An Overview of Bayesian Network Applications in Uncertain Domains <s> A. Bayesian Networks Representation 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> BIB001 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> A. Bayesian Networks Representation 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> BIB002 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> A. Bayesian Networks Representation 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> BIB003 </s> An Overview of Bayesian Network Applications in Uncertain Domains <s> A. Bayesian Networks Representation in Medical Domain <s> There is agreement in that strengthening the sets of neurobiological data would reinforce the diagnostic objectivity of many psychiatric entities. This article attempts to use this approach in borderline personality disorder (BPD). Assuming that most of the biological findings in BPD reflect common underlying pathophysiological processes we hypothesized that most of the data involved in the findings would be statistically interconnected and interdependent, indicating biological consistency for this diagnosis. Prospectively obtained data on scalp and sleep electroencephalography (EEG), clinical neurologic soft signs, the dexamethasone suppression and thyrotropin-releasing hormone stimulation tests of 20 consecutive BPD patients were used to generate a Bayesian network model, an artificial intelligence paradigm that visually illustrates eventual associations (or inter-dependencies) between otherwise seemingly unrelated variables. The Bayesian network model identified relationships among most of the variables. EEG and TSH were the variables that influence most of the others, especially sleep parameters. Neurological soft signs were linked with EEG, TSH, and sleep parameters. The results suggest the possibility of using objective neurobiological variables to strengthen the validity of future diagnostic criteria and nosological characterization of BPD. <s> BIB004
BN have demonstrated a superb performance for predictions in medical domain. Therefore, BN techniques have been applied effectively in diagnosis like examining dental pain BIB001 , BPD BIB004 , risk analysis in alternative medical diagnosis BIB002 and efficient patrolling of nurses BIB003 . The advantage of BN in such domains is the handling of incomplete or missing data for prediction with precision. For a detailed BN representation, we consider a simple example as presented in Fig. 4 . This BN has four nodes/variables in a clinical situation. Consider nodes A, B, C, D in Fig. 4 for Coughing, Spasm, High Pressure and Alarm respectively. The root nodes {A, B} for {Coughing, Spasm} respectively are related to a prior probability distribution and non-root nodes {C, D} for {High Pressure, Alarm} have local conditional probability distribution. The purpose of finding priori and local probability distribution is to quantify the parent-child relationship. The joint probability distribution of each node can be computed by taking the product prior or conditional probabilities assigned to each node in BN according to chain rule as in  . The common topology of Conditional Probability Table of the given BN is shown in Table III which can be articulated in a joint probability as P (A,B, Inference in BN refers to the computation of posterior probabilities conditioned on occurrence of evidence. Thus, BN provide a flexible way to select any node as a target node for inferences. BN are used in diverse problems like diagnostics, causal inter-causal and mixed inferences. Diagnostics inferences are accomplished for justifying cause and effect relationship. Causal inferences are used for reasoning of causes to effects. Inter-causal inferences are used to identify the causes of a common effect. Mixed inferences referred to the combination of two or more of the aforementioned inferences. Consequently, the inferences of BN in Fig. 4 as reported by  , have been described with the help of Table III and IV. Consequently, joint probability distribution table of BN in Fig. 4 can be represented in Table IV .