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18
Building probabilistic networks: where do the numbers come from?  a guide to the literature
 IEEE Transactions on Knowledge and Data Engineering
, 2000
"... Probabilistic networks are now fairly well established as practical representations of knowledge for reasoning under uncertainty, as demonstrated by an increasing number of successful applications in such domains as (medical) diagnosis and prognosis, planning, vision, ..."
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Cited by 29 (3 self)
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Probabilistic networks are now fairly well established as practical representations of knowledge for reasoning under uncertainty, as demonstrated by an increasing number of successful applications in such domains as (medical) diagnosis and prognosis, planning, vision,
Talking Probabilities: Communicating Probabilistic Information With Words And Numbers
 International Journal of Approximate Reasoning
, 1999
"... The number of knowledgebased systems that build on Bayesian belief networks is increasing. The construction of such a network however requires a large number of probabilities in numerical form. This is often considered a major obstacle, one of the reasons being that experts are reluctant to provide ..."
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Cited by 27 (4 self)
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The number of knowledgebased systems that build on Bayesian belief networks is increasing. The construction of such a network however requires a large number of probabilities in numerical form. This is often considered a major obstacle, one of the reasons being that experts are reluctant to provide numerical probabilities. The use of verbal probability expressions as an additional method of eliciting probabilistic information may to some extent remove this obstacle. In this paper, we review studies that address the communication of probabilities in words and/or numbers. We then describe our own experiments concerning the development of a probability scale that contains words as well as numbers. This scale appears to be an aid for researchers and domain experts during the elicitation phase of building a belief network and might help users understand the output of the network.
A Probabilistic and DecisionTheoretic Approach to the Management of Infectious Disease at the ICU
 Artificial Intelligence in Medicine
, 2000
"... The medical community is presently in a state of transition from a situation dominated by the paper medical record to a future situation where all patient data will be available online by an electronic clinical information system. In dataintensive clinical environments, such as intensive care units ..."
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Cited by 20 (9 self)
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The medical community is presently in a state of transition from a situation dominated by the paper medical record to a future situation where all patient data will be available online by an electronic clinical information system. In dataintensive clinical environments, such as intensive care units (ICUs), clinical patient data are already fully managed by such systems in a number of hospitals. However, providing facilities for storing and retrieving patient data to clinicians is not enough; clinical information systems should also offer facilities to assist clinicians in dealing with hard clinical problems. Extending an information system's capabilities by integrating it with a decisionsupport system may be a solution. In this paper, we describe the development of a probabilistic and decisiontheoretic system that aims to assist clinicians in diagnosing and treating patients with pneumonia in the intensivecare unit. Its underlying probabilisticnetwork model includes tempo...
Bayesian Network Modelling through Qualitative Patterns
 ARTIFICIAL INTELLIGENCE
, 2003
"... In designing a Bayesian network for an actual problem, developers need to bridge the gap between the mathematical abstractions o#ered by the Bayesiannetwork formalism and the features of the problem to be modelled. Qualitative probabilistic networks (QPNs) have been put forward as qualitative an ..."
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Cited by 12 (5 self)
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In designing a Bayesian network for an actual problem, developers need to bridge the gap between the mathematical abstractions o#ered by the Bayesiannetwork formalism and the features of the problem to be modelled. Qualitative probabilistic networks (QPNs) have been put forward as qualitative analogues to Bayesian networks, and allow modelling interactions in terms of qualitative signs. They thus have the advantage that developers can abstract from the numerical detail, and therefore the gap may not be as wide as for their quantitative counterparts. A notion that has been suggested in the literature to facilitate Bayesiannetwork development is causal independence. It allows exploiting compact representations of probabilistic interactions among variables in a network. In the paper, we deploy both causal independence and QPNs in developing and analysing a collection of qualitative, causal interaction patterns, called QC patterns. These are endowed with a fixed qualitative semantics, and are intended to o#er developers a highlevel starting point when developing Bayesian networks.
Restricted Bayesian Network Structure Learning
 Advances in Bayesian Networks, Studies in Fuzziness and Soft Computing
, 2002
"... Learning the structure of a Bayesian network from data is a difficult problem, as its associated search space is exponentially large. As a consequence, researchers have studied learning Bayesian networks with a fixed structure, notably naive Bayesian networks and treeaugmented Bayesian networks. Th ..."
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Cited by 11 (3 self)
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Learning the structure of a Bayesian network from data is a difficult problem, as its associated search space is exponentially large. As a consequence, researchers have studied learning Bayesian networks with a fixed structure, notably naive Bayesian networks and treeaugmented Bayesian networks. There is substantial evidence in the literature that the performance of such restricted networks can be surprisingly good. In this paper, we propose a restricted, polynomial time structure learning algorithm that is not as restrictive as both other approaches, and allows researchers to determine the right balance between performance in dealing with classification, as well as with respect to quality of the underlying probability distribution. The results obtained with this algorithm allows drawing some conclusions with regard to Bayesiannetwork structure learning in general.
