By Ulf Grenander
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Rule-based systems may be expected to perform well for problems that cannot be modeled using causality as a guiding principle, or when a problem is too complicated to be modeled as a causal graph. The experiments conﬁrmed that a rule-based system can have difﬁculty with dealing with missing values: around 35% of the patients remained unclassiﬁed by rule-based system, while in Hepar II only 2% of patients remained unclassiﬁed. This behavior is due to the semantics of negation by absence, and is in fact a deliberate design choice in rule-based systems.
It is recommended, if the expert is comfortable with this, to record the sessions with the expert because it is often hard to process all the medical knowledge that is provided by a domain expert during a meeting. It is also recommended to organize brainstorming sessions with the participation of knowledge engineers and medical experts who are not directly involved in building the model. es/~elvira. 4 In causal models, most of the inﬂuences are positive, because usually the presence of the cause increases the probability of the effect’s presence.
Elvira is written in Java and is, therefore, fully platform-independent. 3 Model construction There are two basic approaches to construct Bayesian network models: manual building based purely on human expert knowledge and automatic learning of the structure and the numerical parameters from data. , prior and conditional probability distributions for all the nodes, were learned from the Hepar database. The Hepar database was created in 1990 and thoroughly maintained since then by Dr. Wasyluk at the Gastroentorogical Clinic of the Institute of Food and Feeding in Warsaw.
Abstract Inference [math. stats.] by Ulf Grenander