Secondary analysis of electronic health records / MIT Critical Data.

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.