Application of Bayesian Networks in Consumer Service Industry and Healthcare (original) (raw)
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2014
Introduction: Bayesian networks are a form of statistical modelling, which has been widely used in fields like clinical decision, systems biology, human immunodeficiency virus (HIV) and influenza research, analyses of complex disease systems, interactions between multiple diseases and, also, in diagnostic diseases. The present study aimed to show the usefulness of Bayesian networks (BNs) in epidemiological studies. Material and Methods: 3,993 subjects (men 1,758, women 2,235) belonging to the public productive sector from the Balearic Islands (Spain), which were active workers, constitute the data set. Results: A BN was built from a dataset composed of twelve relevant features in cardiovascular disease epidemiology. Furthermore, the structure and parameters were learnt with GeNIe 2.0 tool. Taking into account the main topological properties some features were optimized, obtaining a hypothesized scenario where the likelihoods of the different features were updated and the adequate conclusions were established. Conclusions: Bayesian networks allow us to obtain a hypothetical scenario where the probabilities of the different features are updated according to the evidence that is introduced. This fact makes Bayesian networks a very attractive tool.
Bayesian networks for health care support
2016
BFs to analyse the impact of Diagnosis on Number of meetings The analysis of observational data requires the use of a model, such as a multivariate regression. Bayesian networks (BNs) are well known as expert systems but can also be used to model data. A BN is a probabilistic model that represents the probabilistic relationships and conditional dependencies among variables. A BN allows probabilistic inference to be performed coherently, using the law of probability. Also a BN has the from the Barts and the London HPB (HepatoPancreaticoBiliary) centre following some changes to the MDT process. By evaluating the strength of each of the associations, we examine whether the MDT process has improved treatment recommendations for these patients.. 8 1.2 Structure of this thesis Chapter 2 discusses the potential benefits of Bayesian methods for introducing new changes in health service. We review the existing approaches to examine the effectiveness of complex health care initiatives and discuss the pitfalls of these approaches. Chapter 3 introduces BNs and reviews existing methods for their construction, including both expert judgement and learning from data. The importance of dynamic
Challenge: Where is the Impact of Bayesian Networks in
Proceedings of the …, 1997
Bayesian networks are graphical representations of probability distributions. Over the last decade, these representations have become the method of choice for representation of uncertainly in arti cial intelligence. Today, they play a crucial role in modern expert systems, diagnosis engines, and decision support systems. In recent years, there has been much interest in learning Bayesian networks from data. Learning such models is desirable simply because there is a wide array of o -the-shelf tools that can apply the learned models as described above. Practitioners also claim that adaptive Bayesian networks have advantages in their own right as a non-parametric method for density estimation, data analysis, pattern classication, and modeling. Among the reasons cited we nd: their semantic clarity and understandability by humans, the ease of acquisition and incorporation of prior knowledge, the ease of integration with optimal decision-making methods, the possibility of causal interpretation of learned models, and the automatic handling of noisy and missing data. In spite of these claims, methods that learn Bayesian networks have yet to make the impact that other techniques such as neural networks and hidden Markov models have made in applications such as pattern and speech recognition. In this paper, we challenge the research community to identify and characterize domains where induction of Bayesian networks makes the critical di erence, and to quantify the factors that are responsible for that di erence. In addition to formalizing the challenge, we identify research problems whose solution is, in our view, crucial for meeting this challenge.
Frontiers in Artificial Intelligence, 2021
Developing data-driven solutions that address real-world problems requires understanding of these problems’ causes and how their interaction affects the outcome–often with only observational data. Causal Bayesian Networks (BN) have been proposed as a powerful method for discovering and representing the causal relationships from observational data as a Directed Acyclic Graph (DAG). BNs could be especially useful for research in global health in Lower and Middle Income Countries, where there is an increasing abundance of observational data that could be harnessed for policy making, program evaluation, and intervention design. However, BNs have not been widely adopted by global health professionals, and in real-world applications, confidence in the results of BNs generally remains inadequate. This is partially due to the inability to validate against some ground truth, as the true DAG is not available. This is especially problematic if a learned DAG conflicts with pre-existing domain d...
