Personalized AI Model for Antibiotics Prescribing

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Antibiotics are widely used for treating patients with infectious diseases or preventing some types of bacterial infections. To ensure the effectiveness of antibiotic therapy, susceptibility tests are used to support antibiotic selection. However, antibiotic susceptibility tests routinely take 2 days to turn around and only when bacterial infection can be confirmed (~50%). As there is also limited understanding of an individual's risk factors for resistance, this substantially delays appropriate therapy, drives pressures on antimicrobial resistance through the empiric use of broad-spectrum antibiotics, and increases unnecessary costs for patients. Additionally, many risks associated with the use of antibiotics need to be carefully considered, such as adverse side effects, complications, the development of antibiotic resistance, and negative interactions with other medications patients are taking. This project aims at developing a personalized machine-learning-based model with the exposition of uncertainty to guide antibiotics prescription, based on a huge, linked dataset of primary and secondary care records, the systemwide HDR-UK dataset. Focusing on clinical decision support, we will train an antibiotic resistance prediction model and investigate what modifiable risk factors predict antibiotic resistance and why, how the resistance changes along with time, whether other medications affect the effectiveness of antibiotics and how, to inform health services and policy research. Based on the model and answers to these questions, we will enable prescribing algorithms that give individualized antibiotic prescription recommendations for patients. Finally, the model and algorithms will be carefully assessed, doctors view about recommendations made by such systems will be investigated, and an antimicrobial stewardship dashboard will be co-designed with clinicians.