Bayesian Networks as Clinical Decision Support
Systems in Medical Settings:
A Review
 Health managers and clinicians are frequently asked to provide
quantifiable information to support their decision, which is not
always easy to obtain.
 Therefore, some artificial intelligence systems are idealized to
support healthcare professionals with responsibility based on the
manipulation of information and knowledge.
Bayesian
can be used
to are
represent
the
 These
Clinical
Decisionnetworks
Support Systems
(CDSS)
considered
probabilistic
and interdependencies
a set an
of
to
combine relationships
medical knowledge
base, patient among
data and
variables, engine
namelyto
diseases
and
symptoms.
inference
generate
case
specific advice.[2] (Classen, 1998)
Medical
Knowledge
Patient
Data
Advice
Artificial Intelligence
System (such as
Bayesian Networks)
In which healthcare domains and
clinical fields are Bayesian networks
being used as clinical decision support
systems in Medicine?
 Identify the healthcare domains and point out which fields
(diagnosis, therapy and prognosis) are usually targeted by BN as
CDSS in real-world clinical practice.
 Discuss the efficacy, effectiveness and efficiency of BN in CDSS
expressed in the included studies.
Review:
The
opinions
by a review
third reviewer
and the
The divergent
articles/papers
usedarein solved
systematic
are searched
in
exclusion
are of
registered.
It isand
necessary
evaluate
this
Medline, causes
ISI Web
Knowledge
Scopus.toThis
literature
process’
anda toconjunction
register the of
exclusion
motives.
search isreproducibility
conducted by
keywords
(and their
synonyms) with other words related with variables.
Finally, a specific formulary is created for data extraction and
processed
using SPSS.
keywords:
- Decision Support Systems, Clinical
- Bayes Theorem
possible,
a meta-analysis will be
If
applied. The final results are
interpreted, discussed and the final article is elaborated.
All the articles are collected using EndNote and are reviewed by
two peers. Initially, these two reviewers analyze the title and the
abstract, registering briefly the causes of non-selection. Then, the
chosen articles are read integrally and are applied the inclusion
and exclusion criteria, previously elaborated.
 Types of Study (e.g. Experimental vs observational) and
Data types (primary data vs secondary data)
 Articles’ information (First author’s country affiliation,
publication date, institution)
 Healthcare domains (emergency, critical care, stroke
service…)
 Clinical fields (diagnosis, therapy, prognosis)
 Efficacy,
techniques
Effectiveness
and
Efficiency
of
Bayesian’s
Inclusion Criteria:
 Applied to diagnosis or prognosis or therapeutic
related to Bayes theorem
 Include results
 Paper provides details so that the study can be
reproduced
 Written in English
Exclusion Criteria:
 Meta-analysis and reviews
 Not applied to humans
 Most of the articles found refer to Diagnostic tests of CDSS
based on BN.
 CDSS based on BN are more frequently used in diagnosis.
 CDSS based on BN have been applied in Rapid Assessment Unit
and in Emergency.
 CDSS based on BN are efficacious and effective but not efficient.
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