{"product_id":"healthcare-analytics-9781118919392","title":"Healthcare Analytics","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eFeatures of statistical and operational research methods and tools being used to improve the healthcare industry\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWith a focus on cutting-edge approaches to the quickly growing field of healthcare, \u003ci\u003eHealthcare Analytics: From Data to Knowledge to Healthcare Improvement \u003c\/i\u003eprovides an integrated and comprehensive treatment on recent research advancements in data-driven healthcare analytics in an effort to provide more personalized and smarter healthcare services. Emphasizing data and healthcare analytics from an operational management and statistical perspective, the book details how analytical methods and tools can be utilized to enhance healthcare quality and operational efficiency.\u003c\/p\u003e \u003cp\u003eOrganized into two main sections, \u003ci\u003ePart I \u003c\/i\u003efeatures biomedical and health informatics and specifically addresses the analytics of genomic and proteomic data; physiological signals from patient-monitoring systems; data uncertainty in clinical laboratory tests; predictive model\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eList of Contributors xvii\u003c\/p\u003e \u003cp\u003ePreface xxi\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Advances In Biomedical And Health Informatics 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Recent Development in Methodology for Gene Network Problems and Inferences 3\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSung W. Han and Hua Zhong\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 3\u003c\/p\u003e \u003cp\u003e1.2 Background 5\u003c\/p\u003e \u003cp\u003e1.3 Genetic Data Available 7\u003c\/p\u003e \u003cp\u003e1.4 Methodology 7\u003c\/p\u003e \u003cp\u003e1.4.1 Structural Equation Model 8\u003c\/p\u003e \u003cp\u003e1.4.2 Score Function Formulation 9\u003c\/p\u003e \u003cp\u003e1.4.3 Two-Stage Learning 12\u003c\/p\u003e \u003cp\u003e1.4.4 Further Issues 13\u003c\/p\u003e \u003cp\u003e1.5 Search Algorithm 13\u003c\/p\u003e \u003cp\u003e1.5.1 Global Optimal Solution Search 13\u003c\/p\u003e \u003cp\u003e1.5.2 Heuristic Algorithm for a Local Optimal Solution 14\u003c\/p\u003e \u003cp\u003e1.6 PC Algorithm 15\u003c\/p\u003e \u003cp\u003e1.7 Application\/Case Studies 16\u003c\/p\u003e \u003cp\u003e1.7.1 Skin Cutaneous Melanoma (SKCM) Data from the TCGA Data Portal Website 16\u003c\/p\u003e \u003cp\u003e1.7.2 The CCLE (Cancer Cell Line Encyclopedia) Project 20\u003c\/p\u003e \u003cp\u003e1.7.3 Cellular Signaling Network in Flow Cytometry Data 20\u003c\/p\u003e \u003cp\u003e1.8 Discussion 23\u003c\/p\u003e \u003cp\u003e1.9 Other Useful Softwares 23\u003c\/p\u003e \u003cp\u003eAcknowledgments 24\u003c\/p\u003e \u003cp\u003eReferences 24\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Biomedical Analytics and Morphoproteomics: An Integrative Approach for Medical Decision Making for Recurrent or Refractory Cancers 31\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMary F. McGuire and Robert E. Brown\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 31\u003c\/p\u003e \u003cp\u003e2.2 Background 32\u003c\/p\u003e \u003cp\u003e2.2.1 Data 33\u003c\/p\u003e \u003cp\u003e2.2.2 Tools 33\u003c\/p\u003e \u003cp\u003e2.2.3 Algorithms 34\u003c\/p\u003e \u003cp\u003e2.2.4 Literature Review 35\u003c\/p\u003e \u003cp\u003e2.3 Methodology 37\u003c\/p\u003e \u003cp\u003e2.3.1 Morphoproteomics (Fig. 2.1(1–3)) 39\u003c\/p\u003e \u003cp\u003e2.3.2 Biomedical Analytics (Fig. 2.1(4–10)) 40\u003c\/p\u003e \u003cp\u003e2.3.3 Integrating Morphoproteomics and Biomedical Analytics 44\u003c\/p\u003e \u003cp\u003e2.4 Case Studies 46\u003c\/p\u003e \u003cp\u003e2.4.1 Clinical: Therapeutic Recommendations for Pancreatic Adenocarcinoma 46\u003c\/p\u003e \u003cp\u003e2.4.2 Clinical: Biology Underlying Exceptional Responder in Refractory Hodgkin’s Lymphoma 48\u003c\/p\u003e \u003cp\u003e2.4.3 Research: Role of the Hypoxia Pathway in Both Oncogenesis and Embryogenesis 50\u003c\/p\u003e \u003cp\u003e2.5 Discussion 51\u003c\/p\u003e \u003cp\u003e2.6 Conclusions 52\u003c\/p\u003e \u003cp\u003eAcknowledgments 53\u003c\/p\u003e \u003cp\u003eReferences 53\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Characterization and Monitoring of Nonlinear Dynamics and Chaos in Complex Physiological Systems\u003c\/b\u003e \u003cb\u003e59\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eHui Yang, Yun Chen, and Fabio Leonelli\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 59\u003c\/p\u003e \u003cp\u003e3.2 Background 61\u003c\/p\u003e \u003cp\u003e3.3 Sensor-Based Characterization and Modeling of Nonlinear Dynamics 65\u003c\/p\u003e \u003cp\u003e3.3.1 Multifractal Spectrum Analysis of Nonlinear Time Series 65\u003c\/p\u003e \u003cp\u003e3.3.2 Recurrence Quantification Analysis 75\u003c\/p\u003e \u003cp\u003e3.3.3 Multiscale Recurrence Quantification Analysis 78\u003c\/p\u003e \u003cp\u003e3.4 Healthcare Applications 80\u003c\/p\u003e \u003cp\u003e3.4.1 Nonlinear Characterization of Heart Rate Variability 81\u003c\/p\u003e \u003cp\u003e3.4.2 Multiscale Recurrence Analysis of Space–Time Physiological Signals 85\u003c\/p\u003e \u003cp\u003e3.5 Summary 88\u003c\/p\u003e \u003cp\u003eAcknowledgments 90\u003c\/p\u003e \u003cp\u003eReferences 90\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Statistical Modeling of Electrocardiography Signal for Subject Monitoring and Diagnosis 95\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eLili Chen, Changyue Song, and Xi Zhang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 95\u003c\/p\u003e \u003cp\u003e4.2 Basic Elements of ECG 96\u003c\/p\u003e \u003cp\u003e4.3 Statistical Modeling of ECG for Disease Diagnosis 99\u003c\/p\u003e \u003cp\u003e4.3.1 ECG Signal Denoising 100\u003c\/p\u003e \u003cp\u003e4.3.2 Waveform Detection 105\u003c\/p\u003e \u003cp\u003e4.3.3 Feature Extraction 106\u003c\/p\u003e \u003cp\u003e4.3.4 Disease Classification and Diagnosis 111\u003c\/p\u003e \u003cp\u003e4.4 An Example: Detection of Obstructive Sleep Apnea from a Single ECG Lead 115\u003c\/p\u003e \u003cp\u003e4.4.1 Introduction to Obstructive Sleep Apnea 115\u003c\/p\u003e \u003cp\u003e4.5 Materials and Methods 115\u003c\/p\u003e \u003cp\u003e4.5.1 Database 115\u003c\/p\u003e \u003cp\u003e4.5.2 QRS Detection and RR Correction 116\u003c\/p\u003e \u003cp\u003e4.5.3 R Wave Amplitudes and EDR Signal 117\u003c\/p\u003e \u003cp\u003e4.5.4 Feature Set 117\u003c\/p\u003e \u003cp\u003e4.5.5 Classifier Training with Feature Selection 118\u003c\/p\u003e \u003cp\u003e4.6 Results 118\u003c\/p\u003e \u003cp\u003e4.6.1 QRS Detection and RR Correction 118\u003c\/p\u003e \u003cp\u003e4.6.2 Feature Selection 118\u003c\/p\u003e \u003cp\u003e4.6.3 OSA Detection 120\u003c\/p\u003e \u003cp\u003e4.7 Conclusions and Discussions 121\u003c\/p\u003e \u003cp\u003eReferences 121\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Modeling and Simulation of Measurement Uncertainty in Clinical Laboratories 127\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eVarun