Improved learning of Bayesian networks in biomedicine

International Journal of Computers …, 2009

Bayesian networks encode causal relations between variables using probability and graph theory. They can be used both for prediction of an outcome and interpretation of predictions based on the encoded causal relations. In this paper we analyse a tree-like Bayesian network learning algorithm optimised for classification of data and we give solutions to the interpretation and analysis of predictions. The classification of logical -i.e. binary -data arises specifically in the field of medical diagnosis, where we have to predict the survival chance based on different types of medical observations or we must select the most relevant cause corresponding again to a given patient record. Surgery survival prediction was examined with the algorithm. Bypass surgery survival chance must be computed for a given patient, having a data-set of  medical examinations for  patients.

Studies in health technology and informatics, 2004

Bayesian Networks (BN) is a knowledge representation formalism that has been proven to be valuable in biomedicine for constructing decision support systems and for generating causal hypotheses from data. Given the emergence of datasets in medicine and biology with thousands of variables and that current algorithms do not scale more than a few hundred variables in practical domains, new efficient and accurate algorithms are needed to learn high quality BNs from data. We present a new algorithm called Max-Min Hill-Climbing (MMHC) that builds upon and improves the Sparse Candidate (SC) algorithm; a state-of-the-art algorithm that scales up to datasets involving hundreds of variables provided the generating networks are sparse. Compared to the SC, on a number of datasets from medicine and biology, (a) MMHC discovers BNs that are structurally closer to the data-generating BN, (b) the discovered networks are more probable given the data, (c) MMHC is computationally more efficient and scal...

Studies in health technology and informatics, 2007

Serving as exploratory data analysis tools, Bayesian networks (BNs) can be automatically learned from data to compactly model direct dependency relationships among the variables in a domain. A major challenge in BN learning is to effectively represent and incorporate domain knowledge in the learning process to improve its efficiency and accuracy. In this paper, we examine two types of domain knowledge representation in BNs: matrix and rule. We develop a set of consistency checking mechanisms for the representations and describe their applications in BN learning. Empirical results from the canonical Asia network example show that topological constraints, especially those imposed on the undirected links in the corresponding completed partially directed acyclic graph (CPDAG) of the learned BN, are particularly useful. Preliminary experiments on a real-life coronary artery disease dataset show that both efficiency and accuracy can be improved with the proposed methodology. The bootstrap...

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.

Expert Systems with Applications, 2010

Learning Bayesian Network structure from database is an NP-hard problem and still one of the most exciting challenges in machine learning. Most of the widely used heuristics search for the (locally) optimal graphs by defining a score metric and employs a search strategy to identify the network structure having the maximum score. In this work, we propose a new score (named implicit score) based on the Implicit inference framework that we proposed earlier. We then implemented this score within the K2 and MWST algorithms for network structure learning. Performance of the new score metric was evaluated on a benchmark database (ASIA Network) and a biomedical database of breast cancer in comparison with traditional score metrics BIC and BD Mutual Information. We show that implicit score yields improved performance over other scores when used with the MWST algorithm and have similar performance when implemented within K2 algorithm.

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.

Lecture Notes in Computer Science, 2009

While for many problems in medicine classification models are being developed, Bayesian network classifiers do not seem to have become as widely accepted within the medical community as logistic regression models. We compare first-order logistic regression and naive Bayesian classification in the domain of reproductive medicine and demonstrate that the two techniques can result in models of comparable performance. For Bayesian network classifiers to become more widely accepted within the medical community then, we feel that they should be better aligned with their context of application. We describe how to incorporate well-known concepts of clinical relevance in the process of constructing and evaluating Bayesian network classifiers to achieve such an alignment.

Structure learning of Bayesian networks is a well-researched but computationally and NP-hard task. We present an algorithm that integrates a low-order conditional independence approach for learning structures of Bayesian networks. Our algorithm also makes use of basic Bayesian network concepts. We show that the proposed algorithm is capable of handling networks with a large number of variables and small sample size in the case of microarray data analysis. We present the applicability of the proposed algorithm on breast cancer data sets and also compare its performance and computational efficiency with full-order conditional independence method. The experimental results show that our method can efficiently and accurately identify complex network structures from data.

2012

Under the Supervision of Professor Istvan Lauko Statistics from the National Cancer Institute indicate that 1 in 8 women will develop Breast cancer in their lifetime. Researchers have developed numerous statistical models to predict breast cancer risk however physicians are hesitant to use these models because of disparities in the predictions they produce. In an effort to reduce these disparities, we use Bayesian networks to capture the joint distribution of risk factors, and simulate artificial patient populations (clinical avatars) for interrogating the existing risk prediction models. The challenge in this effort has been to produce a Bayesian network whose dependencies agree with literature and are good estimates of the joint distribution of risk factors. In this work, we propose a methodology for learning Bayesian networks that uses prior knowledge to guide a collection of search algorithms in identifying an optimum structure. Using data from the breast cancer surveillance consortium we have shown that our methodology produces a Bayesian network with consistent dependencies and a better estimate of the distribution of risk factors compared with existing methods.

BMC Bioinformatics, 2010

Background: Biological networks offer us a new way to investigate the interactions among different components and address the biological system as a whole. In this paper, a reverse-phase protein microarray (RPPM) is used for the quantitative measurement of proteomic responses. Results: To discover the signaling pathway responsive to RPPM, a new structure learning algorithm of Bayesian networks is developed based on mutual Information, conditional independence, and graph immorality. Trusted biology networks are thus predicted by the new approach. As an application example, we investigate signaling networks of ataxia telangiectasis mutation (ATM). The study was carried out at different time points under different dosages for cell lines with and without gene transfection. To validate the performance ofthe proposed algorithm, comparison experiments were also implemented using three well-known networks. From the experiment results, our approach produces more reliable networks with a relatively small number of wrong connection especially in mid-size networks. By using the proposed method, we predicted different networks for ATM under different doses of radiation treatment, and those networks were compared with results from eight different protein protein interaction (PPI) databases.