A Reinforcement Learning-inspired Estimation of Antibiotic Resistance in Poultry Environments

Antibiotic resistance occurs when bacteria evolve to withstand the effects of antibiotics, reducing the effectiveness of treatment medication. Estimation of antibiotics resistance holds importance, as antibiotic-resistant infections can spread rapidly and knowledge in antibiotic resistance allows healthcare providers to make informed decisions. However, this is a challenging task because there are very limited open data sets to work on, containing systematic measurements. In this work, we propose machine learning-inspired estimation of resistance of five specific types of antibiotics on different poultry environments and created confidence percentages in which tell us how accurate the program is for a specific type of antibiotic. The analysis in this work highlights that a variety of factors impact antibiotic resistance in poultry; however, an informative prediction using a model such as the one proposed in this paper would assist in proper planning and usage of antibiotics in terms of effectiveness and necessity.