Using literature and data to learn Bayesian networks as clinical models of ovarian tumors

Artificial Intelligence in Medicine

Volumn 30, Issue 3, 2004, Pages 257-281

  Antal, Peter ^a   Fannes, Geert ^a   Timmerman, Dirk ^b   Moreau, Yves ^a   De Moor, Bart ^a  

^a Katholieke Universiteit Leuven   (Belgium)

^b UNIVERSITY HOSPITALS LEUVEN   (Belgium)

Author keywords

Bayesian networks; Literature networks; Prior incorporation; Structure learning; Text mining

Indexed keywords

DATA REDUCTION; STATISTICAL METHODS; TUMORS;

BAYESIAN NETWORKS;

ARTIFICIAL INTELLIGENCE;

ARTICLE; BAYES THEOREM; BOOK; CLINICAL STUDY; DISEASE CLASSIFICATION; EVALUATION; INFORMATION; LITERATURE; MEASUREMENT; MULTIVARIATE ANALYSIS; OVARY CANCER; OVARY TUMOR; PRIORITY JOURNAL; SCORING SYSTEM; STATISTICAL ANALYSIS; STATISTICAL MODEL;

ARTIFICIAL INTELLIGENCE; BAYES THEOREM; DATABASES; FEMALE; HUMANS; MATCHED-PAIR ANALYSIS; MEDICAL HISTORY TAKING; NEURAL NETWORKS (COMPUTER); OVARIAN NEOPLASMS; PERIODICALS; PROBABILITY; STATISTICAL DISTRIBUTIONS; TUMOR MARKERS, BIOLOGICAL; ULTRASONOGRAPHY, DOPPLER, COLOR; VOCABULARY, CONTROLLED;

EID: 1842714948     PISSN: 09333657     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.artmed.2003.11.007     Document Type: Article

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