A Research on Medical Structured Data Desensitization Method Based on Bayesian Networks

Bayesian networks (BN) are an excellent tool for classification of a complex inter-correlated data such as medical data. This study is aimed at learning diabetes data using CI Search Algorithm and to determine the factors that cause Type 1 and Type 2 Diabetes. Diabetes dataset was obtained from medical records unit of the Abubakar Tafawa Balewa University Teaching Hospital (ATBUTH) Bauchi, Bauchi State consisting of 569 cases with 8 different variables. To achieved the stated objectives, Bayesian Network (BN) were used i.e. CI Search Algorithm. CI Search Algorithm explored the nature of relationship between the attributes and diabetes type (T1D/T2D). Conditional Probability Tables (CPT) obtained by CI Search Algorithm reveals that a diabetes patient of age between 45.5-72 years is 86% likely to be T2D, a diabetes patient of BMI between 24.6-38.6 is 89% likely to be T2D, a diabetes patient with history of diabetes is 64% likely to be T1D, a diabetes patient without history of diabetes is 66% likely to be T2D.

Using sensitivity analyses in Bayesian Networks to highlight the impact of data paucity and direct future analyses: a contribution to the debate on measuring and reporting the precision of likelihood ratios

By definition, the scope of a Bayesian Network uses a complementary technique to restrict the modeling reach. In this paper, the used restriction technique was the Goals, Questions, and Metrics (GQM). The hypothesis to be tested relates cause and effect conditional probabilities in a software test phase of a manufacturing production line. The Bayesian Network concept is related to the specific concept of a Directed Non Cyclic Graph (DNCG), where each one of its nodes represents a random discrete variable and is illustrated by directed arcs of cause and effect relationships between variables. A Bayesian Network is a graphical artifact which restricts problems, incorporating data structures. The major contributions of this paper are conceptualization and implementation of a methodology for using a GQM hypothesis restriction to infer Bayesian network testing with the Netica Bayesian Networks ® computer software.

In order to give adaptive instruction to the learner, it needs to know what knowledge the learner has and what goals the learner is trying to archive. This paper proposes a method to build a model based on Bayesian networks in order to assess the learner's knowledge level. An overlay student model based on Bayesian networks is presented. This student model built on knowledge relationships with prediction ability is discussed in details. In this model, an assessing method based on Logistic model in IRT with three parameters is provided to evaluate student's performance. A case study in the course of Data Structure is illustrated in this paper.

Combining classifier methods have shown their effectiveness in a number of applications. Nonetheless, using simultaneously multiple classifiers may result in some cases in a reduction of the overall performance, since the responses provided by some of the experts may generate consensus on a wrong decision even if other experts provided the correct one. To reduce these undesired effects, in a previous study, we proposed a combining method based on the use of a Bayesian Network. In this paper, we present an improvement of that method which allows to solve some of the drawbacks exhibited by standard learning algorithms for Bayesian Networks. The proposed method is based on an Evolutionary Algorithm which uses a specifically devised data structure to encode direct acyclic graphs. This data structure allows to effectively implement crossover and mutation operators. The experimental results, obtained by using three standard databases, confirmed the effectiveness of the method.

Prediction of future diseases from historical data of medical patients is a topic that has gained increasing interest given the growing availability of such data in electronic format. Most of the developed systems are based on machine learning techniques, which are good to find relations between data but do not help explaining causalities. In particular, it would be difficult to get a meaningful medical explanation for the relationship between a patient's health checkup data and the risk of developing a certain disease. On the other hand, expert system approaches, like Bayesian networks, are based on medical knowledge but have trouble dealing with high levels of uncertainty, which is crucial in this kind of scenario. In this work we present a prediction system for the risk of a patient having a (heart or brain) stroke based on past medical checkup data. The system is based on the Dempster-Shafer Theory of plausibility which is good for handling uncertainty. The data used belongs to a rural hospital in Okayama, Japan, where people are compelled to undergo annual health checkups by law. The model also produces rules that are able to relate data from exam results with the aforementioned risk, thus proposing a cause from the medical point of view. Experiments comparing the results of the Dempster-Shafer method with other machine learning methods like Multilayer perceptron, Quadratic discriminant analysis and Naive Bayes show that our approach performed the best in general, with an overall prediction accuracy of 61% and with the best precision value on true positive cases of stroke.

