Identifying Pathogens of Foodborne Diseases with Machine Learning

[Objective] This paper introduces external data to enhance the word vector representation of exposure foods, and then uses machine learning methods to identify foodborne disease pathogens. [Methods] First, we extracted space, time, patient information, exposure information from foodborne disease cases as features to identify foodborne disease pathogens. Then, we used word vector representation technology integrating domain knowledge to embed foodborne disease exposure foods. Third, we utilized XGBoost machine learning model to examine the correlation among features, and found several important foodborne disease pathogens. [Results] The proposed method yielded more accurate word vector representation of exposure foods than those of the traditional models. It also achieved 68% precision and recall on identifying four important foodborne disease pathogens: Salmonella, Escherichia coli, Vibrio parahaemolyticus and Norovirus, which provides some auxiliary diagnosis and treatment for the patients. [Limitations] We only analyzed four major foodborne disease pathogens. [Conclusions] The proposed method could improve the control of foodborne diseases. © 2023 Chin J Gen Pract. All rights reserved.