Author/Editor     Chowdhury, S; Bodemar, G; Haug, P; Babič, A; Wigertz, O
Title     Methods for knowledge extraction from a clinical database on liver diseases
Type     članek
Source     Comput Biomed Res
Vol. and No.     Letnik 24, št. 6
Publication year     1991
Volume     str. 530-48
Language     eng
Abstract     We performed exploratory data analysis (EDA) to examine the hidden structure in liver disease data. The purpose was to demonstrate the potential of statistical techniques for extracting knowledge from an active HIS (hospital information system) database with decision support. The goal is to give strong support to the creation of new rules or "tuning" of old rules in the knowledge base. This would facilitate utilization of large patient databases, now commonly available, to help build/update decision support systems for improved patient care. Several statistical techniques were investigated. Stepwise discriminant analysis was found to be a good method in discriminating among different disease classes. Results showed that classification strength of a few (3) variables was similar to all the available (19) variables. Other important issues in the work are treatment of missing values as well as atypical values in medical databases. In estimating missing values we utilized both statistical methods and artificial intelligence approaches. Both these approaches were promising in the estimation of missing values. The study showed that several statistical approaches are possible for knowledge extraction from clinical data collected retrospectively.
Descriptors     CLUSTER ANALYSIS
DECISION SUPPORT TECHNIQUES
DISCRIMINANT ANALYSIS
LIVER DISEASES
MEDICAL INFORMATICS
ARTIFICIAL INTELLIGENCE
CLASSIFICATION
DATABASES, FACTUAL
LIVER DISEASES