IT training computational medicine in data mining and modeling rakocevic, djukic, filipovic milutinović 2013 10 29

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IT training computational medicine in data mining and modeling rakocevic, djukic, filipovic  milutinović 2013 10 29

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Goran Rakocevic · Tijana Djukic Nenad Filipovic · Veljko Milutinović Editors Computational Medicine in Data Mining and Modeling Computational Medicine in Data Mining and Modeling Goran Rakocevic • Tijana Djukic Nenad Filipovic • Veljko Milutinovic´ Editors Computational Medicine in Data Mining and Modeling Editors Goran Rakocevic Mathematical Institute Serbian Academy of Sciences and Arts Belgrade, Serbia Nenad Filipovic Faculty of Engineering University of Kragujevac Kragujevac, Serbia Tijana Djukic Faculty of Engineering University of Kragujevac Kragujevac, Serbia Veljko Milutinovic´ School of Electrical Engineering University of Belgrade Belgrade, Serbia ISBN 978-1-4614-8784-5 ISBN 978-1-4614-8785-2 (eBook) DOI 10.1007/978-1-4614-8785-2 Springer New York Heidelberg Dordrecht London Library of Congress Control Number: 2013950376 © Springer Science+Business Media New York 2013 This work is subject to copyright All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer Permissions for use may be obtained through RightsLink at the Copyright Clearance Center Violations are liable to prosecution under the respective Copyright Law The use of general descriptive names, registered names, trademarks, service marks, etc in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made The publisher makes no warranty, express or implied, with respect to the material contained herein Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com) Preface Humans have been exploring the ways to heal wounds and sicknesses since times we evolved as a species and started to form social structures The earliest of these efforts date back to prehistoric times and are, thus, older than literacy itself Most of the information regarding the techniques that were used in those times comes from careful examinations of human remains and the artifacts that have been found Evidence shows that men used three forms of medical treatment – herbs, surgery, and clay and earth – all used either externally with bandages for wounds or through oral ingestion The effects of different substances and the proper ways of applying them had likely been found through trial and error Furthermore, it is likely that any form of medical treatment was accompanied by a magical or spiritual interpretation The earliest written accounts of medical practice date back to around 3300 BC and have been created in ancient Egypt Techniques that had been known at the time included setting of broken bones and several forms of open surgery; an elaborate set of different drugs was also known Evidence also shows that the ancient Egyptians were in fact able to distinguish between different medical conditions and have introduced the basic approach to medicine, which includes a medical examination, diagnoses, and prognoses (much the same it is done to this day) Furthermore, there seems to be a sense of specialization among the medical practitioners, at least according to the ancient Greek historian Herodotus, who is quoted as saying that the practice of medicine is so specialized among them that each physician is a healer of one disease and no more Medical institutions, referred to as Houses of Life, are known to have been established in ancient Egypt as early as the First Dynasty The ancient Egyptian medicine heavily influenced later medical practices in ancient Greece and Rome The Greeks have left extensive written traces of their medical practices A towering figure in the history of medicine was the Greek physician Hippocrates of Kos He is widely considered to be the “father of modern medicine” and has invented the famous Oath of Hippocrates, which still serves as the fundamental ethical norm in medicine Together with his students, Hippocrates began the practice of categorizing illnesses as acute, chronic, endemic, and epidemic Two things can be observed from this: first, the approach to medicine was v vi Preface taking up a scholarly form, with groups of masters and students studying different medical conditions, and second, a systematic approach was taken These observations lead to the conclusion that medicine had been established as a scientific field In parallel with the developments in ancient Greece and, later, Rome, the practice of medicine has also evolved in India and China According to the sacred text of Charaka, based on the Hindu beliefs, health and disease are not predetermined and life may be influenced by human effort Medicine was divided into eight branches: internal medicine, surgery and anatomy, pediatrics, toxicology, spirit medicine, aphrodisiacs, science of rejuvenation, and eye, ear, nose, and throat diseases The healthcare system involved an elaborate education structure, in which the process of training a physician took seven years Chinese medicine, in addition to herbal treatments and surgical operations, also introduced the practices of acupuncture and massages During the Islamic Golden Age, spanning from the eighth to the fifteenth century, scientific developments had been centered in the Middle East and driven by Islamic scholars Central to the medical developments at that time was the Islamic belief that Allah had sent a cure for every ailment and that it was the duty of Muslims to take care of the body and spirit In essence, this meant that the cures had been made