20 Result(s)
within
Chapter
Conference Paper
Arjen Hommersom
Artificial Intelligence
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Chapter and Conference Paper
Meta-level Verification of the Quality of Medical Guidelines Using Interactive Theorem Proving
Requirements about the quality of medical guidelines can be represented using schemata borrowed from the theory of abductive diagnosis, using temporal logic to model the time-oriented aspects expressed in a gu...
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Chapter and Conference Paper
A History-Based Algebra for Quality-Checking Medical Guidelines
In this paper, we propose a formal theory to describe the development of medical guideline text in detail, but at a sufficiently high level abstraction, in such way that essential elements of the guidelines ar...
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Chapter and Conference Paper
Combining Task Execution and Background Knowledge for the Verification of Medical Guidelines
The use of a medical guideline can be seen as the execution of computational tasks, sequentially or in parallel, in the face of patient data. It has been shown that many of such guidelines can be represented a...
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Chapter and Conference Paper
The Role of Model Checking in Critiquing Based on Clinical Guidelines
Medical critiquing systems criticise clinical actions performed by a physician. In order to provide useful feedback, an important task is to find differences between the actual actions and a set of ‘ideal’ act...
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Chapter and Conference Paper
Actions with Failures in Interval Temporal Logic
Failures are unavoidable in many circumstances. For example, an agent may fail at some point to perform a task in a dynamic environment. Robust systems typically have mechanisms to handle such failures. Tempor...
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Chapter and Conference Paper
Integrating Logical Reasoning and Probabilistic Chain Graphs
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...
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Chapter and Conference Paper
Toward Probabilistic Analysis of Guidelines
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...
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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 and Conference Paper
Discovering Probabilistic Structures of Healthcare Processes
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 ...
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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
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 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 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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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 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
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
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...
