Authors
Gabriela Ochoa, Lee A Christie, Alexander E Brownlee, Andrew Hoyle
Publication date
2020/1/1
Journal
Artificial intelligence in medicine
Volume
102
Pages
101759
Publisher
Elsevier
Description
Antibiotic resistance is one of the major challenges we face in modern times. Antibiotic use, especially their overuse, is the single most important driver of antibiotic resistance. Efforts have been made to reduce unnecessary drug prescriptions, but limited work is devoted to optimising dosage regimes when they are prescribed. The design of antibiotic treatments can be formulated as an optimisation problem where candidate solutions are encoded as vectors of dosages per day. The formulation naturally gives rise to competing objectives, as we want to maximise the treatment effectiveness while minimising the total drug use, the treatment duration and the concentration of antibiotic experienced by the patient. This article combines a recent mathematical model of bacterial growth including both susceptible and resistant bacteria, with a multi-objective evolutionary algorithm in order to automatically design successful …
Total citations
2020202120222023202444342
Scholar articles
G Ochoa, LA Christie, AE Brownlee, A Hoyle - Artificial intelligence in medicine, 2020