34,335 research outputs found
Clinical evidence framework for Bayesian networks
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. The evidence framework is capable of presenting supporting and conflicting evidence, and evidence associated with relevant but excluded factors. It also allows the completeness of the evidence to be queried. We illustrate this framework using a BN that has been previously developed to predict acute traumatic coagulopathy, a potentially fatal disorder of blood clotting, at early stages of trauma care
Learning from medical data streams: an introduction
Clinical practice and research are facing a new challenge created by the rapid growth of health information science and technology, and the complexity and volume of biomedical data. Machine learning from medical data streams is a recent area of research that aims to provide better knowledge extraction and evidence-based clinical decision support in scenarios where data are produced as a continuous flow. This year's edition of AIME, the Conference on Artificial Intelligence in Medicine, enabled the sound discussion of this area of research, mainly by the inclusion of a dedicated workshop. This paper is an introduction to LEMEDS, the Learning from Medical Data Streams workshop, which highlights the contributed papers, the invited talk and expert panel discussion, as well as related papers accepted to the main conference
Defining a robust biological prior from Pathway Analysis to drive Network Inference
Inferring genetic networks from gene expression data is one of the most
challenging work in the post-genomic era, partly due to the vast space of
possible networks and the relatively small amount of data available. In this
field, Gaussian Graphical Model (GGM) provides a convenient framework for the
discovery of biological networks. In this paper, we propose an original
approach for inferring gene regulation networks using a robust biological prior
on their structure in order to limit the set of candidate networks.
Pathways, that represent biological knowledge on the regulatory networks,
will be used as an informative prior knowledge to drive Network Inference. This
approach is based on the selection of a relevant set of genes, called the
"molecular signature", associated with a condition of interest (for instance,
the genes involved in disease development). In this context, differential
expression analysis is a well established strategy. However outcome signatures
are often not consistent and show little overlap between studies. Thus, we will
dedicate the first part of our work to the improvement of the standard process
of biomarker identification to guarantee the robustness and reproducibility of
the molecular signature.
Our approach enables to compare the networks inferred between two conditions
of interest (for instance case and control networks) and help along the
biological interpretation of results. Thus it allows to identify differential
regulations that occur in these conditions. We illustrate the proposed approach
by applying our method to a study of breast cancer's response to treatment
Bayesian multi-modal model comparison: a case study on the generators of the spike and the wave in generalized spike–wave complexes
We present a novel approach to assess the networks involved in the generation of spontaneous pathological brain activity based on multi-modal imaging data. We propose to use probabilistic fMRI-constrained EEG source reconstruction as a complement to EEG-correlated fMRI analysis to disambiguate between networks that co-occur at the fMRI time resolution. The method is based on Bayesian model comparison, where the different models correspond to different combinations of fMRI-activated (or deactivated) cortical clusters. By computing the model evidence (or marginal likelihood) of each and every candidate source space partition, we can infer the most probable set of fMRI regions that has generated a given EEG scalp data window. We illustrate the method using EEG-correlated fMRI data acquired in a patient with ictal generalized spike–wave (GSW) discharges, to examine whether different networks are involved in the generation of the spike and the wave components, respectively. To this effect, we compared a family of 128 EEG source models, based on the combinations of seven regions haemodynamically involved (deactivated) during a prolonged ictal GSW discharge, namely: bilateral precuneus, bilateral medial frontal gyrus, bilateral middle temporal gyrus, and right cuneus. Bayesian model comparison has revealed the most likely model associated with the spike component to consist of a prefrontal region and bilateral temporal–parietal regions and the most likely model associated with the wave component to comprise the same temporal–parietal regions only. The result supports the hypothesis of different neurophysiological mechanisms underlying the generation of the spike versus wave components of GSW discharges
Reliable Uncertain Evidence Modeling in Bayesian Networks by Credal Networks
A reliable modeling of uncertain evidence in Bayesian networks based on a
set-valued quantification is proposed. Both soft and virtual evidences are
considered. We show that evidence propagation in this setup can be reduced to
standard updating in an augmented credal network, equivalent to a set of
consistent Bayesian networks. A characterization of the computational
complexity for this task is derived together with an efficient exact procedure
for a subclass of instances. In the case of multiple uncertain evidences over
the same variable, the proposed procedure can provide a set-valued version of
the geometric approach to opinion pooling.Comment: 19 page
Post-mortem toxicology: A pilot study to evaluate the use of a Bayesian network to assess the likelihood of fatality
The challenge of interpreting post-mortem drug concentrations is well documented and relies on appropriate sample collection, knowledge of case circumstances as well as reference to published tables of data, whilst taking into account the known issues of post-mortem drug redistribution and tolerance. Existing published data has evolved from simple data tables to those now including sample origin and single to poly drug use, but additional information tends to be specific to those reported in individual case studies. We have developed a Bayesian network framework to assign a likelihood of fatality based on the contribution of drug concentrations whilst taking into account the pathological findings. This expert system has been tested against casework within the coronial jurisdiction of Sunderland, UK. We demonstrate in this pilot study that the Bayesian network can be used to proffer a degree of confidence in how deaths may be reported in cases when drugs are implicated. It has also highlighted the potential for deaths to be reported according to the pathological states at post-mortem when drugs have a significant contribution that may have an impact on mortality statistics. The Bayesian network could be used as complementary approach to assist in the interpretation of post-mortem drug concentrations
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