Machine learning: Novel bioinformatics approaches for combating antimicrobial resistance

Current Opinion in Infectious Diseases

Volumn 30, Issue 6, 2017, Pages 511-517

  Macesic, Nenad ^a,b,c   Polubriaginof, Fernanda ^b   Tatonetti, Nicholas P ^b  

^a Department of Medicine   (United States)

^b Columbia University ^*  (United States)

^c AUSTIN HEALTH   (Australia)

Author keywords

antimicrobial resistance; antimicrobial susceptibility testing; artificial intelligence; bacteria; machine learning

Indexed keywords

ANTIINFECTIVE AGENT;

ANTIBIOTIC RESISTANCE; ANTIBIOTIC SENSITIVITY; ANTIMICROBIAL THERAPY; BIOINFORMATICS; ELECTRONIC HEALTH RECORD; GENOTYPE PHENOTYPE CORRELATION; MACHINE LEARNING; NEXT GENERATION SEQUENCING; NONHUMAN; REVIEW; BACTERIAL INFECTION; BIOLOGY; HUMAN; MICROBIAL SENSITIVITY TEST; MICROBIOLOGY;

ANTI-BACTERIAL AGENTS; BACTERIAL INFECTIONS; COMPUTATIONAL BIOLOGY; DRUG RESISTANCE, BACTERIAL; HUMANS; MACHINE LEARNING; MICROBIAL SENSITIVITY TESTS;

EID: 85033777988     PISSN: 09517375     EISSN: 14736527     Source Type: Journal    
DOI: 10.1097/QCO.0000000000000406     Document Type: Review

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