Design and Development of a Compound DSS for Laboratory Research - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 479

publications dealing with application in clinical medicine. Vikram and Karjodkar (Vikram &
Karjodkar, 2009) briefly summarize the history of so called clinical decision support systems
(CDSS) development. According to their findings, there are four main types of CDSS, which
are based on:
 Rule Based Reasoning (RBR) - use of the principle of cause and effect (if-then),
 Case Based Reasoning (CBR) - decision making based on the principle of analogy with
already resolved cases,
 Bayesian believe networks (BBN) - the use of probability theory,
 Artificial neural networks (ANN) - computational model inspired by the structure and
functional aspects of biological neural networks.
Apart from these, there are few examples of the use of uncommon techniques, such as
heuristic algorithms, fuzzy logic (Lingaard et al., 2007), or game theory (Lin et al., 2009). The
following subchapters briefly describe the four main techniques used in diagnostic DSS and
provide examples of use and suitability of these technologies for their use in laboratory
research.
3.1 Rule based reasoning (RBR)
The rule based reasoning uses notation of rules in the "if-then-else" form. This notation can
be extended to define the probability of suitability of the proposed actions. Rule-based
reasoning is mainly used by expert systems that analyze the base of facts and apply the
appropriate rules on the solved situation. The main disadvantage of RBR is laborious and
time-consuming creation of a quality knowledge base.
Kumar (Kumar et al., 2009) describes a hybrid approach, combining case and rule based
reasoning for branch independent CDSS for the intensive care unit (ICU). DSS that use rule
based reasoning are usually limited to use in a specific area, such as cancer, poisoning,
cardiology, etc. It is significantly limiting for multidisciplinary applications such as the DSS
for ICU. The rigidity of the system was eliminated by combination with case based
reasoning. CBR has been chosen as the main decision support technique. CBR uses RBR
subsystem with knowledge base containing common rules for all medical disciplines needed
at the intensive care unit.
In the area of research, the use of rule based reasoning seems to be unusable. The process of
research is characterized by exploring and revealing relationships and rules within the
survey data, therefore it is difficult, or impossible, to define the rules beforehand. Especially
in the early stages of research is the use of rule based reasoning as a tool for decision
support absolutely inconceivable.
3.2 Case based reasoning (CBR)
Case based reasoning is technique of computer-based decision-making which uses the
principle of analogy with already resolved cases. It is based on the premise that the newly
solved problems are often similar to previously solved cases, and therefore the previous
solution can be used in current situation. The fundament of CBR is case repository, so called
case library. It contains a number of previously solved cases, which are used for decision
support. CBR is often used in medical DSS. One possible reason is that the reasoning based
on previous cases is psychologically more easily acceptable than reasoning based on rule
model (Turban et al., 2008).
Ting (Ting et al., 2010) describes a DSS integrating case based reasoning and association
rules mining for decision support in prescribing of medications. According to him, CBR,