Bayesian networks in biomedicine and health-care

Lucas, Peter; Gaag, Linda C. van der; Abu‐Hanna, Ameen

doi:10.1016/j.artmed.2003.11.001

Cited by 258 publications

(178 citation statements)

References 28 publications

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“…BNs have proven to be successful in various domains such as medicine [18,19] and bioinformatics [20][21][22][23]. However, classical BNs are not well equipped to deal with temporal information.…”

Section: Bayesian Networkmentioning

confidence: 99%

Discovering human immunodeficiency virus mutational pathways using temporal Bayesian networks

Hernández-Leal

Rios-Flores

Ávila‐Ríos

et al. 2013

Artificial Intelligence in Medicine

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Section: Bayesian Networkmentioning

confidence: 99%

Discovering human immunodeficiency virus mutational pathways using temporal Bayesian networks

Hernández-Leal

Rios-Flores

Ávila‐Ríos

et al. 2013

Artificial Intelligence in Medicine

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“…There is a considerable body of work (e.g. [14,15]), indicating that Bayesian networks offer a natural and intuitive formalism for constructing clinically relevant models.…”

Section: Bayesian Networkmentioning

confidence: 99%

Discovering Probabilistic Structures of Healthcare Processes

Hommersom

Verwer

Lucas

2013

Lecture Notes in Computer Science

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Abstract. Medical protocols and guidelines can be looked upon as concurrent programs, where the patients dynamically change over time. Methods based on verification and model-checking developed in the past have been shown to offer insight into their correctness by adopting a logical point of view. However, there is uncertainty involved both in the management of the disease and the way the disease will develop, and, therefore, a probabilistic view on medical protocols seems more appropriate. On the other hand, representations using Bayesian networks usually involve a single patient group and do not capture the dynamic nature of care. In this paper, we propose a new method inspired by automata learning to represent and identify patient groups for obtaining insight into the care that patients have received. We evaluate this approach using data obtained from general practitioners and identify significant differences in patients who were diagnosed with a transient ischemic attack (TIA). Finally, we discuss the implications of such a computational method for the analysis of medical protocols.

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“…The ability to explicitly model causal dependencies and their simple and effective visualization through nodes and vertices are part of the appeal of Bayesian Networks. Lucas et al (2004) identified four important types of problem solving where Bayesian network methods have been applied: diagnostic reasoning, prognostic reasoning and treatment selection, and the discovery of functional interactions.…”

Section: Bayesian Network In Medical Applicationsmentioning

confidence: 99%