Secondary Analysis of Electronic Health Records.

Secondary Analysis of Electronic Health Records.

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Bibliographic Details
:
MIT Critical Data, M. I. T. Critical.
Place / Publishing House:Cham : : Springer International Publishing AG,, 2016.
©2016.
Year of Publication:2016
Edition:1st ed.
Language:English
Online Access:
Physical Description:1 online resource (435 pages)
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Table of Contents:
  • Intro
  • Preface
  • MIT Critical Data
  • Contents
  • Setting the Stage: Rationale Behind and Challenges to Health Data Analysis
  • Introduction
  • 1 Objectives of the Secondary Analysis of Electronic Health Record Data
  • 1.1 Introduction
  • 1.2 Current Research Climate
  • 1.3 Power of the Electronic Health Record
  • 1.4 Pitfalls and Challenges
  • 1.5 Conclusion
  • References
  • 2 Review of Clinical Databases
  • 2.1 Introduction
  • 2.2 Background
  • 2.3 The Medical Information Mart for Intensive Care (MIMIC) Database
  • 2.3.1 Included Variables
  • 2.3.2 Access and Interface
  • 2.4 PCORnet
  • 2.4.1 Included Variables
  • 2.4.2 Access and Interface
  • 2.5 Open NHS
  • 2.5.1 Included Variables
  • 2.5.2 Access and Interface
  • 2.6 Other Ongoing Research
  • 2.6.1 eICU-Philips
  • 2.6.2 VistA
  • 2.6.3 NSQUIP
  • References
  • 3 Challenges and Opportunities in Secondary Analyses of Electronic Health Record Data
  • 3.1 Introduction
  • 3.2 Challenges in Secondary Analysis of Electronic Health Records Data
  • 3.3 Opportunities in Secondary Analysis of Electronic Health Records Data
  • 3.4 Secondary EHR Analyses as Alternatives to Randomized Controlled Clinical Trials
  • 3.5 Demonstrating the Power of Secondary EHR Analysis: Examples in Pharmacovigilance and Clinical Care
  • 3.6 A New Paradigm for Supporting Evidence-Based Practice and Ethical Considerations
  • References
  • 4 Pulling It All Together: Envisioning a Data-Driven, Ideal Care System
  • 4.1 Use Case Examples Based on Unavoidable Medical Heterogeneity
  • 4.2 Clinical Workflow, Documentation, and Decisions
  • 4.3 Levels of Precision and Personalization
  • 4.4 Coordination, Communication, and Guidance Through the Clinical Labyrinth
  • 4.5 Safety and Quality in an ICS
  • 4.6 Conclusion
  • References
  • 5 The Story of MIMIC
  • 5.1 The Vision
  • 5.2 Data Acquisition
  • 5.2.1 Clinical Data.
  • 5.2.2 Physiological Data
  • 5.2.3 Death Data
  • 5.3 Data Merger and Organization
  • 5.4 Data Sharing
  • 5.5 Updating
  • 5.6 Support
  • 5.7 Lessons Learned
  • 5.8 Future Directions
  • Acknowledgments
  • References
  • 6 Integrating Non-clinical Data with EHRs
  • 6.1 Introduction
  • 6.2 Non-clinical Factors and Determinants of Health
  • 6.3 Increasing Data Availability
  • 6.4 Integration, Application and Calibration
  • 6.5 A Well-Connected Empowerment
  • 6.6 Conclusion
  • References
  • 7 Using EHR to Conduct Outcome and Health Services Research
  • 7.1 Introduction
  • 7.2 The Rise of EHRs in Health Services Research
  • 7.2.1 The EHR in Outcomes and Observational Studies
  • 7.2.2 The EHR as Tool to Facilitate Patient Enrollment in Prospective Trials
  • 7.2.3 The EHR as Tool to Study and Improve Patient Outcomes
  • 7.3 How to Avoid Common Pitfalls When Using EHR to Do Health Services Research
  • 7.3.1 Step 1: Recognize the Fallibility of the EHR
  • 7.3.2 Step 2: Understand Confounding, Bias, and Missing Data When Using the EHR for Research
  • 7.4 Future Directions for the EHR and Health Services Research
  • 7.4.1 Ensuring Adequate Patient Privacy Protection
  • 7.5 Multidimensional Collaborations
  • 7.6 Conclusion
  • References
  • 8 Residual Confounding Lurking in Big Data: A Source of Error
  • 8.1 Introduction
  • 8.2 Confounding Variables in Big Data
  • 8.2.1 The Obesity Paradox
  • 8.2.2 Selection Bias
  • 8.2.3 Uncertain Pathophysiology
  • 8.3 Conclusion
  • References
  • A Cookbook: From Research Question Formulation to Validation of Findings
  • 9 Formulating the Research Question
  • 9.1 Introduction
  • 9.2 The Clinical Scenario: Impact of Indwelling Arterial Catheters
  • 9.3 Turning Clinical Questions into Research Questions
  • 9.3.1 Study Sample
  • 9.3.2 Exposure
  • 9.3.3 Outcome
  • 9.4 Matching Study Design to the Research Question.