accessible to men, allowing for an active and relatively secular development of medical science Islamic scholars also gathered as much of the already acquired knowledge as they could, both from the Greek and Roman sources, as well as the East A sophisticated healthcare system was established, built around public hospitals Furthermore, physicians kept detailed records of their practices These data were used both for spreading and developing knowledge, as well as could be provided for peer review in case a physician was accused of malpractice During the Islamic Golden Age, medical research went beyond looking at the symptoms of an illness and finding the means to alleviate them, to establishing the very cause of the disease The sixteenth century brought the Renaissance to Europe and with it a revival of interest in science and knowledge One of the central focuses of that age was the “man” and the human body, leading to large leaps in the understanding of anatomy and the human functions Much of the research that was done was descriptive in nature and relied heavily on postmortem examinations and autopsies The development of modern neurology began at this time, as well as the efforts to understand and describe the pulmonary and circulatory systems Pharmacological foundations were adopted from the Islamic medicine, and significantly expanded, with the use of minerals and chemicals as remedies, which included drugs like opium and quinine Major centers of medical science were situated in Italy, in Padua and Bologna During the nineteenth century, the practice of medicine underwent significant changes with rapid advances in science, as well as new approaches by physicians, and gave rise to modern medicine Medical practitioners began to perform much more systematic analyses of patients’ symptoms in diagnosis Anesthesia and aseptic operating theaters were introduced for surgeries Theory regarding Preface vii microorganisms being the cause of different diseases was introduced and later accepted As for the means of medical research, these times saw major advances in chemical and laboratory equipment and techniques Another big breakthrough was brought on by the development of statistical methods in epidemiology Finally, psychiatry had been established as a separate field This rate of progress continued well into the twentieth century, when it was also influenced by the two World Wars and the needs they had brought forward The twenty-first century has witnessed the sequencing of the entire human genome in 2003, and the subsequent developments in the genetic and proteomic sequencing technologies, following which we can study medical conditions and biological processes down to a very fine grain level The body of information is further reinforced by precise imaging and laboratory analyses On the other hand, following Moore’s law for more than 40 years has yielded immensely powerful computing systems Putting the two together points to an opportunity to study and treat illnesses with the support of highly accurate computational models and an opportunity to explore, in silico, how a certain patient may respond to a certain treatment At the same time, the introduction of digital medical records paved the way for large-scale epidemiological analyses Such information could lead to the discovery of complex and well-hidden rules in the functions and interactions of biological systems This book aims to deliver a high-level overview of different mathematical and computational techniques that are currently being employed in order to further the body of knowledge in the medical domain The book chooses to go wide rather than deep in the sense that the readers will only be presented the flavors, ideas, and potentials of different techniques that are or can be used, rather than giving them a definitive tutorial on any of these techniques The authors hope that with such an approach, the book might serve as an inspiration for future multidisciplinary research and help to establish a better understanding of the opportunities that lie ahead Belgrade, Serbia Goran Rakocevic Contents Mining Clinical Data Argyris Kalogeratos, V Chasanis, G Rakocevic, A Likas, Z Babovic, and M Novakovic Applications of Probabilistic and Related Logics to Decision Support in Medicine Aleksandar Perovic´, Dragan Doder, and Zoran Ognjanovic´ 35 Transforming Electronic Medical Books to Diagnostic Decision Support Systems Using Relational Database Management Systems Milan Stosovic, Miodrag Raskovic, Zoran Ognjanovic, and Zoran Markovic 79 Text Mining in Medicine Slavko Zˇitnik and Marko Bajec A Primer on Information Theory with Applications to Neuroscience Felix Effenberger 135 Machine Learning-Based Imputation of Missing SNP Genotypes in SNP Genotype Arrays Aleksandar R Mihajlovic 193 Computer Modeling of Atherosclerosis Nenad Filipovic, Milos Radovic, Velibor Isailovic, Zarko Milosevic, Dalibor Nikolic, Igor Saveljic, Tijana Djukic, Exarchos Themis, Dimitris Fotiadis, and Oberdan Parodi 105 233 ix .. .Computational Medicine in Data Mining and Modeling Goran Rakocevic • Tijana Djukic Nenad Filipovic • Veljko Milutinovic´ Editors Computational Medicine in Data Mining and Modeling Editors... Novakovic Innovation Center of the School of Electrical Engineering, University of Belgrade, Belgrade 1100 0, Serbia G Rakocevic et al (eds.), Computational Medicine in Data Mining and Modeling, DOI 10. 1007/978-1-4614-8785-2_1,... Polyzos, and Dimitrios I Fotiadis 309 349 Chapter Mining Clinical Data Argyris Kalogeratos, V Chasanis, G Rakocevic, A Likas, Z Babovic, and M Novakovic 1.1 Data Mining Methodology The prerequisite

