Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Explicit Evidence for Prognostic Bayesian Network Models
Date
2014-01-01
Author
Yet, Barbaros
Tai, Nigel
Marsh, William
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
126
views
0
downloads
Cite This
Many prognostic models are not adopted in clinical practice regardless of their reported accuracy. Doubts about the basis of the model is considered to be a major reason for this as the evidence behind clinical models is often not clear to anyone other than their developers. We propose a framework for representing the evidence behind Bayesian networks (BN) developed for prognostic decision support. The aim of this evidence framework is to be able to present all the evidence alongside the BN itself. We illustrate this framework by a BN developed with clinical evidence to predict coagulation disorders in trauma care.
Subject Keywords
Bayesian Networks
,
Clinical Evidence
,
Knowledge Engineering
,
Prognostic Models
,
Clinical Decision Support
URI
https://hdl.handle.net/11511/56295
DOI
https://doi.org/10.3233/978-1-61499-432-9-53
Conference Name
25th European Medical Informatics Conference (MIE)
Collections
Graduate School of Informatics, Conference / Seminar
Suggestions
OpenMETU
Core
Clinical evidence framework for Bayesian networks
Yet, Barbaros; Tai, Nigel R. M.; Marsh, D. William R. (2017-01-01)
There is poor uptake of prognostic decision support models by clinicians regardless of their accuracy. There is evidence that this results from doubts about the basis of the model as the evidence behind clinical models is often not clear to anyone other than their developers. In this paper, we propose a framework for representing the evidence-base of a Bayesian network (BN) decision support model. The aim of this evidence framework is to be able to present all the clinical evidence alongside the BN itself. ...
Random effects’ distribution assumption on joint mixed modelling
Özdemir, Celal Oğuz; Kalaylıoğlu Akyıldız, Zeynep Işıl; Department of Statistics (2018)
Joint mixed model is an appealing approach in medical research where it is critical to estimate the odds of a fatal complication that occurs to a patient given the covariate profile such as a risk factor observed over time. For this kind of estimation, joint mixed model is used. In the standard Bayesian analysis of the model, the error variance and random effects’ variance-covariance matrix are apriori modeled independently with Inverse-Gamma and Inverse-Wishart distributions respectively. Recently however,...
Decision support system for Warfarin therapy management using Bayesian networks
Yet, Barbaros; Raharjo, Hendry; Lifvergren, Svante; Marsh, William; Bergman, Bo (2013-05-01)
Warfarin therapy is known as a complex process because of the variation in the patients' response. Failure to deal with such variation may lead to death as a result of thrombosis or bleeding. The possible sources of variation such as concomitant illnesses and drug interactions have to be investigated by the clinician in order to deal with the variation. This paper describes a decision support system (DSS) using Bayesian networks for assisting clinicians to make better decisions in Warfarin therapy managemen...
Application of Dempster-Schafer Method in Family-Based Association Studies
Rajabli, Farid; Goktas, Unal; İnan, Gül (2013-07-01)
In experiments designed for family-based association studies, methods such as transmission disequilibrium test require large number of trios to identify single-nucleotide polymorphisms associated with the disease. However, unavailability of a large number of trios is the Achilles' heel of many complex diseases, especially for late-onset diseases. In this paper, we propose a novel approach to this problem by means of the Dempster-Shafer method. The simulation studies show that the Dempster-Shafer method has ...
Implementation of a fast simulation tool for the analysis of contrast mechanisms in HMMDI and enhancement of the SNR in the experimental set-up
İrgin, Ümit; Gençer, Nevzat Güneri; Top, Can Barış; Department of Electrical and Electronics Engineering (2021-9-06)
Clinical method for breast tumor detection is Mammography (X-rays), which have limitations and may yield inaccurate results. Alternative novel techniques are required to characterize the breast tissues and extract accurate information for identification of malignancies in the tissue. Harmonic Motion Microwave Doppler Imaging (HMMDI), which enhances hybridizing microwave signals and ultrasound techniques, has been recently proposed for detection of tumors in the tissue. In HMMDI method, the data is a combina...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
B. Yet, N. Tai, and W. Marsh, “Explicit Evidence for Prognostic Bayesian Network Models,” Istanbul, TURKEY, 2014, vol. 205, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/56295.