Diapositivo 1

Introduo Medicina I

Professor Doutor Altamiro Pereira

2008/2009

Bayesian Networks as Clinical Decision Support Systems in Medical Settings:

A Review

Turma 22

Introduction

Bayesian Networks

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.

Clinical Decision Support Systems (CDSS) are considered to combine medical knowledge base, patient data and an inference engine to generate case specific advice.[2] (Classen, 1998)

These Bayesian networks can be used to represent the probabilistic relationships and interdependencies among a set of variables, namely diseases and symptoms.

Introduction

Bayesian Networks

CDSS

Medical Knowledge

Artificial Intelligence System (such as Bayesian Networks)

Patient Data

Advice

Main Question

Bayesian Networks

In which healthcare domains and clinical fields are Bayesian networks being used as clinical decision support systems in Medicine?

Specific Objectives

Bayesian Networks

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.

Methods

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Review:

The articles/papers used in systematic review are searched in Medline, ISI Web of Knowledge and Scopus. This literature search is conducted by a conjunction of keywords (and their synonyms) with other words related with variables.

keywords:

- Decision Support Systems, Clinical

- Bayes Theorem

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.

The divergent opinions are solved by a third reviewer and the exclusion causes are registered. It is necessary to evaluate this process reproducibility and to register the exclusion motives.

Finally, a specific formulary is created for data extraction and processed using SPSS.

If possible, a meta-analysis will be applied. The final results are interpreted, discussed and the final article is elaborated.

Variables in Study

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Types of Study (e.g. Experimental vs observational) and Data types (primary data vs secondary data)

Articles information (First authors country affiliation, publication date, institution)

Healthcare domains (emergency, critical care, stroke service)

Clinical fields (diagnosis, therapy, prognosis)

Efficacy, Effectiveness and Efficiency of Bayesians techniques

Inclusion and Exclusion Criteria

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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

Expected Results

Bayesian Networks

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.

Flowchart

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Bibliography

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Tan J, Sheps S (1998). Health Decision Support Systems. Jones & Bartlett Publishers.

Classen DC. Clinical decision support systems to improve clinical practice and quality of care. JAMA. 1998 Oct 21;280(15):1360-1.

Coiera E (2003). The Guide to Health Informatics (2nd Edition). Arnold, London.

Sim I, Sanders GD, McDonald KM. Evidence-based practice for mere mortals: the role of informatics and health services research. J Gen Intern Med. 2002 Apr;17(4):302-8.

Fieschi M, Dufour JC, Staccini P, Gouvernet J, Bouhaddou O. Medical decision support systems: old dilemmas and new paradigms? Methods Inf Med. 2003;42(3):190-8. Erratum in: Methods Inf Med. 2003;42(4):VI.

Miller RA. Medical diagnostic decision support systems--past, present, and future: a threaded bibliography and brief commentary. J Am Med Inform Assoc. 1994 Jan-Feb;1(1):8-27. Erratum in: J Am Med Inform Assoc. 1994 Mar-Apr;1(2):160.

Wong HJ, Legnini MW, Whitmore HH. The diffusion of decision support systems in healthcare: are we there yet? J Healthc Manag. 2000 Jul-Aug;45(4):240-9; discussion 249-53.

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