  • 9.5 Types of Observational Research
  • 9.6 Choosing the Right Database
  • 9.7 Putting It Together
  • References
  • 10 Defining the Patient Cohort
  • 10.1 Introduction
  • 10.2 PART 1-Theoretical Concepts
  • 10.2.1 Exposure and Outcome of Interest
  • 10.2.2 Comparison Group
  • 10.2.3 Building the Study Cohort
  • 10.2.4 Hidden Exposures
  • 10.2.5 Data Visualization
  • 10.2.6 Study Cohort Fidelity
  • 10.3 PART 2-Case Study: Cohort Selection
  • References
  • 11 Data Preparation
  • 11.1 Introduction
  • 11.2 Part 1-Theoretical Concepts
  • 11.2.1 Categories of Hospital Data
  • 11.2.2 Context and Collaboration
  • 11.2.3 Quantitative and Qualitative Data
  • 11.2.4 Data Files and Databases
  • 11.2.5 Reproducibility
  • 11.3 Part 2-Practical Examples of Data Preparation
  • 11.3.1 MIMIC Tables
  • 11.3.2 SQL Basics
  • 11.3.3 Joins
  • 11.3.4 Ranking Across Rows Using a Window Function
  • 11.3.5 Making Queries More Manageable Using WITH
  • References
  • 12 Data Pre-processing
  • 12.1 Introduction
  • 12.2 Part 1-Theoretical Concepts
  • 12.2.1 Data Cleaning
  • 12.2.2 Data Integration
  • 12.2.3 Data Transformation
  • 12.2.4 Data Reduction
  • 12.3 PART 2-Examples of Data Pre-processing in R
  • 12.3.1 R-The Basics
  • 12.3.2 Data Integration
  • 12.3.3 Data Transformation
  • 12.3.4 Data Reduction
  • 12.4 Conclusion
  • References
  • 13 Missing Data
  • 13.1 Introduction
  • 13.2 Part 1-Theoretical Concepts
  • 13.2.1 Types of Missingness
  • 13.2.2 Proportion of Missing Data
  • 13.2.3 Dealing with Missing Data
  • Available-Case Analysis
  • Weighting-Case Analysis
  • Mean and Median
  • Linear Interpolation
  • Hot Deck and Cold Deck
  • Last Observation Carried Forward
  • Linear Regression
  • Stochastic Regression
  • Multiple-Value Imputation
  • K-Nearest Neighbors
  • 13.2.4 Choice of the Best Imputation Method
  • 13.3 Part 2-Case Study.
