References of "Boucher, C. A. B."
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See detailHIV Decision Support: From Molecule to Man
Sloot, P. M. A.; Coveney, P. V.; Ertaylan, Gökhan UL et al

in Philosophical Transactions. Mathematical, Physical and Engineering Sciences (2009), 367(1898), 2691-2703

Human immunodeficiency virus (HIV) is recognized to be one of the most destructive pandemics in recorded history. Effective highly active antiretroviral therapy and the availability of genetic screening ... [more ▼]

Human immunodeficiency virus (HIV) is recognized to be one of the most destructive pandemics in recorded history. Effective highly active antiretroviral therapy and the availability of genetic screening of patient virus data have led to sustained viral suppression and higher life expectancy in patients who have been infected with HIV. The sheer complexity of the disease stems from the multiscale and highly dynamic nature of the system under study. The complete cascade from genome, proteome, metabolome and physiome to health forms a multidimensional system that crosses many orders of magnitude in temporal and spatial scales. Understanding, quantifying and handling this complexity is one of the biggest challenges of our time, which requires a highly multidisciplinary approach. In order to supply researchers with an interactive framework and to provide the medical professional with appropriate tools and information for making a balanced and reliable clinical decision, we have developed 'ViroLab', a collaborative decision-support system (http://www.virolab.org/). ViroLab contains computational models that cover various spatial and temporal scales from atomic-level interactions in nanoseconds up to sociological interactions on the epidemiological level, spanning years of disease progression. ViroLab allows for personalized drug ranking. It is on trial in six hospitals and various virology and epidemiology laboratories across Europe. [less ▲]

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See detailVirolab: A Distributed Decision Support System for Viral Disease Treatment
Sloot, P. M. A.; Tirado-Ramos, A.; Ertaylan, Gökhan UL et al

in Bubak, M.T.; Turala, M.; Wiatr, K. (Eds.) CGW'07 proceedings (2007)

The HIV drug-resistance interpretation systems are used routinely throughout the world in a clinical setting. More knowledge is rapidly becoming available upon which clinical decisions could be made. This ... [more ▼]

The HIV drug-resistance interpretation systems are used routinely throughout the world in a clinical setting. More knowledge is rapidly becoming available upon which clinical decisions could be made. This knowledge, information, data and evidence from many sources are combined within a Decision Support System (DSS) to provide coherent judgements on drug-susceptibility. At the core of the DSS is a HIV drug-resistance interpretation system incorporating knowledge from the principal systems (Stanford HIVdb, Rega, ANRS, Virolab ) in use throughout the world. We describe an improved rule-based language which has adequate expressiveness and enjoys a fully-specified, formal semantics, allowing for automated reasoning over rule sets. Among the questions which can be addressed are: • Ambiguity: Is the rule set internally ambiguous? Does it allow more than one interpretation? • Completeness: Does the rule set have complete coverage? • Consistency: Are there rules in the set which make contradictory predictions? • Redundancy: Do some rules of a rule set subsume others? • Dissonance: How do rule sets differ in their predictions? • Predictive power: Can one rule set make more specific predictions than another or can it make predictions in cases where the other is silent? The formal language which we present has a well-defined semantics that will allow for making judgements of the above kinds using reasoning that is either completely automated or at least semi-automated. Furthermore recent findings have revealed the need to express multiplicative effects of certain mutations on drugs. The state of the art language for specifying HIV drug interpretation rules, ASI, in its present form, is limited to linear combinations of effects. In future work we will use Bayesian hierarchical modelling to make predictive distributions in the presence of uncertainty. The full chain of analysis will combine Bayesian hierarchical modelling with probabilistic decision analysis based on utility attribution and/or multi-objective optimisation of such quantities as cost,chance and duration of survival or quality-adjusted life years. [less ▲]

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