Related Experiment Video
Updated: Feb 16, 2026

A Zebrafish Model of Diabetes Mellitus and Metabolic Memory
Published on: February 28, 2013
Development and Validation of Various Phenotyping Algorithms for Diabetes Mellitus Using Data from Electronic Health
Santiago Esteban1, Manuel Rodríguez Tablado1, Francisco Peper1
1Family and Community Medicine Division, Hospital Italiano, Buenos Aires, Argentina.
Abstract:
Precision medicine requires extremely large samples. Electronic health records (EHR) are thought to be a cost-effective source of data for that purpose. Phenotyping algorithms help reduce classification errors, making EHR a more reliable source of information for research. Four algorithm development strategies for classifying patients according to their diabetes status (diabetics; non-diabetics; inconclusive) were tested (one codes-only algorithm; one boolean algorithm, four statistical learning algorithms and six stacked generalization meta-learners). The best performing algorithms within each strategy were tested on the validation set. The stacked generalization algorithm yielded the highest Kappa coefficient value in the validation set (0.95 95% CI 0.91, 0.98). The implementation of these algorithms allows for the exploitation of data from thousands of patients accurately, greatly reducing the costs of constructing retrospective cohorts for research.
More Related Videos
Related Concept Videos
Data Reporting and Recording
Diabetes Mellitus: Type 2 and Gestational
Diabetes Mellitus: Overview and Type I Subtype
Type 1 diabetes is an autoimmune disease in which the immune system mistakenly attacks and destroys the insulin-producing beta cells in the pancreas. As a result, the body is unable to produce sufficient insulin, and individuals with...
Data Validation
Key parameters for method validation include:
Data Validation
Nursing assessment guides are generally based on holistic models rather than medical...
Purpose of Health Records I
Here's a breakdown of how health records serve these purposes:

