Data Mining and Medical Knowledge Management: Cases and Applications

Data Mining and Medical Knowledge Management: Cases and Applications

Petr Berka (University of Economics, Prague, Czech Republic), Jan Rauch (University of Economics, Prague, Czech Republic) and Djamel Abdelkader Zighed (University of Lumiere Lyon 2, France)
Indexed In: SCOPUS View 2 More Indices
Release Date: February, 2009|Copyright: © 2009 |Pages: 464
ISBN13: 9781605662183|ISBN10: 1605662186|EISBN13: 9781605662190|DOI: 10.4018/978-1-60566-218-3

Description

The healthcare industry produces a constant flow of data, creating a need for deep analysis of databases through data mining tools and techniques resulting in expanded medical research, diagnosis, and treatment.

Data Mining and Medical Knowledge Management: Cases and Applications presents case studies on applications of various modern data mining methods in several important areas of medicine, covering classical data mining methods, elaborated approaches related to mining in electroencephalogram and electrocardiogram data, and methods related to mining in genetic data. A premier resource for those involved in data mining and medical knowledge management, this book tackles ethical issues related to cost-sensitive learning in medicine and produces theoretical contributions concerning general problems of data, information, knowledge, and ontologies.

Topics Covered

The many academic areas covered in this publication include, but are not limited to:

  • Classification and prediction with neural networks
  • Clinical prediction rules
  • Cost-sensitive learning in medicine
  • ECG processing
  • EEG data mining using PCA
  • Gene expression mining
  • Human embryo selection
  • Image registration for biomedical information integration
  • Medical knowledge management
  • Medical Web site management
  • Mining tuberculosis data
  • Ontologies in the health field
  • Preprocessing perceptrons and multivariate decision limits
  • Risk prediction models using data mining

Reviews and Testimonials

This publication is a collection of case studies in which advanced DM and KM solutions are applied to concrete cases in biomedical research. The reader will find all the peculiarities of the medical field, which require specific solutions to complex problems.

– Riccardo Bellazzi, Università di Pavia, Italy

The book will be of interest to researchers and students in computer science and medicine, and to physicians and managers in the healthcare industry.

– Book News Inc. (June 2009)

Table of Contents and List of Contributors

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Preface

The basic notion of the book “Data Mining and Medical Knowledge Management: Cases and Applications” is knowledge. A number of definitions of this notion can be found in the literature:
  • Knowledge is the sum of what is known: the body of truth, information, and principles acquired by mankind.
  • Knowledge is human expertise stored in a person’s mind, gained through experience, and interaction with the person’s environment.
  • Knowledge is information evaluated and organized by the human mind so that it can be used purposefully, e.g., conclusions or explanations.
  • Knowledge is information about the world that allows an expert to make decisions.

There are also various classifications of knowledge. A key distinction made by the majority of knowledge management practitioners is Nonaka's reformulation of Polanyi's distinction between tacit and explicit knowledge. By definition, tacit knowledge is knowledge that people carry in their minds and is, therefore, difficult to access. Often, people are not aware of the knowledge they possess or how it can be valuable to others. Tacit knowledge is considered more valuable because it provides context for people, places, ideas, and experiences. Effective transfer of tacit knowledge generally requires extensive personal contact and trust. Explicit knowledge is knowledge that has been or can be articulated, codified, and stored in certain media. It can be readily transmitted to others. The most common forms of explicit knowledge are manuals, documents, and procedures. We can add a third type of knowledge to this list, the implicit knowledge. This knowledge is hidden in a large amount of data stored in various databases but can be made explicit using some algorithmic approach. Knowledge can be further classified into procedural knowledge and declarative knowledge. Procedural knowledge is often referred to as knowing how to do something. Declarative knowledge refers to knowing that something is true or false.

In this book we are interested in knowledge expressed in some language (formal, semi-formal) as a kind of model that can be used to support the decision making process. The book tackles the notion of knowledge (in the domain of medicine) from two different points of view: data mining and knowledge management.

Knowledge Management (KM) comprises a range of practices used by organizations to identify, create, represent, and distribute knowledge. Knowledge Management may be viewed from each of the following perspectives:

  • Techno-centric: A focus on technology, ideally those that enhance knowledge sharing/growth.
  • Organizational: How does the organization need to be designed to facilitate knowledge processes? Which organizations work best with what processes?
  • Ecological: Seeing the interaction of people, identity, knowledge, and environmental factors as a complex adaptive system.

Keeping this in mind, the content of the book fits into the first, technological perspective. Historically, there have been a number of technologies “enabling” or facilitating knowledge management practices in the organization, including expert systems, knowledge bases, various types of Information Management, software help desk tools, document management systems, and other IT systems supporting organizational knowledge flows.

