Medical Device Data for Clinical Decision Making

by John R. Zaleski

Overview

This cutting-edge volume is the first book that provides practical guidance on the use of medical device data for bioinformatics modeling purposes. Professionals learn how to develop original methods for communicating with medical devices within healthcare enterprises and assisting with bed-side-clinical decision making. This book guides readers in the implementation and use of clinical decision support methods within the context of electronic health records in the hospital environment. Supported with over 100 illustrations, this all-in-one resource discusses key concepts in detail and then presents clear implementation examples to give professionals a complete understanding of how to use this knowledge in the field.

Product Details

ISBN-13:	9781608070947
Publisher:	Artech House, Incorporated
Publication date:	10/31/2010
Pages:	310
Product dimensions:	7.30(w) x 10.30(h) x 1.00(d)

About the Author

John R. Zaleski is a senior director & research department head of Biomedical Informatics at Philips Research North America. He was previously a product manager for the critical care product line and director of clinical research at Siemens Health Services USA. Dr. Zaleski holds an M.S. in aerospace engineering from Boston University and a Ph.D. in biomedical systems engineering from the University of Pennsylvania.

Table of Contents

Preface xi

Chapter 1 Introduction to Physiological Modeling in Medicine: A Survey of Existing Methods, Approaches, and Trends 1

1.1 Overview 1

1.2 The Art of Modeling and Prediction 2

1.3 Why Model? 3

1.4 Multivariate Models and Model Complexity 6

1.5 Clinical Informatics and Meaningful Use 8

1.6 Types of Models 9

1.7 Stochastic Modeling and Monte Carlo Simulation 12

1.8 Guidelines and Protocols 14

1.9 Summary 16

References 17

Chapter 2 Simulation and Modeling Techniques 19

2.1 Simulating Physical Systems 19

2.2 Introduction to Monte Carlo Simulation 20

2.3 Introduction to Discrete Event Simulation 30

2.4 Queuing and Discrete Event Simulation Models 32

2.5 Deterministic Mathematical Models 39

2.6 Probability and Statistics 39

2.6.1 Binomial Probability Distribution 40

2.6.2 Gaussian Probability Distribution 42

2.6.3 Exponential Distribution 48

2.6.4 Poisson Distribution 49

2.6.5 Confidence Intervals 51

2.7 Sensitivity, Specificity and Confidence Intervals 55

2.8 Chi-Square Tests 58

2.9 The Concept of χ² and the Assignment Problem 62

2.10 Other Applications of Optimal Assignment Methodologies 65

2.11 Noise and Error Representation 68

2.11.1 Special Case: Gauss-Markov Random Processes 75

2.12 Queuing Theory Deep Dive and Examples 77

2.13 Summary 81

References 83

Chapter 3 Introduction to Automatic Control Systems Theory and Applications 85

3.1 State Space Modeling 85

3.2 Controllability and Observability 99

3.3 The Feedback Control Loop 105

3.4 System Stability 109

3.5 Techniques for Representing Physical Phenomena 113

References 117

Chapter 4 Physical System Modeling and State Representation 119

4.1 Fluid Mechanics Applications 119

4.2 Electrical Signal and Circuit Analogs to Physical Systems 132

4.3 Simplified Physiological Systems Modeling 136

References 139

Chapter 5 Medical Device Data Measurement, Interoperability, Interfacing, and Analysis 141

5.1 Types of Medical Devices Used for Physiological Measurement 141

5.2 Medical Device Interfaces, Standards, and Interoperability Initiatives 144

5.3 Medical Device Data Collection, Validation, and Clinical Use 145

5.4 Biomedical Device Interoperability and the Electronic Medical Record 147

5.5 Associating Biomedical Device Data with Patients 149

5.6 Spatial-Temporal Modeling of Medical Device Data 149

5.7 Biomedical Data Storage and Retrieval 155

5.8 Applying Wavelet Transforms to Biomedical Device Data 161

5.9 Summary 163

References 165

Chapter 6 Systems Modeling Example Applications 167

6.1 Modeling to Assist Diagnosis and Treatment 167

6.2 Clinical Workflow and Decision Support 168

6.3 Systems Modeling and Integration 173

6.4 Integrating Clinical Decision Support Systems Within Clinical Information Systems 178

6.5 Summary 181

References 182

Chapter 7 Modeling Benefits, Cautions, and Future Work 185

References 187

Appendix A 189

A.1 Monte Carlo Simulation: Computing π 189

Reference 193

Appendix B 195

B.1 Monte Carlo Simulation: Stereo Viewing Covariance Model 195

Appendix C 211

C.1 Optimal Assignment Algorithm 211

Appendix D 219

D.1 Simple Discrete Event Model 219

Reference 227

Appendix E 229

E.1 Gaussian Random Number Generation 229

Reference 232

Appendix F 233

F.1 Poisson and Exponentially Distributed Random Numbers 233

Appendix G 237

G.1 Java Applet Plotter 237

G.1.1 Method Overview 237

G.1.2 Creating the Active X Data Object (ADO) 237

G.1.3 Applet Design 241

G.1.4 Design of the Active Server Page 246

G.1.5 Setting Up a Virtual Directory 253

References 258

Appendix H 259

H.1 Kalman Filter 259

Appendix I 283

I.1 Line Plotter 283

Appendix J 307

J.1 Control Systems Modeling: Spring Mass Response to Step Function 307

List of Acronyms 327

Bibliography 331

About the Author 335

Index 337