Secondary Analysis of Electronic Health Records [electronic resource] by MIT Critical Data.

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...

Full description

Bibliographic Details
Main Author: MIT Critical Data (Author)
Corporate Author: SpringerLink (Online service)
Language:English
Published: Cham : Springer International Publishing : Imprint: Springer, 2016.
Edition:1st ed. 2016.
Subjects:
Medical informatics.
Ethics.
Data mining.
Biometry.
Online Access:
Springer Medicine eBooks 2016 English/International: 2016 (Springer Link)
SpringerLink eBooks Fully Open Access: 2016 (Springer Link)
Open Research Library (ORL): 2016 (Open Research Library)
OAPEN (Open Access Publishing in European Networks): 2016 (Open Access Publishing in European Networks)
DOAB Directory of Open Access Books: 2016 (Directory of Open Access Books)
Format: Electronic eBook
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.