röhrs, Rohn, and Pfeifer build a scoring matrix to grade food incident reports for use in AI-supported risk spotting. Foodborne illness still strikes one in ten people worldwide each year. Data and smart tools to fight it keep growing. The hard part is using the flood of reports well. Their scheme uses multi-criteria decision analysis to rate each incident. Ratings can feed an AI database that sorts risks on its own.
A set priority level helps surface new or rising threats early. The matrix weighs harm, spread, and other set factors. The authors frame it as groundwork for autonomous risk sorting. Human judgment sets the weights; the machine applies them at scale.
Catalog record
| CREATOR | Sina Röhrs, Sascha Rohn, Yvonne Pfeifer |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 51 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |