An Overview of Bayesian Network Applications in Uncertain Domains <s> V. BAYESIAN NETWORKS APPLICATION IN MEDICAL DOMAIN <s> This paper considers and suggests efficient patrol of nurses from analysis result of nurse calls log. As the method of analysis, we consider applying Bayesian network. This is one of probability model that is available for prospects of a phenomenon, rational mind decision and so on. In conventional studies, correlation coefficient was used to examine relation between phenomena. However, we could analyze in detail by using Bayesian network because we didn't overlook the information that may overlook by using correlation coefficient. Applying the proposed method to total 5,478 nurse call data, we have obtained following knowledge: (a) some reasons of nurse call are inferred by situation of patient and demand level of nurse call, (b) an efficient patrol of nurse can be devised, and (c) Bayesian network didn't overlook the information compared with conventional method. <s> BIB001 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> V. BAYESIAN NETWORKS APPLICATION IN MEDICAL DOMAIN <s> In this paper we discuss different architectures for reasoning under uncertainty related to our ongoing research into building a medical decision support system. The uncertainty in the medical domain can be divided into a well understood part and a less understood part. This motivates the use of a hybrid decision support system, and in particular, we argue that a Bayesian network should be used for those parts of the domain that are well understood and can be explicitly modeled, whereas a case-based reasoning system should be employed to reason in parts of the domain where no such model is available. Four architectures that combine Bayesian networks and case-based reasoning are proposed, and our working hypothesis is that these hybrid systems each will perform better than either framework will do on its own. <s> BIB002 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> V. BAYESIAN NETWORKS APPLICATION IN MEDICAL DOMAIN <s> This paper explains the role of Bayes Theorem and Bayesian networks arising in a medical negligence case brought by a patient who suffered a stroke as a result of an invasive diagnostic test. The claim of negligence was based on the premise that an alternative (non-invasive) test should have been used because it carried a lower risk. The case raises a number of general and widely applicable concerns about the decision-making process within the medical profession, including the ethics of informed consent, patient care liabilities when errors are made, and the research problem of focusing on 'true positives' while ignoring 'false positives'. An immediate concern is how best to present Bayesian arguments in such a way that they can be understood by people who would normally balk at mathematical equations. We feel it is possible to present purely visual representations of a non-trivial Bayesian argument in such a way that no mathematical knowledge or understanding is needed. The approach supports a wide range of alternative scenarios, makes all assumptions easily understandable and offers significant potential benefits to many areas of medical decision-making. <s> BIB003 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> V. BAYESIAN NETWORKS APPLICATION IN MEDICAL DOMAIN <s> Toothache is the most common symptom encountered in dental practice. It is subjective and hence, there is a possibility of under or over diagnosis of oral pathologies where patients present with only toothache. Addressing the issue, the paper proposes a methodology to develop a Bayesian classifier for diagnosing some common dental diseases (D?=?10) using a set of 14 pain parameters (P?=?14). A questionnaire is developed using these variables and filled up by ten dentists (n?=?10) with various levels of expertise. Each questionnaire is consisted of 40 real-world cases. Total 14*10*10 combinations of data are hence collected. The reliability of the data (P and D sets) has been tested by measuring (Cronbach's alpha). One-way ANOVA has been used to note the intra and intergroup mean differences. Multiple linear regressions are used for extracting the significant predictors among P and D sets as well as finding the goodness of the model fit. A naive Bayesian classifier (NBC) is then designed initially that predicts either presence/absence of diseases given a set of pain parameters. The most informative and highest quality datasheet is used for training of NBC and the remaining sheets are used for testing the performance of the classifier. Hill climbing algorithm is used to design a Learned Bayes' classifier (LBC), which learns the conditional probability table (CPT) entries optimally. The developed LBC showed an average accuracy of 72%, which is clinically encouraging to the dentists. <s> BIB004 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> V. BAYESIAN NETWORKS APPLICATION IN MEDICAL DOMAIN <s> Objectives: Bayesian networks (BNs) are rapidly becoming a leading technology in applied Artificial Intelligence, with many applications in medicine. 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 when learning structure and present new techniques for assessing their results. Methods and materials: Using public-domain medical data, 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. We present an algorithm for generating a single BN from a set of learned BNs that incorporates user preferences regarding complexity vs completeness. These techniques are presented as part of the first detailed workflow for hybrid structure learning within the broader knowledge engineering process. Results: The detailed knowledge engineering workflow is shown to be useful for structuring a complex iterative BN development process. The adjacency matrices make it clear that for our medical case study using the IOWA dataset, 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. The method for generating a single BN captures relationships that would be overlooked by other approaches in the literature. 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. <s> BIB005 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> V. BAYESIAN NETWORKS APPLICATION IN MEDICAL DOMAIN <s> Mild Cognitive Impairment (MCI) is thought to be the prodromal phase to Alzheimer's disease (AD), which is the most common form of dementia and leads to irreversible neurogenerative damage of the brain. In order to further improve the diagnostic quality of the MCI, we developed a MCI expert system to address MCI's prediction and inference question, consequently, assist the diagnosis of doctor. In this system, we mainly deal with following problems: (1) Estimate missing data in the experiment by utilizing mutual information and Newton interpolation. (2) Make certain the prior feature ordering in constructing Bayesian network. (3) Construct the Bayesian network (We term the algorithm as MNBN). The experimental results indicate that MNBN algorithm achieved better results than some existing methods in most instances. The mean square error comes to 0.0173 in the MCI experiment. Our results shed light on the potential application in MCI diagnosis. <s> BIB006
Many researchers have used BN in medical domain because of inherent uncertainty in this domain and the ability of BN to handle uncertainty in the diagnosis process of diseases with improved patient caring facilities. In context of clinical tests for a diagnosis of a disease, BN can be helpful in selecting better alternative tests to ensure reliability. Literature review on BN-based techniques in medical domain is presented that support to resolve uncertainty issues in the treatment of patients. Lee et al.  used BN for knowledge discovery from nursing database. Nevertheless, effectiveness cannot be judged without validated outcome. Aoki et al. BIB001 developed a BN-based methodology for efficient patrolling of nurses. The purpose of nurse patrolling is to improve the dependability of calling a nurse according to patient's needs by analyzing data based on demand level of nurse calls (time zone of previous nurse call, interval of previous nurse call, reason of previous call, and degrees of freedom in life) and patient's condition (sex, age, hospital department, and degrees of freedom in life). It may help in decision support process according to the condition of a patient and the level of need to call a nurse for an on-time attention. However, this approach has an inherent shortcoming associated with it; i.e. the need of frequent interruption by the supervisory staff. In support to clinical side, Bruland et al. BIB002 integrated Case-Based Reasoning (CBR) and Bayesian Network (BN) for making decision under uncertainty. Although the approach lacks an implementation, the authors have attempted to prove its effectiveness through intuition. Moreover, Flores et al. BIB005 integrated expert knowledge to examine different approaches to devise novel method for assessment of experimental parameters and transitional results. However, the method has limited application for all real variables, despite the fact of reduction of mining burden. Fenton et al. BIB003 assessed the risks of alternative medical diagnosis with BN. The objective is to support decision making with a variety of alternatives in medical diagnosis using BN. The assumption based decision making has the trade-off between the region accuracy and storage space. Chattopadhyay et al. BIB004 BIB006 . This diagnostic system is capable of handling incomplete data for prediction and assist in improving precision in terms of execution time, lowest average and variance. Nonetheless, variation effect cannot be overlooked.