Artificial Intelligence in Medicine, 2004
Due to the uncertainty of many of the factors that influence the performance of an emergency medical service, we propose using Bayesian networks to model this kind of system. We use different algorithms for learning Bayesian networks in order to build several models, from the hospital manager's point of view, and apply them to the specific case of the emergency service of a Spanish hospital. This first study of a real problem includes preliminary data processing, the experiments carried out, the comparison of the algorithms from different perspectives, and some potential uses of Bayesian networks for management problems in the health service.
Bayesian networks for data mining
Data mining and knowledge discovery
A Bayesian network is a graphical model that encodes probabilistic relationships among variables of interest. When used in conjunction with statistical techniques, the graphical model has several advantages for data modeling. One, because the model encodes dependencies among all variables, it readily handles situations where some data entries are missing. Two, a Bayesian network can be used to learn causal relationships, and hence can be used to gain understanding about a problem domain and to predict the consequences of intervention. Three, because the model has both a causal and probabilistic semantics, it is an ideal representation for combining prior knowledge (which often comes in causal form) and data. Four, Bayesian statistical methods in conjunction with Bayesian networks offer an efficient and principled approach for avoiding the overfitting of data. In this paper, we discuss methods for constructing Bayesian networks from prior knowledge and summarize Bayesian statistical methods for using data to improve these models. With regard to the latter task, we describe methods for learning both the parameters and structure of a Bayesian network, including techniques for learning with incomplete data. In addition, we relate Bayesian-network methods for learning to techniques for supervised and unsupervised learning. We illustrate the graphical-modeling approach using a real-world case study.
A comprehensive scoping review of Bayesian networks in healthcare: Past, present and future
Artificial Intelligence in Medicine, 2021
No comprehensive review of Bayesian networks (BNs) in healthcare has been published in the past, making it difficult to organize the research contributions in the present and identify challenges and neglected areas that need to be addressed in the future. This unique and novel scoping review of BNs in healthcare provides an analytical framework for comprehensively characterizing the domain and its current state. The review shows that: (1) BNs in healthcare are not used to their full potential; (2) a generic BN development process is lacking; (3) limitations exist in the way BNs in healthcare are presented in the literature, which impacts understanding, consensus towards systematic methodologies, practice and adoption of BNs; and (4) a gap exists between having an accurate BN and a useful BN that impacts clinical practice. This review empowers researchers and clinicians with an analytical framework and findings that will enable understanding of the need to address the problems of restricted aims of BNs, ad hoc BN development methods, and the lack of BN adoption in practice. To map the way forward, the paper proposes future research directions and makes recommendations regarding BN development methods and adoption in practice.
Bayesian Network Model for Epidemiological Data
Global journal of computer science and technology, 2013
This documentation describes the implementation of Bayesian Network on Hiroshima Nagasaki atomic bomb survivor data, using “R” software. Bayesian networks, a state-of-the art representation of probabilistic knowledge by a graphical diagram, has emerged in recent years as essential for pattern recognition and classification in the healthcare field. Unlike some data mining techniques, Bayesian networks allow investigators to combine domain knowledge with statistical data. This tailored discussion presents the basic concepts of Bayesian networks and its use for building a health risk model on Epidemiological data. The main objectives of our study is to find interdependencies between various attributes of data and to determine the threshold value of radiation dosage under which death counts are negligible.
Incorporating expert knowledge when learning Bayesian network structure: A medical case study
Artificial Intelligence in Medicine, 2011
Objectives: Bayesian networks (BNs) are rapidly becoming a leading technology in applied Artificial Intelligence, with medicine its most popular application area. Both automated learning of BNs and expert elicitation have been used to build these networks, but the potentially more useful combination of these two methods remains underexplored. In this paper we examine a number of approaches to their combination and present new techniques for assessing their results. Methods and materials: Using public-domain data for heart failure, we run an automated causal discovery system (CaMML), which allows the incorporation of multiple kinds of prior expert knowledge into its search, to test and compare unbiased discovery with discovery biased with different kinds of expert opinion. We use adjacency matrices enhanced with numerical and colour labels to assist with the interpretation of the results. These techniques are presented within a wider context of knowledge engineering with Bayesian networks (KEBN). Results: The adjacency matrices make it clear that for our particular application problem, the heart failure data, the simplest kind of prior information (partially sorting variables into tiers) was more effective in aiding model discovery than either using no prior information or using more sophisticated and detailed expert priors. Conclusion: Hybrid causal learning of BNs is an important emerging technology. We present methods for incorporating it into the knowledge engineering process, including visualisation and analysis of the learned networks.