<p>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.<br />Surgery survival prediction was examined with the algorithm. Bypass surgery survival chance must be computed for a given patient, having a data-set of 66 medical examinations for 313 patients.</p>

Transforming the non-structured medical text data into structured data is the basis of the processing and analysis of medical data. The effect of general-purpose word segmentation tools recognizing terminology is not ideal, which greatly affects the accuracy of the word segmentation, and further influences the result of text structuralization. In view of above problems, this paper puts forward a method of discovering new words based on word embedding. It uses Google open source word vector tool word2vec to train text and map the words into abstracted n-dimensional vector space. We can get the latent semantic relations between words and words in the corpus. And then combining the information entropy and word frequency, we can find new words. Finally, we design information extraction rules to get the key information according to the new words, and organize them into structured data. Experimental results on real medical data show that the accuracy is improved by 10% compared to traditional method, and the time is saved by 18% compared to traditional method.

The primary concept of the hospital is the provision of health services to the community. In many cases, the utilization of information technology to record all hospital activity data can improve hospitals' quality services. However currently, the data is only stored in the database and used as history without further use. Many experiences show that optimizing data usage can greatly assist doctors in making decisions to minimize medical errors. For example, examination data that among others of anamnesis (medical abstract), blood pressure, temperature, and other patient’s symptom data can be used to classify the kind of disease. One of the challenges in medical data utilization is that these data consists of various formats, structured, and unstructured as well. In this study, we address the medical unstructured data format by using Natural Language Processing approach. The combination of its representation results with the structured format data is then used as the dataset to build the model for disease type prediction based on Naïve Bayes and Artificial Neural Network classifier. By using these two algorithms, the results of the classification of the kind of disease. The performed experiments show that the ANN model performs better with the best accuracy average of 89.29% compared to Naive Bayes, which is 80.60 %.

BackgroundTraditional approaches to identify missing mechanisms are usually based on the hypothesis test and confronted with both theoretical and practical challenges. It has been proved that the Bayesian network is powerful in integrating, analyzing and visualizing information, and some previous researches have verified the promising features of Bayesian network to deal with the aforementioned challenges in missing mechanism identification. Based on the above reasons, this paper explores the application of Bayesian network to the identification of missing mechanisms for the first time, and proposes a new method, the Bayesian network-based missing mechanism identification (BN-MMI) method, to identify missing mechanism in medical research.MethodsThe procedure of BN-MMI method consists three easy-to-implement steps: estimating the missing data structure by the Bayesian network; assessing the credibility of the estimated missing data structure; and identifying the missing mechanism from the estimated missing data structure. The BN-MMI method is verified by simulation research and empirical research.ResultsThe simulation study verified the validity, consistency and robustness of BN-MMI method, and indicated its outperformance in contrast to the traditional logistic regression method. In addition, the empirical study illustrated the applicability of BN-MMI method in the real world by an example of medical record data.ConclusionsIt was confirmed that the BN-MMI method itself, together with human knowledge and expertise, could identify the missing mechanisms according to the probabilistic dependence/independence relations among variables of interest. At the same time, our research shed light upon the potential application of BN-MMI method to a broader range of missing data issues in medical studies.

Bayesian Networks are one of the most popular formalisms for reasoning under uncertainty. Hierarchical Bayesian Networks (HBNs) are an extension of Bayesian Networks that are able to deal with structured domains, using knowledge about the structure of the data to introduce a bias that can contribute to improving inference and learning methods. In effect, nodes in an HBN are (possibly nested) aggregations of simpler nodes. Every aggregate node is itself an HBN modelling independences inside a subset of the whole world under consideration. In this paper we discuss how HBNs can be used as Bayesian classifiers for structured domains. We also discuss how HBNs can be further extended to model more complex data structures, such as lists or sets, and we present the results of preliminary experiments on the mutagenesis dataset.