Ramamohan, James T. Abbott, and Yuehwern Yih\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 127\u003c\/p\u003e \u003cp\u003e5.2 Background and Literature Review 129\u003c\/p\u003e \u003cp\u003e5.2.1 Measurement Uncertainty: Background and Analytical Estimation 130\u003c\/p\u003e \u003cp\u003e5.2.2 Uncertainty in Clinical Laboratories 134\u003c\/p\u003e \u003cp\u003e5.2.3 Uncertainty in Clinical Laboratories: A System Approach 136\u003c\/p\u003e \u003cp\u003e5.3 Model Development Guidelines 138\u003c\/p\u003e \u003cp\u003e5.3.1 System Description and Process Phases 138\u003c\/p\u003e \u003cp\u003e5.3.2 Modeling Guidelines 139\u003c\/p\u003e \u003cp\u003e5.4 Implementation of Guidelines: Enzyme Assay Uncertainty Model 141\u003c\/p\u003e \u003cp\u003e5.4.1 Calibration Phase 142\u003c\/p\u003e \u003cp\u003e5.4.2 Sample Analysis Phase 149\u003c\/p\u003e \u003cp\u003e5.4.3 Results and Analysis 150\u003c\/p\u003e \u003cp\u003e5.5 Discussion and Conclusions 152\u003c\/p\u003e \u003cp\u003eReferences 154\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Predictive Analytics: Classification in Medicine and Biology 159\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eEva K. Lee\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 159\u003c\/p\u003e \u003cp\u003e6.2 Background 161\u003c\/p\u003e \u003cp\u003e6.3 Machine Learning with Discrete Support Vector Machine Predictive Models 163\u003c\/p\u003e \u003cp\u003e6.3.1 Modeling of Reserved-Judgment Region for General Groups 164\u003c\/p\u003e \u003cp\u003e6.3.2 Discriminant Analysis via Mixed-Integer Programming 165\u003c\/p\u003e \u003cp\u003e6.3.3 Model Variations 167\u003c\/p\u003e \u003cp\u003e6.3.4 Theoretical Properties and Computational Strategies 170\u003c\/p\u003e \u003cp\u003e6.4 Applying DAMIP to Real-World Applications 170\u003c\/p\u003e \u003cp\u003e6.4.1 Validation of Model and Computational Effort 171\u003c\/p\u003e \u003cp\u003e6.4.2 Applications to Biological and Medical Problems 171\u003c\/p\u003e \u003cp\u003e6.5 Summary and Conclusion 182\u003c\/p\u003e \u003cp\u003eAcknowledgments 183\u003c\/p\u003e \u003cp\u003eReferences 183\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Predictive Modeling in Radiation Oncology 189\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eHao Zhang, Robert Meyer, Leyuan Shi, Wei Lu, and Warren D’Souza\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 189\u003c\/p\u003e \u003cp\u003e7.2 Tutorials of Predictive Modeling Techniques 191\u003c\/p\u003e \u003cp\u003e7.2.1 Feature Selection 191\u003c\/p\u003e \u003cp\u003e7.2.2 Support Vector Machine 192\u003c\/p\u003e \u003cp\u003e7.2.3 Logistic Regression 193\u003c\/p\u003e \u003cp\u003e7.2.4 Decision Tree 193\u003c\/p\u003e \u003cp\u003e7.3 Review of Recent Predictive Modeling Applications in Radiation Oncology 194\u003c\/p\u003e \u003cp\u003e7.3.1 Machine Learning for Medical Image Processing 194\u003c\/p\u003e \u003cp\u003e7.3.2 Machine Learning in Real-Time Tumor Localization 196\u003c\/p\u003e \u003cp\u003e7.3.3 Machine Learning for Predicting Radiotherapy Response 197\u003c\/p\u003e \u003cp\u003e7.4 Modeling Pathologic Response of Esophageal Cancer to Chemoradiotherapy 199\u003c\/p\u003e \u003cp\u003e7.4.1 Input Features 200\u003c\/p\u003e \u003cp\u003e7.4.2 Feature Selection and Predictive Model Construction 200\u003c\/p\u003e \u003cp\u003e7.4.3 Results 202\u003c\/p\u003e \u003cp\u003e7.4.4 Discussion 204\u003c\/p\u003e \u003cp\u003e7.5 Modeling Clinical Complications after Radiation Therapy 205\u003c\/p\u003e \u003cp\u003e7.5.1 