Secondary Analysis of Electronic Health Records
This book trains the next generation of scientists representing different disciplines to leverage the data generated during routine patient care. It formulates a more complete lexicon of evidence-based recommendations and support shared, ethical decision making by doctors with their patients. Diagno...
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| Format: | Electronic eBook |
| Language: | English |
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Cham :
Springer International Publishing : Imprint: Springer,
2016.
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| Online Access: | http://dx.doi.org/10.1007/978-3-319-43742-2 |
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Table of Contents:
- Introduction to the Book
- Objectives of secondary analysis of EHR data
- Review of clinical database
- Challenges and opportunities
- Secondary Analysis of EHR Data Cookbook
- Overview
- Step 1: Formulate research question
- Step 2: Data extraction and preprocessing
- Step 3: Exploratory Analysis
- Step 4: Data analysis
- Step 5: Validation and sensitivity analysis
- Missing Data
- Noise vs. Outliers
- Case Studies
- Introduction
- Predictive Modeling: outcome prediction (discrete)
- Predictive Modeling: dose optimization (regression)
- Pharmacovigilance (classification)
- Comparative effectiveness: propensity score analysis
- Comparative effectiveness: instrumental variable analysis
- Decision and Cost Effectiveness Analysis: Hidden Markov models and Monte Carlo simulation
- Time series analysis: Gaussian processes (ICP modelling)
- Time series analysis: Bayesian inference (Motif discovery in numerical signals)
- Time Series analysis: Optimization techniques for hyperparameter selection
- Signal processing: analysis of waveform data
- Signal processing: False alarm reduction.