Constructing Bayesian networks for medical diagnosis from incomplete and partially correct statistics

Nikovski, Daniel

doi:10.1109/69.868904

Cited by 157 publications

(93 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Recently, Bayesian networks are increasingly used in the representation and interpretation of retrieval problems applied in the medical field. This is argued by its benefits in managing the uncertainty that appears in such types of problems 14 .…”

Section: Retrieval Applied In the Medical Fieldmentioning

confidence: 99%

A new similarity measure based on Bayesian Network signature correspondence for brain tumors cases retrieval

Yazid¹,

Kalti²,

BenAmara³

2014

IJCIS

View full text Add to dashboard Cite

Case retrieval constitutes an interesting area of research which contributes to the evolution of several domains. The similarity measure module is a fundamental step in the retrieval process which affects remarkably on a retrieval system. In this context, we suggest in this paper a similarity measure applied to brain tumor cases retrieval. The rationale behind the proposed measure consists in quantifying the diagnosis correspondence followed by a clinician while comparing two medical cases. Our idea is characterized by the use of the Bayesian inference in the formulation of the proposed measure. The Bayesian network is applied in the classification task and it describes the decision-making process of a radiologist facing a tumor. The proposed similarity algorithm is based essentially on graph correspondence based on signature nodes comparison from the Bayesian classifiers. experiments were directed to compare the performance of the proposed similarity measure method with classical methods of similarity quantification. The performance indices of our proposition are promising.

show abstract

Section: Retrieval Applied In the Medical Fieldmentioning

confidence: 99%

A new similarity measure based on Bayesian Network signature correspondence for brain tumors cases retrieval

Yazid¹,

Kalti²,

BenAmara³

2014

IJCIS

View full text Add to dashboard Cite

show abstract

“…Here, φ 0 is responsible for the leak probabilities P(S|nodisease), to account for the missing disease information and other sources of error [21,23]. In the following, we assume that local sign fields are sufficient to produce an accurate representation of the leak probabilities, i.e., φ 0 = exp( i K 0 i S i ).…”

Section: Learning the Model: Maximum Entropy Principlementioning

confidence: 99%

“…An essential simplifying assumption in these studies was that only one disease is behind the findings (exclusive diseases assumption), otherwise, the diseases act independently on the symptoms (causal independence assumption). Among recent developments, we should mention Bayesian belief networks, which provide a probabilistic framework to study sign-disease dependencies [20][21][22][23]. These models are represented by tables of conditional probabilities that show how the state of a node (sign or disease) variable in an acyclic directed graph depends on the state of the parent variables.…”

Section: Introductionmentioning

confidence: 99%

Toward First Principle Medical Diagnostics: On the Importance of Disease-Disease and Sign-Sign Interactions

Ramezanpour

Mashaghi

2017

Front. Phys.

View full text Add to dashboard Cite

A fundamental problem in medicine and biology is to assign states, e.g., healthy or diseased, to cells, organs or individuals. State assignment or making a diagnosis is often a nontrivial and challenging process and, with the advent of omics technologies, the diagnostic challenge is becoming more and more serious. The challenge lies not only in the increasing number of measured properties and dynamics of the system (e.g., cell or human body) but also in the co-evolution of multiple states and overlapping properties, and degeneracy of states. We develop, from first principles, a generic rational framework for state assignment in cell biology and medicine, and demonstrate its applicability with a few simple theoretical case studies from medical diagnostics. We show how disease-related statistical information can be used to build a comprehensive model that includes the relevant dependencies between clinical and laboratory findings (signs) and diseases. In particular, we include disease-disease and sign-sign interactions and study how one can infer the probability of a disease in a patient with given signs. We perform comparative analysis with simple benchmark models to check the performances of our models. We find that including interactions can significantly change the statistical importance of the signs and diseases. This first principles approach, as we show, facilitates the early diagnosis of disease by taking interactions into accounts, and enables the construction of consensus diagnostic flow charts. Additionally, we envision that our approach will find applications in systems biology, and in particular, in characterizing the phenome via the metabolome, the proteome, the transcriptome, and the genome.

show abstract

“…This is a preprint of an article accepted for publication in the 'Proceedings of the Nordic Conference on Secure IT Systems (Nordic 2017)', Springer under DOI https://doi.org/10.1007/978-3-319-70290-2_ 7 …”

mentioning

confidence: 99%

Bayesian Network Models in Cyber Security: A Systematic Review

Chockalingam

Pieters

Teixeira

et al. 2017

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Bayesian Networks (BNs) are an increasingly popular modelling technique in cyber security especially due to their capability to overcome data limitations. This is also exemplified by the growth of BN models development in cyber security. However, a comprehensive comparison and analysis of these models is missing. In this paper, we conduct a systematic review of the scientific literature and identify 17 standard BN models in cyber security. We analyse these models based on 8 different criteria and identify important patterns in the use of these models. A key outcome is that standard BNs are noticeably used for problems especially associated with malicious insiders. This study points out the core range of problems that were tackled using standard BN models in cyber security, and illuminates key research gaps.

show abstract

Constructing Bayesian networks for medical diagnosis from incomplete and partially correct statistics

Cited by 157 publications

References 11 publications

A new similarity measure based on Bayesian Network signature correspondence for brain tumors cases retrieval

A new similarity measure based on Bayesian Network signature correspondence for brain tumors cases retrieval

Toward First Principle Medical Diagnostics: On the Importance of Disease-Disease and Sign-Sign Interactions

Bayesian Network Models in Cyber Security: A Systematic Review

Contact Info

Product

Resources

About