Dose-Volume Thresholds: Relationship to OAR Complications 205\u003c\/p\u003e \u003cp\u003e7.5.2 Modeling the Radiation-Induced Complications via Treatment Plan Surface 206\u003c\/p\u003e \u003cp\u003e7.5.3 Modeling Results 208\u003c\/p\u003e \u003cp\u003e7.6 Modeling Tumor Motion with Respiratory Surrogates 211\u003c\/p\u003e \u003cp\u003e7.6.1 Cyberknife System Data 211\u003c\/p\u003e \u003cp\u003e7.6.2 Modeling for the Prediction of Tumor Positions 212\u003c\/p\u003e \u003cp\u003e7.6.3 Results of Tumor Positions Modeling 212\u003c\/p\u003e \u003cp\u003e7.6.4 Discussion 214\u003c\/p\u003e \u003cp\u003e7.7 Conclusion 215\u003c\/p\u003e \u003cp\u003eReferences 215\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Mathematical Modeling of Innate Immunity Responses of Sepsis: Modeling and Computational Studies 221\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eChih-Hang J. Wu, Zhenzhen Shi, David Ben-Arieh, and Steven Q. Simpson\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Background 221\u003c\/p\u003e \u003cp\u003e8.2 System Dynamic Mathematical Model (SDMM) 223\u003c\/p\u003e \u003cp\u003e8.3 Pathogen Strain Selection 224\u003c\/p\u003e \u003cp\u003e8.3.1 Step 1: Kupffer Local Response Model 224\u003c\/p\u003e \u003cp\u003e8.3.2 Step 2: Neutrophils Immune Response Model 228\u003c\/p\u003e \u003cp\u003e8.3.3 Step 3: Damaged Tissue Model 233\u003c\/p\u003e \u003cp\u003e8.3.4 Step 4: Monocytes Immune Response Model 234\u003c\/p\u003e \u003cp\u003e8.3.5 Step 5: Anti-inflammatory Immune Response Model 237\u003c\/p\u003e \u003cp\u003e8.4 Mathematical Models of Innate Immunity of AIR 239\u003c\/p\u003e \u003cp\u003e8.4.1 Inhibition of Anti-inflammatory Cytokines 239\u003c\/p\u003e \u003cp\u003e8.4.2 Mathematical Model of Innate Immunity of AIR 239\u003c\/p\u003e \u003cp\u003e8.4.3 Stability Analysis 241\u003c\/p\u003e \u003cp\u003e8.5 Discussion 247\u003c\/p\u003e \u003cp\u003e8.5.1 Effects of Initial Pathogen Load on Sepsis Progression 247\u003c\/p\u003e \u003cp\u003e8.5.2 Effects of Pro- and Anti-inflammatory Cytokines on Sepsis Progression 250\u003c\/p\u003e \u003cp\u003e8.6 Conclusion 254\u003c\/p\u003e \u003cp\u003eReferences 254\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Analytics for Healthcare Delivery 261\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Systems Analytics: Modeling and Optimizing ClinicWorkflow and Patient Care 263\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eEva K. Lee, Hany Y. Atallah, Michael D. Wright, Calvin Thomas IV, Eleanor T. Post, Daniel T. Wu, and Leon L. Haley Jr\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 264\u003c\/p\u003e \u003cp\u003e9.2 Background 266\u003c\/p\u003e \u003cp\u003e9.3 Challenges and Objectives 267\u003c\/p\u003e \u003cp\u003e9.4 Methods and Design of Study 268\u003c\/p\u003e \u003cp\u003e9.4.1 ED Workflow and Services 269\u003c\/p\u003e \u003cp\u003e9.4.2 Data Collection and Time-Motion Studies 270\u003c\/p\u003e \u003cp\u003e9.4.3 Machine Learning for Predicting Patient Characteristics and Return Patterns 274\u003c\/p\u003e \u003cp\u003e9.4.4 The Computerized ED System Workflow Model 277\u003c\/p\u003e \u003cp\u003e9.4.5 Model Validation 282\u003c\/p\u003e \u003cp\u003e9.5 Computational Results, Implementation, and ED Performance Comparison 285\u003c\/p\u003e \u003cp\u003e9.5.1 Phase I: Results 285\u003c\/p\u003e \u003cp\u003e9.5.2 Phase I: Adoption and Implementation 288\u003c\/p\u003e \u003cp\u003e9.5.3 Phase II: Results 288\u003c\/p\u003e \u003cp\u003e9.5.4 Phase II: Adoption and Implementation 290\u003c\/p\u003e \u003cp\u003e9.6 Benefits and Impacts 292\u003c\/p\u003e \u003cp\u003e9.6.1 Quantitative Benefits 294\u003c\/p\u003e \u003cp\u003e9.6.2 Qualitative Benefits 296\u003c\/p\u003e \u003cp\u003e9.7 Scientific Advances 297\u003c\/p\u003e \u003cp\u003e9.7.1 Hospital Care Delivery Advances 297\u003c\/p\u003e \u003cp\u003e9.7.2 OR Advances 298\u003c\/p\u003e \u003cp\u003eAcknowledgments 298\u003c\/p\u003e \u003cp\u003eReferences 299\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 A Multiobjective Simulation Optimization of the Macrolevel Patient Flow Distribution 303\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eYunzhe Qiu and Jie Song\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 303\u003c\/p\u003e \u003cp\u003e10.2 Literature Review 305\u003c\/p\u003e \u003cp\u003e10.2.1 Simulation Modeling on Patient Flow 305\u003c\/p\u003e \u003cp\u003e10.2.2 Multiobjective Patient Flow Optimization Problems 306\u003c\/p\u003e \u003cp\u003e10.2.3 Simulation Optimization 307\u003c\/p\u003e \u003cp\u003e10.3 Problem Description and Modeling 308\u003c\/p\u003e \u003cp\u003e10.3.1 Problem Description 308\u003c\/p\u003e \u003cp\u003e10.3.2 System Modeling 310\u003c\/p\u003e \u003cp\u003e10.4 Methodology 312\u003c\/p\u003e \u003cp\u003e10.4.1 Simulation Model Description 312\u003c\/p\u003e \u003cp\u003e10.4.2 Optimization 313\u003c\/p\u003e \u003cp\u003e10.5 Case Study: Adjusting Patient Flow for a Two-Level Healthcare System Centered on the Puth 316\u003c\/p\u003e \u003cp\u003e10.5.1 Background and Data 316\u003c\/p\u003e \u003cp\u003e10.5.2 Simulation under Current Situation 318\u003c\/p\u003e \u003cp\u003e10.5.3 Model Validation 320\u003c\/p\u003e \u003cp\u003e10.5.4 Optimization through Algorithm 1 321\u003c\/p\u003e \u003cp\u003e10.5.5 Optimization through Algorithm 2 322\u003c\/p\u003e \u003cp\u003e10.5.6 Comparison of the Two Algorithms 327\u003c\/p\u003e \u003cp\u003e10.5.7 Managerial Insights and Recommendations 328\u003c\/p\u003e \u003cp\u003e10.6 Conclusions and the Future Work 329\u003c\/p\u003e \u003cp\u003eAcknowledgments 330\u003c\/p\u003e \u003cp\u003eReferences 331\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Analysis of Resource Intensive Activity Volumes in US Hospitals 335\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eShivon Boodhoo and Sanchoy Das\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 335\u003c\/p\u003e \u003cp\u003e11.2 Structural Classification of Hospitals 337\u003c\/p\u003e \u003cp\u003e11.3 Productivity Analysis of Hospitals 339\u003c\/p\u003e \u003cp\u003e11.4 Resource and Activity Database for US Hospitals 341\u003c\/p\u003e \u003cp\u003e11.4.1 Medicare Data Sources for Hospital Operations 343\u003c\/p\u003e \u003cp\u003e11.5 Activity-Based Modeling of Hospital Operations 344\u003c\/p\u003e \u003cp\u003e11.5.1 Direct Care Activities 344\u003c\/p\u003e \u003cp\u003e11.5.2 The Hospital Unit of Care (HUC) Model 347\u003c\/p\u003e \u003cp\u003e11.5.3 HUC Component Results by State 350\u003c\/p\u003e \u003cp\u003e11.6 Resource use Profile of Hospitals from HUC Activity Data 351\u003c\/p\u003e \u003cp\u003e11.6.1 Comparing the Resource Use Profile of States 353\u003c\/p\u003e \u003cp\u003e11.6.2 Application of the Hospital Classification Rules 355\u003c\/p\u003e \u003cp\u003e11.7 Summary 357\u003c\/p\u003e \u003cp\u003eReferences 358\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Discrete-Event Simulation for Primary Care Redesign: Review and a Case Study 361\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eXiang Zhong, Molly Williams, Jingshan Li, Sally