Knowledge Discovery or Data Mining is the partially automated process of extracting patterns from usually large databases. It has proven to be a promising approach for enhancing the intelligence of systems and services. Knowledge discovery in real-world databases requires a broad scope of techniques and forms of knowledge. Both the knowledge and the applied methods should fit the discovery tasks and should adapt to knowledge hidden in the data. Knowledge discovery has been successfully used in various application areas: business and finance, insurance, telecommunication, chemistry, sociology, or medicine. Data mining in biology and medicine is an important part of biomedical informatics, and one of the first intensive applications of computer science to this field, whether at the clinic, the laboratory, or the research center.

The healthcare industry produces a constantly growing amount of data. There is however a growing awareness of potential hidden in these data. It becomes widely accepted that health care organizations can benefit in various ways from deep analysis of data stored in their databases. It results into numerous applications of various data mining tools and techniques. The analyzed data are in different forms covering simple data matrices, complex relational databases, pictorial material, time series, and so forth. Efficient analysis requires knowledge not only of data analysis techniques but also involvement of medical knowledge and close cooperation between data analysis experts and physicians. The mined knowledge can be used in various areas of healthcare covering research, diagnosis, and treatment. It can be used both by physicians and as a part of AI-based devices, such as expert systems. Raw medical data are by nature heterogeneous. Medical data are collected in the form of images (e.g. X-ray), signals (e.g. EEG, ECG), laboratory data, structural data (e.g. molecules), and textual data (e.g. interviews with patients, physician’s notes). Thus there is a need for efficient mining in images, graphs, and text, which is more difficult than mining in “classical” relational databases containing only numeric or categorical attributes. Another important issue in mining medical data is privacy and security; medical data are collected on patients, misuse of these data or abuse of patients must be prevented.

The goal of the book is to present a wide spectrum of applications of data mining and knowledge management in medical area.

The book is divided into 3 parts. The first part entitled “Theoretical Aspects” discusses some basic notions of data mining and knowledge management with respect to the medical area. This section presents a theoretical background for the rest of the book.

Chapter 1 introduces the basic concepts of medical informatics: data, information, and knowledge. It shows how these concepts are interrelated and how they can be used for decision support in medicine. All discussed approaches are illustrated on one simple medical example.

Chapter 2 introduces the basic notions about ontologies, presents a survey of their use in medicine and explores some related issues: knowledge bases, terminology, and information retrieval. It also addresses the issues of ontology design, ontology representation, and the possible interaction between data mining and ontologies.

Health managers and clinicians often need models that try to minimize several types of costs associated with healthcare, including attribute costs (e.g. the cost of a specific diagnostic test) and misclassification costs (e.g. the cost of a false negative test). Chapter 3 presents some concepts related to cost-sensitive learning and cost-sensitive classification in medicine and reviews research in this area.

There are a number of machine learning methods used in data mining. Among them, artificial neural networks gain a lot of popularity although the built models are not as understandable as, for example, decision trees. These networks are presented in two subsequent chapters. Chapter 4 describes the theoretical background of artificial neural networks (architectures, methods of learning) and shows how these networks can be used in medical domain to solve various classification and regression problems. Chapter 5 introduces classification networks composed of preprocessing layers and classification networks and compares them with “classical” multilayer perceptions on three medical case studies.

The second part “General Applications,” presents work that is general in the sense of a variety of methods or variety of problems described in each of the chapters.

In chapter 6, biomedical image registration and fusion, which is an effective mechanism to assist medical knowledge discovery by integrating and simultaneously representing relevant information from diverse imaging resources, is introduced. This chapter covers fundamental knowledge and major methodologies of biomedical image registration, and major applications of image registration in biomedicine.

The next two chapters describe methods of biomedical signal processing. Chapter 7 describes methods for preprocessing, analysis, feature extraction, visualization, and classification of electrocardiogram (ECG) signals. First, preprocessing methods mainly based on the discrete wavelet transform are introduced. Then classification methods such as fuzzy rule-based decision trees and neural networks are presented. Two examples, visualization and feature extraction from Body Surface Potential Mapping (BSPM) signals and classification of Holter ECGs, illustrate how these methods are used. Chapter 8 deals with the application of principal components analysis (PCA) to the field of data mining in electroencephalogram (EEG) processing. Possible applications of this approach include separation of different signal components for feature extraction in the field of EEG signal processing, adaptive segmentation, epileptic spike detection, and long-term EEG monitoring evaluation of patients in a coma.

