Antilogic, a new supervised machine learning software for the automatic interpretation of antibiotic susceptibility testing in clinical microbiology: proof-of-concept on three frequently isolated bacterial species

Andriamiharimamy Rajaonison (1, 2) , Stéphanie Le Page (3, 1) , Thomas Maurin (1) , Hervé Chaudet (4, 2) , Didier Raoult (1, 2) , Sophie Alexandra Baron (1, 2) , Jean-Marc Rolain (1, 2)

1 MEPHI - Microbes évolution phylogénie et infections
2 IHU Marseille - Institut Hospitalier Universitaire Méditerranée Infection
3 AMU PHARM - Aix-Marseille Université - Faculté de pharmacie
4 VITROME - Vecteurs - Infections tropicales et méditerranéennes

Domains: Life Sciences [q-bio]; Microbiology and Parasitology; Bacteriology; Life Sciences [q-bio]; Microbiology and Parasitology; Virology; Life Sciences [q-bio]; Microbiology and Parasitology; Parasitology; Life Sciences [q-bio]; Human health and pathology; Cardiology and cardiovascular system; Life Sciences [q-bio]; Human health and pathology; Emerging diseases; Life Sciences [q-bio]; Human health and pathology; Infectious diseases

Andriamiharimamy Rajaonison, Stéphanie Le Page, Thomas Maurin, Hervé Chaudet, Didier Raoult, et al.. Antilogic, a new supervised machine learning software for the automatic interpretation of antibiotic susceptibility testing in clinical microbiology: proof-of-concept on three frequently isolated bacterial species. Clinical Microbiology and Infection, 2022, ⟨10.1016/j.cmi.2022.03.035⟩. ⟨hal-03678890⟩