A. Kraft, and Jeffrey S. Sleeth\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 361\u003c\/p\u003e \u003cp\u003e12.2 Review of Relevant Literature 362\u003c\/p\u003e \u003cp\u003e12.2.1 Literature on Primary Care Redesign 362\u003c\/p\u003e \u003cp\u003e12.2.2 Literature on Discrete-Event Simulation in Healthcare 366\u003c\/p\u003e \u003cp\u003e12.2.3 UW Health Improvement Projects 369\u003c\/p\u003e \u003cp\u003e12.3 A Simulation Case Study at a Pediatric Clinic 369\u003c\/p\u003e \u003cp\u003e12.3.1 Patient Flow 369\u003c\/p\u003e \u003cp\u003e12.3.2 Model Development 371\u003c\/p\u003e \u003cp\u003e12.3.3 Model Validation 376\u003c\/p\u003e \u003cp\u003e12.4 What–If Analyses 376\u003c\/p\u003e \u003cp\u003e12.4.1 Staffing Analysis 376\u003c\/p\u003e \u003cp\u003e12.4.2 Resident Doctor 377\u003c\/p\u003e \u003cp\u003e12.4.3 Schedule Template Change 377\u003c\/p\u003e \u003cp\u003e12.4.4 Volume Change 379\u003c\/p\u003e \u003cp\u003e12.4.5 Room Assignment 379\u003c\/p\u003e \u003cp\u003e12.4.6 Early Start 380\u003c\/p\u003e \u003cp\u003e12.4.7 Additional Observations 382\u003c\/p\u003e \u003cp\u003e12.5 Conclusions 382\u003c\/p\u003e \u003cp\u003eReferences 382\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Temporal and Spatiotemporal Models for Ambulance Demand 389\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eZhengyi Zhou and David S. Matteson\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 389\u003c\/p\u003e \u003cp\u003e13.2 Temporal Ambulance Demand Estimation 391\u003c\/p\u003e \u003cp\u003e13.2.1 Notation 392\u003c\/p\u003e \u003cp\u003e13.2.2 Factor Modeling with Constraints and Smoothing 393\u003c\/p\u003e \u003cp\u003e13.2.3 Adaptive Forecasting with Time Series Models 395\u003c\/p\u003e \u003cp\u003e13.3 Spatiotemporal Ambulance Demand Estimation 398\u003c\/p\u003e \u003cp\u003e13.3.1 Spatiotemporal Finite Mixture Modeling 400\u003c\/p\u003e \u003cp\u003e13.3.2 Estimating Ambulance Demand 403\u003c\/p\u003e \u003cp\u003e13.3.3 Model Performance 405\u003c\/p\u003e \u003cp\u003e13.4 Conclusions 409\u003c\/p\u003e \u003cp\u003eReferences 410\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Mathematical Optimization and Simulation Analyses for Optimal Liver Allocation Boundaries 413\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eNaoru Koizumi, Monica Gentili, Rajesh Ganesan, Debasree DasGupta, Amit Patel, Chun-Hung Chen, Nigel Waters, and Keith Melancon\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 414\u003c\/p\u003e \u003cp\u003e14.2 Methods 416\u003c\/p\u003e \u003cp\u003e14.2.1 Mathematical Model: Optimal Locations of Transplant Centers and OPO Boundaries 416\u003c\/p\u003e \u003cp\u003e14.2.2 Discrete-Event Simulation: Evaluation of Optimal OPO Boundaries 422\u003c\/p\u003e \u003cp\u003e14.3 Results 423\u003c\/p\u003e \u003cp\u003e14.3.1 New Locations of Transplant Centers 423\u003c\/p\u003e \u003cp\u003e14.3.2 New OPO Boundaries 426\u003c\/p\u003e \u003cp\u003e14.3.3 Evaluation of New OPO Boundaries 428\u003c\/p\u003e \u003cp\u003e14.4 Conclusions 433\u003c\/p\u003e \u003cp\u003eAcknowledgment 435\u003c\/p\u003e \u003cp\u003eReferences 435\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Predictive Analytics in 30-Day Hospital Readmissions for Heart Failure Patients 439\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSi-Chi Chin, Rui Liu, and Senjuti B. Roy\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 440\u003c\/p\u003e \u003cp\u003e15.2 Analytics in Prediction Hospital Readmission Risk 441\u003c\/p\u003e \u003cp\u003e15.2.1 The Overall