In chapter 9, existing clinical risk prediction models are examined and matched to the patient data to which they may be applied, using classification and data mining techniques, such as neural Nets. Novel risk prediction models are derived using unsupervised cluster analysis algorithms. All existing and derived models are verified as to their usefulness in medical decision support on the basis of their effectiveness on patient data from two UK sites.

Chapter 10 deals with the problem of quality assessment of medical Web sites. The so called “quality labeling” process can benefit from employment of Web mining and information extraction techniques, in combination with flexible methods of Web-based information management developed within the Semantic Web initiative.

In medicine, doctors are often confronted with exceptions both in medical practice or in medical research; a proper method of how to deal with exceptions are case-based systems.

Chapter 11 presents two such systems. The first one is a knowledge-based system for therapy support. The second one is designed for medical studies or research. It helps to explain cases that contradict a theoretical hypothesis.

The third part, “Specific Cases,” shows results of several case studies of (mostly) data mining, applied to various specific medical problems. The problems covered by this part range from discovery of biologically interpretable knowledge from gene expression data, over human embryo selection for the purpose of human in-vitro fertilization treatments, to diagnosis of various diseases based on machine learning techniques.

Discovery of biologically interpretable knowledge from gene expression data is a crucial issue. Current gene data analysis is often based on global approaches such as clustering. An alternative way is to utilize local pattern mining techniques for global modeling and knowledge discovery. The next two chapters deal with this problem from two points of view: using data only, and combining data with domain knowledge. Chapter 12 proposes three data mining methods to deal with the use of local patterns, and chapter 13 points out the role of genomic background knowledge in gene expression data mining. Its application is demonstrated in several tasks such as relational descriptive analysis, constraint-based knowledge discovery, feature selection, and construction or quantitative association rule mining.

Chapter 14 describes the process used to mine a database containing data related to patient visits during Tinnitus Retraining Therapy.

Chapter 15 describes a new multi-classification system using Gaussian networks to combine the outputs (probability distributions) of standard machine learning classification algorithms. This multi-classification technique has been applied to the selection of the most promising embryo-batch for human in-vitro fertilization treatments.

Chapter 16 reviews current policies of tuberculosis control programs for the diagnosis of tuberculosis. A data mining project that uses WHO’s Direct Observation of Therapy data to analyze the relationship among different variables and the tuberculosis diagnostic category registered for each patient is then presented.

Chapter 17 describes how to integrate medical knowledge with purely inductive (data-driven) methods for the creation of clinical prediction rules. The described framework has been applied to the creation of clinical prediction rules for the diagnosis of obstructive sleep apnea.

Chapter 18 describes goals, current results, and further plans of long time activity concerning application of data mining and machine learning methods to the complex medical data set. The analyzed data set concerns longitudinal study of atherosclerosis risk factors.

The book can be used as a textbook of advanced data mining applications in medicine. The book addresses not only researchers and students in the field of computer science or medicine but it will be of great interest also for physicians and managers of healthcare industry. It should help physicians and epidemiologists to add value to their collected data.

Petr Berka
Jan Rauch
Djamel Abdelkader Zighed

Author(s)/Editor(s) Biography

Petr Berka is a full professor at the Department of Information and Knowledge Engineering, University of Economics and also works in the Centre of Biomedical Informatics, Institute of Computer Science, Academy of Sciences of the Czech Republic. His main research interests are machine learning, data mining and knowledge-based systems.
Jan Rauch is an associate professor at the Deptartment of Information and Knowledge Engineering, University of Economics and also works in the Centre of Biomedical Informatics, Institute of Computer Science, Academy of Sciences of the Czech Republic. His main research interest is data mining.
Djamel Abdelkader Zighed received his Master in Computer and Automatic Science in 1982 and his PhD in computer science in 1985 – both from University Lyon 1. He was as assistant professor at University Lyon 1 in 1984-1987. In 1987, he joined the University Lyon 2 where he worked as lecturer (1987-1991), professor (1991-2000) and 1st class professor (2000 - present). He is interested in data mining (including mining complex data), machine learning and knowledge engineering. He is the founder and was director (1995-2002) of the ERIC laboratory (laboratory for development of methods and software for knowledge engineering, and more specifically automatic Knowledge Discovery in Databases).

Indices

Editorial Board

  • Riccardo Bellazzi, University of Pavia, Italy
  • Radim Jiroušek, Institute of Information Theory and Automation, Czech Republic
  • Katharina Morik, University of Dortmund, Germany
  • Ján Paralic, Technical University Košice, Slovak Republic
  • Luis Torgo, University of Porto, Portugal
  • Blaž Župan, University of Ljubljana, Slovenia