Reinforcement Learning informs optimal treatment strategies to limit antibiotic resistance.
Journal
bioRxiv : the preprint server for biology
Titre abrégé: bioRxiv
Pays: United States
ID NLM: 101680187
Informations de publication
Date de publication:
16 Nov 2023
16 Nov 2023
Historique:
pubmed:
31
1
2023
medline:
31
1
2023
entrez:
30
1
2023
Statut:
epublish
Résumé
Antimicrobial resistance was estimated to be associated with 4.95 million deaths worldwide in 2019. It is possible to frame the antimicrobial resistance problem as a feedback-control problem. If we could optimize this feedback-control problem and translate our findings to the clinic, we could slow, prevent or reverse the development of high-level drug resistance. Prior work on this topic has relied on systems where the exact dynamics and parameters were known
Identifiants
pubmed: 36711676
doi: 10.1101/2023.01.12.523765
pmc: PMC9882109
pii:
doi:
Types de publication
Preprint
Langues
eng
Subventions
Organisme : NCI NIH HHS
ID : R37 CA244613
Pays : United States
Organisme : NCI NIH HHS
ID : T32 CA094186
Pays : United States
Organisme : NIGMS NIH HHS
ID : T32 GM007250
Pays : United States