Foundations of Biomedical Knowledge Representation
Methods and Applications
Chapter and Conference Paper
“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...
Chapter and Conference Paper
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...
Chapter and Conference Paper
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, ...
Chapter and Conference Paper
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 ...
Chapter and Conference Paper
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...
Chapter and Conference Paper
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...
Chapter and Conference Paper
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...
Chapter and Conference Paper
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 ...
Book
Chapter
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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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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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...
Chapter and Conference Paper
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...
Chapter
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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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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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...
Chapter and Conference Paper
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...
Chapter and Conference Paper
Medical protocols and guidelines can be looked upon as concurrent programs, where the patient’s state dynamically changes over time. Methods based on verification and model-checking developed in the past have ...
Chapter and Conference Paper
In the formal analysis of health-care, there is little work that combines probabilistic and temporal reasoning. On the one hand, there are those that aim to support the clinical thinking process, which is char...
Chapter and Conference Paper
Probabilistic logics have attracted a great deal of attention during the past few years. While logical languages have taken a central position in research on knowledge representation and automated reasoning, p...