Foundations of Biomedical Knowledge Representation
Methods and Applications
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Article
Open AccessEvaluating the Usefulness of Counterfactual Explanations from Bayesian Networks
Bayesian networks are commonly used for learning with uncertainty and incorporating expert knowledge. However, they are hard to interpret, especially when the network structure is complex. Methods used to expl...
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Article
Open AccessDeterminants of physical activity behaviour change in (online) interventions, and gender-specific differences: a Bayesian network model
Physical activity (PA) is known to be beneficial for health, but adherence to international PA guidelines is low across different subpopulations. Interventions have been designed to stimulate PA of different t...
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Chapter and Conference Paper
Correction to: Gaining Insight into Determinants of Physical Activity Using Bayesian Network Learning
“Gaining Insight into Determinants of Physical Activity Using Bayesian Network Learning” was previously published non-open access. It has now been changed to open access under a CC BY 4.0 license and the copyr...
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Chapter and Conference Paper
Gaining Insight into Determinants of Physical Activity Using Bayesian Network Learning
Bayesian network modelling is applied to health psychology data in order to obtain more insight into the determinants of physical activity. This preliminary study discusses some challenges to apply general mac...
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Chapter and Conference Paper
Temporal Exceptional Model Mining Using Dynamic Bayesian Networks
The discovery of subsets of data that are characterized by models that differ significantly from the entire dataset, is the goal of exceptional model mining. With the increasing availability of temporal data, ...
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Chapter and Conference Paper
A Data-Driven Exploration of Hypotheses on Disease Dynamics
Unsupervised learning is often used to obtain insight into the underlying structure of medical data. In this paper, we show that unsupervised methods, in particular hidden Markov models, can go beyond this by ...
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Chapter and Conference Paper
Modeling the Dynamics of Multiple Disease Occurrence by Latent States
The current availability of large volumes of health care data makes it a promising data source to new views on disease interaction. Most of the times, patients have multiple diseases instead of a single one (a...
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Chapter and Conference Paper
Representing Hypoexponential Distributions in Continuous Time Bayesian Networks
Continuous time Bayesian networks offer a compact representation for modeling structured stochastic processes that evolve over continuous time. In these models, the time duration that a variable stays in a sta...
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Chapter and Conference Paper
Explaining the Most Probable Explanation
The use of Bayesian networks has been shown to be powerful for supporting decision making, for example in a medical context. A particularly useful inference task is the most probable explanation (MPE), which p...
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Chapter and Conference Paper
A Prognostic Model of Glioblastoma Multiforme Using Survival Bayesian Networks
Bayesian networks are attractive for develo** prognostic models in medicine, due to the possibility for modelling the multivariate relationships between variables that come into play in the care process. In ...
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Book
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Chapter
Supporting Physicians and Patients Through Recommendation: Guidelines and Beyond
The recommendation task, intended as the task of supporting physicians in their activity (and, in particular, in decision making) by providing them indications of the most appropriate way of treating patients,...
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Chapter and Conference Paper
Mining Hierarchical Pathology Data Using Inductive Logic Programming
Considerable amounts of data are continuously generated by pathologists in the form of pathology reports. To date, there has been relatively little work exploring how to apply machine learning and data mining ...
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Chapter
How to Read the Book “Foundations of Biomedical Knowledge Representation”
Biology and medicine are very rich knowledge domains in which already at an early stage in their scientific development it was realised that without a proper way to organise this knowledge they would inevitabl...
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Chapter and Conference Paper
Hybrid Time Bayesian Networks
Capturing heterogeneous dynamic systems in a probabilistic model is a challenging problem. A single time granularity, such as employed by dynamic Bayesian networks, provides insufficient flexibility to capture...
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Chapter
A Hybrid Approach to the Verification of Computer Interpretable Guidelines
Computer Interpretable Guidelines (CIGs) are assuming a major role in the medical area, in order to enhance the quality of medical assistance by providing physicians with evidence-based recommendations. Howeve...
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Chapter
An Introduction to Knowledge Representation and Reasoning in Healthcare
Healthcare and medicine are, and have always been, very knowledge-intensive fields. Healthcare professionals use knowledge of the structure (molecular biology, cell biology, histology, gross anatomy) and funct...
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Chapter and Conference Paper
Causal Independence Models for Continuous Time Bayesian Networks
The theory of causal independence is frequently used to facilitate the assessment of the probabilistic parameters of probability distributions of Bayesian networks. Continuous time Bayesian networks are a rela...
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Chapter and Conference Paper
Understanding the Co-occurrence of Diseases Using Structure Learning
Multimorbidity, i.e., the presence of multiple diseases within one person, is a significant health-care problem for western societies: diagnosis, prognosis and treatment in the presence of of multiple diseases...
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Chapter
Reasoning with Uncertainty about System Behaviour: Making Printing Systems Adaptive
Any conclusion about a system’s hidden behaviour based on the observation of findings emanating from this behaviour is inherently uncertain. If one wishes to take action only when these conclusions give rise t...