Prediction Pipeline 441\u003c\/p\u003e \u003cp\u003e15.2.2 Data Preprocessing 441\u003c\/p\u003e \u003cp\u003e15.2.3 Predictive Models 442\u003c\/p\u003e \u003cp\u003e15.2.4 Experiment and Evaluation 444\u003c\/p\u003e \u003cp\u003e15.3 Analytics in Recommending Intervention Strategies 447\u003c\/p\u003e \u003cp\u003e15.3.1 The Overall Intervention Pipeline 447\u003c\/p\u003e \u003cp\u003e15.3.2 Bayesian Network Construction 448\u003c\/p\u003e \u003cp\u003e15.3.3 Recommendation Rule Generation 452\u003c\/p\u003e \u003cp\u003e15.3.4 Intervention Recommendation 453\u003c\/p\u003e \u003cp\u003e15.3.5 Experiments 454\u003c\/p\u003e \u003cp\u003e15.4 Related Work 457\u003c\/p\u003e \u003cp\u003e15.5 Conclusion 459\u003c\/p\u003e \u003cp\u003eReferences 459\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Heterogeneous Sensing and Predictive Modeling of Postoperative Outcomes 463\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eYun Chen, Fabio Leonelli, and Hui Yang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 463\u003c\/p\u003e \u003cp\u003e16.2 Research Background 466\u003c\/p\u003e \u003cp\u003e16.2.1 Acute Physiology and Chronic Health Evaluation (APACHE) 466\u003c\/p\u003e \u003cp\u003e16.2.2 Simplified Acute Physiology Score (SAPS) 469\u003c\/p\u003e \u003cp\u003e16.2.3 Mortality Probability Model (MPM) 470\u003c\/p\u003e \u003cp\u003e16.2.4 Sequential Organ Failure Assessment (SOFA) 472\u003c\/p\u003e \u003cp\u003e16.3 Research Methodology 474\u003c\/p\u003e \u003cp\u003e16.3.1 Data Categorization 475\u003c\/p\u003e \u003cp\u003e16.3.2 Data Preprocessing and Missing Data Imputation 475\u003c\/p\u003e \u003cp\u003e16.3.3 Feature Extraction 482\u003c\/p\u003e \u003cp\u003e16.3.4 Feature Selection 484\u003c\/p\u003e \u003cp\u003e16.3.5 Predictive Model 487\u003c\/p\u003e \u003cp\u003e16.3.6 Cross-Validation and Ensemble Voting Processes 489\u003c\/p\u003e \u003cp\u003e16.4 Materials and Experimental Design 491\u003c\/p\u003e \u003cp\u003e16.5 Experimental Results 491\u003c\/p\u003e \u003cp\u003e16.6 Discussion and Conclusions 498\u003c\/p\u003e \u003cp\u003eAcknowledgments 499\u003c\/p\u003e \u003cp\u003eReferences 499\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 Analyzing Patient–Physician Interaction in Consultation for Shared Decision Making 503\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eThembi Mdluli, Joyatee Sarker, Carolina Vivas-Valencia, Nan Kong, and Cleveland G. Shields\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction 503\u003c\/p\u003e \u003cp\u003e17.2 Literature Review 505\u003c\/p\u003e \u003cp\u003e17.2.1 Patient–Physician Interaction on Prognosis Discussion 506\u003c\/p\u003e \u003cp\u003e17.2.2 Physician–Patient Interaction on Pain Assessment 509\u003c\/p\u003e \u003cp\u003e17.3 Our Recent Data Mining Studies 510\u003c\/p\u003e \u003cp\u003e17.3.1 Predicting Patient Satisfaction with Survey Data 510\u003c\/p\u003e \u003cp\u003e17.3.2 Predicting Patient Satisfaction with Conservation Data 513\u003c\/p\u003e \u003cp\u003e17.4 Future Directions 515\u003c\/p\u003e \u003cp\u003e17.4.1 Regression Shrinkage and Selection 515\u003c\/p\u003e \u003cp\u003e17.4.2 Conversational Characterization 517\u003c\/p\u003e \u003cp\u003e17.5 Concluding Remarks 519\u003c\/p\u003e \u003cp\u003eReferences 520\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 The History and Modern Applications of Insurance Claims Data in Healthcare Research 523\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMargrét V. Bjarndóttir, David Czerwinski, and Yihan