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
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

Auteurs

Davis T Weaver (DT)

Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA.
Translational Hematology Oncology Research, Cleveland Clinic, Cleveland OH, 44106, USA.

Eshan S King (ES)

Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA.
Translational Hematology Oncology Research, Cleveland Clinic, Cleveland OH, 44106, USA.

Jeff Maltas (J)

Translational Hematology Oncology Research, Cleveland Clinic, Cleveland OH, 44106, USA.

Jacob G Scott (JG)

Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA.
Translational Hematology Oncology Research, Cleveland Clinic, Cleveland OH, 44106, USA.
Department of Physics, Case Western Reserve University, Cleveland, OH, 44106, USA.

Classifications MeSH