Bayesian Belief Networks (bbns) are becoming increasingly popular in the Knowledge Discovery and Data Mining community. A bbn is defined by a graphical structure of conditional dependencies among the domain variables and a set of probability distributions defining these dependencies. In this way, bbns provide a compact formalism --- grounded in the welldeveloped mathematics of probability theory --- able to predict variable values, explain observations, and visualize dependencies among variables. During the past few years, several efforts have been addressed to develop methods able to extract both the graphical structure and the conditional probabilities of a bbn from a database. All these methods share the assumption that the database at hand is complete, that is, it does not report any entry as unknown. When this assumption fails, these methods have to resort to expensive iterative procedures which are infeasible for large databases. This paper describes a new Knowledge Discovery sys...

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. Intelligent data analysis methods support information extraction from data by exploiting domain's Background Knowledge. We address several issues regarding definition, use and impact of these methods, and investigate for their acceptance in application domains of medicine and pharmacology by a MEDLINE search. The authors of the paper believe that the basic philosophy of IDA is to be application driven: its goal is to develop, adapt, or re-use existing methods to solve a specific problem. Sticking to application driven approach may help to prove the points on cost-effectiveness and may increase the awareness and acceptance of these methods in medical community. 1 Introduction A crucial problem in Medical Informatics (MI) is to provide final users (physicians, patients, researchers) with instruments for interpreting data, so that final decisions (diagnosis, therapy) will be better with the help of computers than without [20]. In other words, a central role of MI is to help users in t...

This paper describes the application oflntelligent Data Analysis techniques for extracting information on trends and cycles of time series coming from home monitoring of diabetic patients. In particular, we propose the combination of structural Time Series analysis and Temporal Abstractions for the interpretation of longitudinal Blood Glucose measurements. First, the measured time series is analyzed by using a novel Bayesian technique for structural filtering; second, the results obtained are post-processed using Temporal Abstractions, in order to extract knowledge that can be exploited "at the point of use" from physicians. The proposed data analysis procedure can be viewed as a typical Intelligent Data Analysis process applied to time-varying data: Background Knowledge is exploited in each step of the analysis, and the final result is a meaningful, abstract description of the complex process at hand. The work here described is part of a web-based telemedicine system for the management of Insulin Dependent Diabetes Mellitus patients, developed within the EU-funded project called T-IDDM.

We propose a Multi Modal Reasoning (MMR) methodology designed to provide physicians with knowledge management and decision support functionality in the context of type I diabetes mellitus care. The MMR system performs a tight integration of Case Based Reasoning (CBR), Rule Based Reasoning (RBR) and Model Based Reasoning (MBR), with the aim of suggesting a therapy properly tailored to the patient's needs, overcoming the single approaches' limitations. This methodology allows the exploitation of the implicit knowledge embedded in patients' visits (past cases) and in monitoring data through Case Based retrieval. On the other hand the explicit domain knowledge is formalized in a set of production rules and in a mathematical model.

This paper describes the application of a novel Bayesian estimation technique to extract the structural components, i.e., trend and daily patterns, from blood glucose level time series coming from home monitoring of insulin dependent diabetes mellitus patients. The problem is formulated through a set of stochastic equations, and is solved in a Bayesian framework by using a Markov chain Monte Carlo technique. The potential of the method is illustrated by analyzing data coming from the home monitoring of a 14-year old male patient.

In spite of many statistical methods for outlier detection and for robust analysis, there is little work on further analysis of outliers themselves to determine their origins. For example, there are “good ” outliers that provide useful information that can lead to the discovery of new knowledge, or “bad ” outliers that include noisy data points. Successfully distinguishing between different types of outliers is an important issue in many applications, including fraud detection, medical tests, process analysis and scientific discovery. It requires not only an understanding of the mathematical properties of data but also relevant knowledge in the domain context in which the outliers occur. This thesis presents a novel attempt in automating the use of domain knowledge in helping distinguish between different types of outliers. Two complementary knowledge-based outlier analysis strategies are proposed: one using knowledge regarding how “normal data ” should be distributed in a domain of interest in order to identify “good ” outliers, and the other using the understanding of “bad ” outliers. This kind of knowledge-based outlier analysis is a useful extension to existing work in both statistical and computing communities on outlier detection.

. The use of AI techniques to deal with the identification of variant haemoglobins has been so far restricted to the development of expert systems of limited scope. The process of identifying haemoglobins is difficult, particularly because the large number of missing values in the data set hinders comparisons between data about an unknown haemoglobin and data about known haemoglobins, in a context where classification is not appropriate. Case-based reasoning requires an assessment of similarity between a new case and known cases, and it is possible to use a distance measure making implicit assumptions about the missing values. However, the characteristics of the data set make this unsafe, as well as the use of such assumptions when trying to predict directly each column using all the others. Consequently, the induction of rules out of the available data, to be used to fill the missing values, seems to be a good option, in particular the use of association rules, since they impose few r...

Alternative framework for the fair valuation of participating life insurance contracts