Prognostic Models in Medicine, AI and Statistical Approaches
, 2001
"... Introduction Prognosis (pro: before; gnoscere: to know) literary means to know beforehand or, as a noun, foreknowledge. The key concept behind prognosis is the prediction of an event before its possible occurrence. One could argue that the prediction of anything without knowing about its possible oc ..."
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Cited by 6 (0 self)
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Introduction Prognosis (pro: before; gnoscere: to know) literary means to know beforehand or, as a noun, foreknowledge. The key concept behind prognosis is the prediction of an event before its possible occurrence. One could argue that the prediction of anything without knowing about its possible occurrence is a form of prognosis. An example of the latter is used in the evaluation of a clinician's performance on predicting the disease outcome of a patient without knowledge of the actual outcome. Either way, the essence of prognosis is that the predicted event occurs in the future relative to the information available at the time of prediction. Time is thus inherent to the concept of prognosis and distinguishes it, for example, from that of diagnosis, where the future plays a less important role. Medical prognosis is defined here as: the prediction of the future course and outcome<
Bayesian Networks in Medicine: a Modelbased Approach to Medical Decision Making
"... Bayesian networks have been introduced in the 1980s. Research to explore the use of the formalism in the context of medical decision making started in the 1990s. The formalism possesses the unique quality of being both a statistical and an AIlike knowledgerepresentation formalism. As it allows f ..."
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Cited by 3 (0 self)
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Bayesian networks have been introduced in the 1980s. Research to explore the use of the formalism in the context of medical decision making started in the 1990s. The formalism possesses the unique quality of being both a statistical and an AIlike knowledgerepresentation formalism. As it allows for structuring domain knowledge, by exploiting causal and other relationships between domain variables, the formalism is also modelbased. In this paper the use of the formalism in building medical decision support systems in medicine is discussed, taking the problem of optimal prescription of antibiotics to patients with pneumonia in the ICU as a reallife example. Keywords: medical decision support, intelligent systems, Bayesian networks. 1
Expert Knowledge and its Role in Learning Bayesian Networks in Medicine
 in: AIME 2001. Lecture Notes in Artificial Intelligence 2101
"... A major part of the medical knowledge concerns diseases that are uncommon or even rare. The uncommon nature of these disorders renders it impossible to collect data of a suciently large number of patients to develop machinelearning models that faithfully reflect the subtleties of the domain. An alt ..."
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Cited by 2 (2 self)
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A major part of the medical knowledge concerns diseases that are uncommon or even rare. The uncommon nature of these disorders renders it impossible to collect data of a suciently large number of patients to develop machinelearning models that faithfully reflect the subtleties of the domain. An alternative is to develop a Bayesian network with the help of clinical experts. Lack of data is then compensated for by eliciting the structure with its associated local probability distributions from the experts. The resulting network can be subsequently evaluated using the available dataset. One may also consider adopting very strong independence assumptions, such as in naive Bayesian models. Normally not all subtleties of the interactions among the variables in the domain are reflected in such models. Yet, a relatively small dataset may suce to obtain an acceptably accurate model. This paper explores the tradeoffs between modelling using expert knowledge, and machine learning using a small clinical dataset in the context of Bayesian networks.
Using Bayesian Networks in an Industrial Setting: Making Printing Systems Adaptive
"... Abstract. Control engineering is a field of major industrial importance as it offers principles for engineering controllable physical devices, such as cell phones, television sets, and printing systems. Control engineering techniques assume that a physical system’s dynamic behaviour can be completel ..."
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Cited by 1 (1 self)
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Abstract. Control engineering is a field of major industrial importance as it offers principles for engineering controllable physical devices, such as cell phones, television sets, and printing systems. Control engineering techniques assume that a physical system’s dynamic behaviour can be completely described by means of a set of equations. However, as modern systems are often of high complexity, drafting such equations has become more and more difficult. Moreover, to dynamically adapt the system’s behaviour to a changing environment, observations obtained from sensors at runtime need to be taken into account. However, such observations give an incomplete picture of the system’s behaviour; when combined with the incompletely understood complexity of the device, control engineering solutions increasingly fall short. Probabilistic reasoning would allow one to deal with these sources of incompleteness, yet in the area of control engineering such AI solutions are rare. When using a Bayesian network in this context the required model can be learnt, and tuned, from data, uncertainty can be handled, and the model can be subsequently used for stochastic control of the system’s behaviour. In this paper we discuss industrial research in which Bayesian networks were successfully used to control complex printing systems. 1