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  • Preface

  • Contents

  • Chapter 1: Mining Clinical Data

    • 1.1 Data Mining Methodology

    • 1.2 Data Mining Algorithms

      • 1.2.1 Classification Methods

        • 1.2.1.1 Decision Trees

        • 1.2.1.2 Random Forests

        • 1.2.1.3 Support Vector Machines

        • 1.2.1.4 Naïve Bayes Classifier

        • 1.2.1.5 Bayesian Neural Networks

        • 1.2.1.6 Logistic Regression

      • 1.2.2 Generalization Measures

        • 1.2.2.1 Feature Selection and Ranking

        • 1.2.2.2 Single-Feature Evaluation

          • Information Gain

          • Chi-Square

        • 1.2.2.3 Feature Subset Selection

          • Recursive Feature Elimination SVM (RFE-SVM)

          • Minimum Redundancy, Maximum Relevance (mRMR)

          • K-Way Interaction Information/Interaction Graphs

          • Multifactor Dimensionality Reduction (MDR)

          • AMBIENCE Algorithm

      • 1.2.3 Treating Missing Values and Nominal Features

    • 1.3 Case Study: Coronary Artery Disease

      • 1.3.1 Coronary Artery Disease

      • 1.3.2 The Main Database (M-DB)

      • 1.3.3 The Database with Scintigraphies (S-DB)

      • 1.3.4 Defining Disease Severity

        • 1.3.4.1 The Number of Diseased Vessels

        • 1.3.4.2 Angiographic Score17

        • 1.3.4.3 HybridScore: A Hybrid Angiographic Score

        • 1.3.4.4 Discussion on Angiographic Scores

      • 1.3.5 Results for Data Mining Tasks

        • 1.3.5.1 Correlating Patients´ Profile and ATS Burden

          • Data Preprocessing

          • Evaluating the Trained Classification Models

          • Classification Results

            • Defining ATS Disease Severity Using DefA

    • References

  • Chapter 2: Applications of Probabilistic and Related Logics to Decision Support in Medicine

    • 2.1 Introduction

    • 2.2 Probability Logic

      • 2.2.1 Overview

      • 2.2.2 LPP2 Logic

      • 2.2.3 Decidability and Complexity

      • 2.2.4 A Heuristical Approach to Satisfiability Problem

      • 2.2.5 Probabilistic Classification

    • 2.3 Nonmonotonic Reasoning and Conditional Probabilities

      • 2.3.1 Nonmonotonic Reasoning

      • 2.3.2 Preferential and Rational Relations

      • 2.3.3 Probabilistic Semantics

      • 2.3.4 Some Non-Horn Rules and Nonstandard Probabilities

    • 2.4 Probabilistic Approach to Measuring Inconsistency

      • 2.4.1 Measures of Inconsistency

      • 2.4.2 Probabilistic Approach for Measuring Propositional Theories

      • 2.4.3 Measuring Inconsistency in Probabilistic Knowledge Bases

    • 2.5 Evidence and PST Logics

      • 2.5.1 Reasoning About Evidence

      • 2.5.2 PST Logics

    • 2.6 MYCIN

    • 2.7 CADIAG-2

    • References

  • Chapter 3: Transforming Electronic Medical Books to Diagnostic Decision Support Systems Using Relational Database Management Systems

