Learning from biomedical time series through the integration of qualitative models and fuzzy systems

Artificial Intelligence in Medicine

Volumn 21, Issue 1-3, 2001, Pages 215-220

  Bellazzi, R ^a   Guglielmann, R ^b   Ironi, L ^b  

^a UNIVERSITY OF PAVIA   (Italy)

^b CNR   (Italy)

Author keywords

Fuzzy logic systems; Nonlinear system identification; Qualitative reasoning; Thiamine kinetics

Indexed keywords

APPROXIMATION THEORY; DRUG THERAPY; FUZZY SETS; MEDICAL APPLICATIONS; PHYSIOLOGICAL MODELS; TIME SERIES ANALYSIS; VITAMINS;

NONLINEAR SYSTEM IDENTIFICATION; QUALITATIVE REASONING;

LEARNING SYSTEMS;

THIAMINE;

ARTICLE; COMPARTMENT MODEL; DYNAMICS; INTESTINE; LOGIC; METHODOLOGY; MODEL; NONLINEAR SYSTEM; PRIORITY JOURNAL; SIMULATION;

ARTIFICIAL INTELLIGENCE; DIGESTIVE SYSTEM; FUZZY LOGIC; HUMANS; MEDICAL INFORMATICS COMPUTING; THIAMINE;

EID: 0035177331     PISSN: 09333657     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0933-3657(00)00088-9     Document Type: Article

References (9)

1. How to improve fuzzy-neural system modeling by means of qualitative simulation
2. Qualitative models and Fuzzy Systems: an integrated approach for learning from data
3. Fuzzy basis functions: comparison with other basis functions
4. In vivo study of the kinetics of thiamine and its phosphates in the deafferented rat cerebellum
5. Thiamine content and turnover rates of some rats nervous regions using labeled thiamine as tracer
6. Effect on ethanol on the in vivo kinetics of thiamine phosphorilation and dephosphorilation in different organs-II