Guan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction 523\u003c\/p\u003e \u003cp\u003e18.1.1 Advantages and Limitations of Claims Data 525\u003c\/p\u003e \u003cp\u003e18.1.2 Application Areas 526\u003c\/p\u003e \u003cp\u003e18.1.3 Statistical Methodologies Used in Claims-Based Studies 528\u003c\/p\u003e \u003cp\u003e18.2 Healthcare Cost Predictions 531\u003c\/p\u003e \u003cp\u003e18.2.1 Modeling of Healthcare Costs 531\u003c\/p\u003e \u003cp\u003e18.2.2 Modeling of Disease Burden and Interactions 533\u003c\/p\u003e \u003cp\u003e18.2.3 Performance Measures and Baselines 534\u003c\/p\u003e \u003cp\u003e18.2.4 Prediction Algorithms 534\u003c\/p\u003e \u003cp\u003e18.2.5 Applying Regression Trees to Cost Predictions 535\u003c\/p\u003e \u003cp\u003e18.2.6 Applying Clustering Algorithms to Cost Predictions 537\u003c\/p\u003e \u003cp\u003e18.2.7 Identifying High-Cost Members 539\u003c\/p\u003e \u003cp\u003e18.2.8 Discussion 539\u003c\/p\u003e \u003cp\u003e18.3 Measuring Quality of Care 540\u003c\/p\u003e \u003cp\u003e18.3.1 Structure, Process, and Outcomes 540\u003c\/p\u003e \u003cp\u003e18.3.2 The Quality of Quality Data 542\u003c\/p\u003e \u003cp\u003e18.3.3 Composite Quality Measures 542\u003c\/p\u003e \u003cp\u003e18.3.4 Practical Considerations for Constructing Quality Scores 544\u003c\/p\u003e \u003cp\u003e18.3.5 A Statistical Approach to Measuring Quality 545\u003c\/p\u003e \u003cp\u003e18.3.6 Quality as a Case Management Tool 546\u003c\/p\u003e \u003cp\u003e18.3.7 Discussion 547\u003c\/p\u003e \u003cp\u003e18.4 Conclusions 548\u003c\/p\u003e \u003cp\u003eReferences 548\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Understanding the Role of Social Media in Healthcare via Analytics: a Health Plan Perspective 555\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSinjini Mitra and Rema Padman\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction 555\u003c\/p\u003e \u003cp\u003e19.2 Literature Review 556\u003c\/p\u003e \u003cp\u003e19.2.1 Privacy and Security Concerns in Social Media and Healthcare 559\u003c\/p\u003e \u003cp\u003e19.2.2 Analytics in Healthcare and Social Media 561\u003c\/p\u003e \u003cp\u003e19.3 Case Study Description 562\u003c\/p\u003e \u003cp\u003e19.3.1 Survey Design 563\u003c\/p\u003e \u003cp\u003e19.4 Research Methods and Analytics Tools 564\u003c\/p\u003e \u003cp\u003e19.4.1 The Logistic Regression Model 564\u003c\/p\u003e \u003cp\u003e19.5 Results and Discussions 568\u003c\/p\u003e \u003cp\u003e19.5.1 Descriptive Statistics 568\u003c\/p\u003e \u003cp\u003e19.5.2 Baseline of Technology Usage 570\u003c\/p\u003e \u003cp\u003e19.5.3 Mobile and Social Media Usage 571\u003c\/p\u003e \u003cp\u003e19.5.4 Clustering of Member Population by Technology, Social, and Mobile Media Usage 572\u003c\/p\u003e \u003cp\u003e19.5.5 Interest in Adopting Online Tools for Healthcare Purposes 573\u003c\/p\u003e \u003cp\u003e19.5.6 Interest in Adopting Mobile Apps for Healthcare Purposes 574\u003c\/p\u003e \u003cp\u003e19.5.7 Health and Wellness Objectives 577\u003c\/p\u003e \u003cp\u003e19.5.8 Privacy and Security Concerns 580\u003c\/p\u003e \u003cp\u003e19.5.9 Predictive Models 581\u003c\/p\u003e \u003cp\u003e19.6 Conclusions 584\u003c\/p\u003e \u003cp\u003eReferences 585\u003c\/p\u003e \u003cp\u003eIndex 589 \u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":49406946869591,"sku":"9781118919392","price":97.16,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781118919392.jpg?v=1730497651","url":"https:\/\/bookcurl.com\/products\/healthcare-analytics-9781118919392","provider":"Book Curl","version":"1.0","type":"link"}