    • 3.1 Conceptual Accents

    • 3.2 Basic Logic Outline

    • 3.3 Structure of the Data

    • 3.4 Structure of Relations Among Sets of Findings and Diseases

    • 3.5 Multiple References Relations to Other Publications

    • 3.6 Structure of the Knowledge Base

    • 3.7 Input

    • 3.8 Output

    • 3.9 User-Friendly Interface

    • 3.10 Inference Mechanisms

    • 3.11 Linkage with the Text

    • 3.12 Linkage with Information System

    • 3.13 Discussion

    • Appendix

      • Example 1

      • Example 2

    • References

  • Chapter 4: Text Mining in Medicine

    • 4.1 Introduction

    • 4.2 Medicine Linguistic Resources

      • 4.2.1 Scientific Literature Databases

      • 4.2.2 Ontologies

    • 4.3 Text Mining Platforms and Tools

      • 4.3.1 General Tools

      • 4.3.2 Medicine-Specialized Tools

    • 4.4 Information Retrieval

      • 4.4.1 Data Representation

      • 4.4.2 Models

    • 4.5 Information Extraction

      • 4.5.1 Data Representation

      • 4.5.2 Methods for Extraction

      • 4.5.3 Evaluation Metrics

    • 4.6 Data Integration

      • 4.6.1 Data Representation

      • 4.6.2 General Data Integration Framework

    • 4.7 Summary

    • References

  • Chapter 5: A Primer on Information Theory with Applications to Neuroscience

    • 5.1 Introduction

    • 5.2 Modeling

    • 5.3 Probabilities and Random Variables

      • 5.3.1 A First Approach to Probabilities via Relative Frequencies

      • 5.3.2 An Axiomatic Description of Probabilities

      • 5.3.3 Theory and Reality

      • 5.3.4 Independence of Events and Conditional Probabilities

      • 5.3.5 Random Variables

        • 5.3.5.1 Cumulative Distribution Function

        • 5.3.5.2 Independence of Random Variables

        • 5.3.5.3 Expectation and Variance

      • 5.3.6 Laws of Large Numbers

      • 5.3.7 Some Parametrized Probability Distributions

      • 5.3.8 Stochastic Processes

    • 5.4 Information Theory

      • 5.4.1 A Notion of Information

      • 5.4.2 Entropy as Expected Information Content

        • 5.4.2.1 Joint Entropy

      • 5.4.3 Mutual Information

        • 5.4.3.1 Point-Wise Mutual Information

        • 5.4.3.2 Mutual Information as Expected Point-Wise Mutual Information

        • 5.4.3.3 Mutual Information and Channel Capacities

        • 5.4.3.4 Normalized Measures of Mutual Information

        • 5.4.3.5 Multivariate Case

      • 5.4.4 A Distance Measure for Probability Distributions: The Kullback-Leibler Divergence

      • 5.4.5 Transfer Entropy: Conditional Mutual Information

    • 5.5 Estimation of Information-Theoretic Quantities

      • 5.5.1 A Bit of Theory Regarding Estimations

        • 5.5.1.1 Estimators

        • 5.5.1.2 Estimating Parameters: The Maximum Likelihood Estimator

      • 5.5.2 Regularization

      • 5.5.3 Nonparametric Estimation Techniques

    • 5.6 Information-Theoretic Analyses of Neural Systems

      • 5.6.1 The Question of Coding

      • 5.6.2 Computing Entropies of Spike Trains

      • 5.6.3 Efficient Coding?

      • 5.6.4 Scales

      • 5.6.5 Causality in the Neurosciences

      • 5.6.6 Information-Theoretic Aspects of Neural Dysfunction

    • 5.7 Software

    • References

  • Chapter 6: Machine Learning-Based Imputation of Missing SNP Genotypes in SNP Genotype Arrays

