Application of artificial neural network modelling to identify severely ill patients whose aminoglycoside concentrations are likely to fall below therapeutic concentrations

Journal of Clinical Pharmacy and Therapeutics

Volumn 28, Issue 5, 2003, Pages 425-432

  Yamamura, S ^a   Takehira, R ^a   Kawada, K ^a   Nishizawa, K ^b   Katayama, S ^b   Hirano, M ^b   Momose, Y ^a  

^a TOHO UNIVERSITY   (Japan)

^b NIPPON MEDICAL SCHOOL HOSPITAL   (Japan)

Author keywords

Aminoglycoside antibiotics; Artificial neural network; Covariate modelling; Pharmacokinetic modelling; Severely ill patients

Indexed keywords

AMIKACIN; AMIKAMYCIN; AMINOGLYCOSIDE ANTIBIOTIC AGENT; HABEKACIN;

ADULT; AGED; ARTICLE; ARTIFICIAL NEURAL NETWORK; DISEASE SEVERITY; DRUG BLOOD LEVEL; HUMAN; INTENSIVE CARE; MAJOR CLINICAL STUDY; MODEL; RISK ASSESSMENT; SENSITIVITY AND SPECIFICITY;

AMIKACIN; AMINOGLYCOSIDES; ANTI-BACTERIAL AGENTS; ARTIFICIAL INTELLIGENCE; DIBEKACIN; HUMANS; INTENSIVE CARE UNITS; LOGISTIC MODELS; NEURAL NETWORKS (COMPUTER); PREDICTIVE VALUE OF TESTS; REFERENCE VALUES; SEVERITY OF ILLNESS INDEX;

EID: 0242361771     PISSN: 02694727     EISSN: None     Source Type: Journal    
DOI: 10.1046/j.0269-4727.2003.00514.x     Document Type: Article

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