  • 13.3.1 Proportion of Missing Data and Possible Reasons for Missingness
  • 13.3.2 Univariate Missingness Analysis
  • Linear Regression Imputation
  • Stochastic Linear Regression Imputation
  • 13.3.3 Evaluating the Performance of Imputation Methods on Mortality Prediction
  • 13.4 Conclusion
  • References
  • 14 Noise Versus Outliers
  • 14.1 Introduction
  • 14.2 Part 1-Theoretical Concepts
  • 14.3 Statistical Methods
  • 14.3.1 Tukey's Method
  • 14.3.2 Z-Score
  • 14.3.3 Modified Z-Score
  • 14.3.4 Interquartile Range with Log-Normal Distribution
  • 14.3.5 Ordinary and Studentized Residuals
  • 14.3.6 Cook's Distance
  • 14.3.7 Mahalanobis Distance
  • 14.4 Proximity Based Models
  • 14.4.1 k-Means
  • 14.4.2 k-Medoids
  • 14.4.3 Criteria for Outlier Detection
  • 14.5 Supervised Outlier Detection
  • 14.6 Outlier Analysis Using Expert Knowledge
  • 14.7 Case Study: Identification of Outliers in the Indwelling Arterial Catheter (IAC) Study
  • 14.8 Expert Knowledge Analysis
  • 14.9 Univariate Analysis
  • 14.10 Multivariable Analysis
  • 14.11 Classification of Mortality in IAC and Non-IAC Patients
  • 14.12 Conclusions and Summary
  • Code Appendix
  • References
  • 15 Exploratory Data Analysis
  • 15.1 Introduction
  • 15.2 Part 1-Theoretical Concepts
  • 15.2.1 Suggested EDA Techniques
  • 15.2.2 Non-graphical EDA
  • 15.2.3 Graphical EDA
  • 15.3 Part 2-Case Study
  • 15.3.1 Non-graphical EDA
  • 15.3.2 Graphical EDA
  • 15.4 Conclusion
  • Code Appendix
  • References
  • 16 Data Analysis
  • 16.1 Introduction to Data Analysis
  • 16.1.1 Introduction
  • 16.1.2 Identifying Data Types and Study Objectives
  • 16.1.3 Case Study Data
  • 16.2 Linear Regression
  • 16.2.1 Section Goals
  • 16.2.2 Introduction
  • 16.2.3 Model Selection
  • 16.2.4 Reporting and Interpreting Linear Regression
  • 16.2.5 Caveats and Conclusions
  • 16.3 Logistic Regression
  • 16.3.1 Section Goals.
  • 16.3.2 Introduction
  • 16.3.3 2 × 2 Tables
  • 16.3.4 Introducing Logistic Regression
  • 16.3.5 Hypothesis Testing and Model Selection
  • 16.3.6 Confidence Intervals
  • 16.3.7 Prediction
  • 16.3.8 Presenting and Interpreting Logistic Regression Analysis
  • 16.3.9 Caveats and Conclusions
  • 16.4 Survival Analysis
  • 16.4.1 Section Goals
  • 16.4.2 Introduction
  • 16.4.3 Kaplan-Meier Survival Curves
  • 16.4.4 Cox Proportional Hazards Models
  • 16.4.5 Caveats and Conclusions
  • 16.5 Case Study and Summary
  • 16.5.1 Section Goals
  • 16.5.2 Introduction
  • 16.5.3 Logistic Regression Analysis
  • 16.5.4 Conclusion and Summary
  • References
  • 17 Sensitivity Analysis and Model Validation
  • 17.1 Introduction
  • 17.2 Part 1-Theoretical Concepts
  • 17.2.1 Bias and Variance
  • 17.2.2 Common Evaluation Tools
  • 17.2.3 Sensitivity Analysis
  • 17.2.4 Validation
  • 17.3 Case Study: Examples of Validation and Sensitivity Analysis
  • 17.3.1 Analysis 1: Varying the Inclusion Criteria of Time to Mechanical Ventilation
  • 17.3.2 Analysis 2: Changing the Caliper Level for Propensity Matching
  • 17.3.3 Analysis 3: Hosmer-Lemeshow Test
  • 17.3.4 Implications for a 'Failing' Model
  • 17.4 Conclusion
  • Code Appendix
  • References
  • Case Studies Using MIMIC
  • Introduction
  • 18 Trend Analysis: Evolution of Tidal Volume Over Time for Patients Receiving Invasive Mechanical Ventilation
  • 18.1 Introduction
  • 18.2 Study Dataset
  • 18.3 Study Pre-processing
  • 18.4 Study Methods
  • 18.5 Study Analysis
  • 18.6 Study Conclusions
  • 18.7 Next Steps
  • 18.8 Connections
  • Code Appendix
  • References
  • 19 Instrumental Variable Analysis of Electronic Health Records
  • 19.1 Introduction
  • 19.2 Methods
  • 19.2.1 Dataset
  • 19.2.2 Methodology
  • 19.2.3 Pre-processing
  • 19.3 Results
  • 19.4 Next Steps
  • 19.5 Conclusions
  • Code Appendix
  • References.
  • 20 Mortality Prediction in the ICU Based on MIMIC-II Results from the Super ICU Learner Algorithm (SICULA) Project.

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