    • 6.1 Introduction

    • 6.2 The Missing Genotype Problem

    • 6.3 The Biological Problem Domain

      • 6.3.1 Chromosomes

      • 6.3.2 DNA

      • 6.3.3 SNPs and Point Mutations

      • 6.3.4 SNP Haplotypes and Haplogroups

      • 6.3.5 GWAS in Detail

      • 6.3.6 Missing SNP Genotypes

    • 6.4 The Mathematical Problem Domain

      • 6.4.1 Probabilistic Graphic Models

      • 6.4.2 Single Random Variable Graphical Modeling

      • 6.4.3 Markov Models

        • 6.4.3.1 Markov Chains

        • 6.4.3.2 Hidden Markov Models

      • 6.4.4 Forward-Backward Algorithm

    • 6.5 Applied Imputation Algorithms

      • 6.5.1 The KNNimpute algorithm

        • 6.5.1.1 Limitations

      • 6.5.2 The fastPHASE Algorithm

        • 6.5.2.1 Preliminary Assumptions

        • 6.5.2.2 Haplotype Clusters

        • 6.5.2.3 Necessary fastPHASE Notation and Concepts

        • 6.5.2.4 HMM for fastPHASE

        • 6.5.2.5 HMM for Two Alleles: The Genotype

        • 6.5.2.6 Limitations

      • 6.5.3 Comparison of fastPHASE and KNNimpute

    • 6.6 Summary

    • References

  • Chapter 7: Computer Modeling of Atherosclerosis

    • 7.1 Introduction

    • 7.2 Numerical Model of Plaque Formation and Growing in 3D Space

      • 7.2.1 Mesh-Moving Algorithm

      • 7.2.2 2D Axisymmetric Model

      • 7.2.3 Three-Dimensional Tube Constriction Benchmark Model

    • 7.3 Computational Modeling of Experiments

      • 7.3.1 Cheng et al. 2006 Experiment in 3D

      • 7.3.2 Experimental and Computational LDL Transport Model from the University of Kragujevac

        • 7.3.2.1 Introduction

        • 7.3.2.2 Experimental Setup

        • 7.3.2.3 Histological Methods

        • 7.3.2.4 Results

        • 7.3.2.5 Fluid-Solid Interaction Analysis

        • 7.3.2.6 Discussion

    • 7.4 Animal Experiments on the Pigs

    • 7.5 Results on the Coronary Patients

      • 7.5.1 Clinical Validation by CTA Follow-up

      • 7.5.2 Patient Characteristics

      • 7.5.3 Plaque Characterization and Detection of Lesion Progression

      • 7.5.4 Wall Shear Stress and Mass Transport Computation

      • 7.5.5 Features Affecting Plaque Progression

      • 7.5.6 Discussion

    • 7.6 Results on the Carotid Artery Patients

      • 7.6.1 Patients´ Data

      • 7.6.2 Fitting Procedure for Plaque Volume Growing Function

      • 7.6.3 Growth Functions and the Fitting Procedure

      • 7.6.4 Results

    • 7.7 Discussion and Conclusions

    • References

  • Chapter 8: Particle Dynamics and Design of Nano-drug Delivery Systems

    • 8.1 Introduction

    • 8.2 Lattice Boltzmann Method

      • 8.2.1 Theoretical Background

      • 8.2.2 Discretization Procedure and Implementation Details

      • 8.2.3 Definition of Macroscopic Quantities

      • 8.2.4 Boundary Conditions

        • 8.2.4.1 Periodical Boundary Condition

        • 8.2.4.2 Bounce-Back Boundary Condition

        • 8.2.4.3 Pressure and Velocity Boundary Conditions

    • 8.3 Modeling Solid-Fluid Interaction

    • 8.4 Numerical Results

      • 8.4.1 Drag Force on a Circular Particle

      • 8.4.2 Pure Rotation of Elliptical Particle

      • 8.4.3 Simulation of Movement of Elliptical Particle

      • 8.4.4 Simulation of Movement of a Circular Particle in Linear Shear Flow

      • 8.4.5 Particle Sedimentation in Viscous Fluid

      • 8.4.6 Simulation of Movement of Circular Particle Through a Stenotic Artery

      • 8.4.7 Simulation of Movement of Two Circular Particles Through a Stenotic Artery

      • 8.4.8 Simulation of Movement of a Circular Particle Through an Artery with Bifurcation

    • 8.5 Conclusion

    • References

  • Chapter 9: Computational Modeling of Ultrasound Wave Propagation in Bone

    • 9.1 Introduction

    • 9.2 Bone Physiology and Pathologies

      • 9.2.1 Bone Structure

      • 9.2.2 Bone Pathologies: Osteoporosis and Fracture Healing

        • 9.2.2.1 Osteoporosis

        • 9.2.2.2 Bone Fracture Healing

    • 9.3 Ultrasound Configurations and Measuring Parameters

    • 9.4 Computational Modeling of Wave Propagation in Intact and Osteoporotic Bones

      • 9.4.1 Computational Methods

      • 9.4.2 2D Studies on Cortical Bone

      • 9.4.3 2D Studies on Trabecular Bone

      • 9.4.4 3D Models of Cortical Bone

      • 9.4.5 3D Models of Trabecular Bone

    • 9.5 Ultrasound Wave Propagation in Healing Bones

    • 9.6 Conclusions

    • References

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