Modelling treatment effects in a clinical Bayesian network using Boolean threshold functions

Visscher, Stefan; Lucas, Peter; Schurink, Carolina A. M.; Bonten, Marc J. M.

doi:10.1016/j.artmed.2008.11.006

Cited by 17 publications

(9 citation statements)

References 13 publications

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“…The definition of the notion of causal independence given here follows the one from [43]. The general structure of a causal-independence model is shown in Fig.…”

Section: Preliminariesmentioning

confidence: 99%

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Exploiting causal functional relationships in Bayesian network modelling for personalised healthcare

Velikova

Scheltinga

Lucas

et al. 2014

International Journal of Approximate Reasoning

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Bridging the gap between the theory of Bayesian networks and solving an actual problem is still a big challenge and this is in particular true for medical problems, where such a gap is clearly evident. We argue that Bayesian networks offer appropriate technology for the successful modelling of medical problems, including the personalisation of healthcare. Personalisation is an important aspect of remote disease management systems. It involves the forecasting of progression of a disease based on the interpretation of patient data by a disease model. A natural foundation for disease models is physiological knowledge, as such knowledge facilitates building clinically understandable models. This paper proposes ways to represent such knowledge as part of engineering principles employed in building clinically practical probabilistic models. The methodology has been used to construct a temporal Bayesian network model for preeclampsia -a pregnancy-related disorder. The model is the first of its kind and an integral part of a mobile home-monitoring system intended for use in daily pregnancy care. We conducted an evaluation study with actual patient data to obtain insight into the model's performance and suitability. The results obtained are encouraging and show the potential of exploiting physiological knowledge for personalised decisionsupport systems.

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“…The definition of the notion of causal independence given here follows the one from [43]. The general structure of a causal-independence model is shown in Fig.…”

Section: Preliminariesmentioning

confidence: 99%

“…There is other recent research where causal independence models have been explored in designing medical Bayesian networks [37,41,38,43]. Bayesian networks also offer powerful methods to represent and reason about uncertain state changes over time.…”

Section: Preliminariesmentioning

confidence: 99%

Exploiting causal functional relationships in Bayesian network modelling for personalised healthcare

Velikova

Scheltinga

Lucas

et al. 2014

International Journal of Approximate Reasoning

Self Cite

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“…To obtain an inference based on multiple sources and therefore a large amount of heterogeneous dimensions, models able to integrate the conditional dependencies and relationships between these factors are appealing. Thus, in clinical applications, causal modelling has been suggested as a method to facilitate the specification of BNs with many parent variables [96]. When the purpose is to model the causality of concurrent dynamical systems in continuous time, learning probabilistic graphical models considering all unmeasured confounding factors can become challenging, but the availability of continuous (rather than episodic) context-aware sensing data offers unique opportunities to address this issue.…”

Section: G Machine Learningmentioning

confidence: 99%

From Wearable Sensors to Smart Implants-–Toward Pervasive and Personalized Healthcare

Andreu-Pérez

Leff

et al. 2015

IEEE Trans. Biomed. Eng.

299

156

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Abstract-Objective:This article discusses the evolution of pervasive healthcare from its inception for activity recognition using wearable sensors to the future of sensing implant deployment and data processing. Methods: We provide an overview of some of the past milestones and recent developments, categorised into different generations of pervasive sensing applications for health monitoring. This is followed by a review on recent technological advances that have allowed unobtrusive continuous sensing combined with diverse technologies to reshape the clinical workflow for both acute and chronic disease management. We discuss the opportunities of pervasive health monitoring through data linkages with other health informatics systems including the mining of health records, clinical trial databases, multi-omics data integration and social media. Conclusion: Technical advances have supported the evolution of the pervasive health paradigm towards preventative, predictive, personalised and participatory medicine. Significance: The sensing technologies discussed in this paper and their future evolution will play a key role in realising the goal of sustainable healthcare systems.Index Terms-pervasive health, wearable sensors, implantable sensors, health informatics.

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“…A more detailed description of exact and threshold functions can be found in [8]. Causal independence models with threshold functions are the basic elements for the model presented in the next section.…”

Section: Exact and Threshold Functionsmentioning

confidence: 99%

“…Causal independence models provide a way to specify interactions among random variables in a compact fashion [7]. A definition of the notion of causal independence is given following the one from [8].…”

Section: Causal Independence Modelsmentioning

confidence: 99%

Causal Probabilistic Modelling for Two-View Mammographic Analysis

Velikova

Samulski

Lucas

et al. 2009

Artificial Intelligence in Medicine

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Abstract.Mammographic analysis is a difficult task due to the complexity of image interpretation. This results in diagnostic uncertainty, thus provoking the need for assistance by computer decision-making tools. Probabilistic modelling based on Bayesian networks is among the suitable tools, as it allows for the formalization of the uncertainty about parameters, models, and predictions in a statistical manner, yet such that available background knowledge about characteristics of the domain can be taken into account. In this paper, we investigate a specific class of Bayesian networks-causal independence models-for exploring the dependencies between two breast image views. The proposed method is based on a multi-stage scheme incorporating domain knowledge and information obtained from two computer-aided detection systems. The experiments with actual mammographic data demonstrate the potential of the proposed two-view probabilistic system for supporting radiologists in detecting breast cancer, both at a location and a patient level.

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Modelling treatment effects in a clinical Bayesian network using Boolean threshold functions

Cited by 17 publications

References 13 publications

Exploiting causal functional relationships in Bayesian network modelling for personalised healthcare

Exploiting causal functional relationships in Bayesian network modelling for personalised healthcare

From Wearable Sensors to Smart Implants-–Toward Pervasive and Personalized Healthcare

Causal Probabilistic Modelling for Two-View Mammographic Analysis

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