Table of Contents: Secondary Analysis of Electronic Health Records.

Secondary Analysis of Electronic Health Records.

Bibliographic Details
Main Author:	MIT Critical Data, M. I. T. Critical.
Format:	eBook
Language:	English
Published:	Cham : Springer International Publishing AG, 2016.
Edition:	1st ed.
Subjects:	Electronic books.
Online Access:	Click to View

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.

Secondary Analysis of Electronic Health Records.

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