Interconnection between multiple data link systems is an urgent problem for wireless control systems. Its difficulty lies in the fact that data link messages are multi-source heterogeneous, By analyzing its sub-domain characteristics, we constructs the data message domain ontology and establishes a data link message ontology model based on Bayesian network(DLMOBN). It includes the study of nodes, directed edges and node similarity probability distribution and so on, convert multi-source heterogeneous messages into mathematical models. We propose a data link message ontology mapping algorithm, the OWL syntax is used to formally describe the acquired domain ontology, extract useful information such as concepts, attributes, and instances, and then store the information in a preset data structure, k-means algorithm is used to cluster them to form “cluster”, which is used as a classification index to classify the similarity pair as a node in the Bayesian network, and pass the concept of the lower layer between nodes to the prior concept of the upper layer. The semantic distance, the attribute, the feature and other factors of the similar pair are used to calculate the semantic similarity. Finally, the final semantic similarity value is obtained by weighting. It is verified by experiments that the method improves the recall rate and precision, reduces the time complexity.

The purpose of the present study is to analyze the prognostic factors of acute leukemia and to construct a decision model based on a causal relationship between the factors of this disease to assist medical specialists. In medical decisions, to reach effective, quick, and reliable results, there is a need for a simple decision-making model based on a specialist's self-assessment. It may help the medical team before final diagnosis by costly and time-consuming procedures such as bone marrow sampling and pathological test as well as provide an appropriate prognosis and diagnosis tool. Because of the complex and not the well-defined structure of medical data, the use of intelligent methods must be considered. For this purpose, first, a data-driven Bayesian network (BN) and Greedy algorithm are employed to determine causal relationships and probability between nodes using the real set of data. Then, these causal relationships will form based on the fuzzy cognitive map (FCM). Finally, according to scenarios defined, the results are analyzed. These analyses are also repeated for each type of acute leukemia including acute lymphocytic leukemia (ALL) and acute myelocytic leukemia (AML). Graphical abstract.

Medical entity relation extraction is of great significance for medical text data mining and medical knowledge graph. However, medical field requires very high data accuracy rate, the current medical entity relation extraction system is difficult to achieve the required accuracy. A main technical difficulty lies in how to obtain high-precision medical data, and automatically generate annotated training sample set. In this paper, a medical entity relation automatic extraction system based on weak supervision is proposed. At first, we designed a visual annotation tool, it can automatically generate crawl scripts, crawling the medical data from the site where the entity and its attributes are Separate stored. Then, based on the acquired data structure, we propose a weakly supervised hypothesis to automatically generate positive sample training data. Finally, we use CNN model to extract medical entity relation. Experiments show that the method is feasible and accurate.

Every day a huge amount of unstructured and semi structured data is used in all the business sectors. Those data are very complicated to store and process for applying into the decision-making system. Especially, the medical data, clinical data and patient history data are to be accessed in a faster way to bring the feasible solution. In view of this, a high speed, reliable and fault tolerant programming framework is needed [1].Apache Pig is a high level and globally accepted programming language to execute the map reduce tasks over the Hadoop cluster while dealing with unstructured data. This language works on Hadoop Distributed File System (HDFS) and this language is written in Java.In this proposed work, the medical history data of patients are considered to be processed. The existing approaches such as oracle SQL queries and mongo DB based results have been producing the very slower time to process these records. But in pig programming language, these gaps are rectified and produced an efficient result.The data dictionary of this implemented dataset is having 7 fields of records to be process in a phased approach. Each and every phase of analysis, the various fields are considered for further processing and interpretation. With the help of relationship operator’s, the relationship of these dataset fields is identified. Followed by this, the functions are applied to produce the faster segregation on the given dataset. Two types of functions are used here such as math function and evaluation function [2].