Vanderbilt University Medical Center

Developmental Language Disorder

APT-DLD
Version 1.0, July 2020

Automated Phenotyping Tool for identifying DLD cases in health-systems data (APT-DLD) is an algorithm for classifying/identifying developmental language disorder cases in electronic health records system data. APT-DLD can be used to:
1. Identify pediatric DLD cases from electronic health record systems using ICD9 and ICD10 codes
2. Study epidemiology and population-level charateristics of DLD from EHRs

The How-To guide for using APT-DLD is provided in the files listed below.

Owner Phenotyping Groups: 
Vanderbilt - Music Cognition Lab
Final

Diverticular Disease Severity, Colonic

This algorithm builds off prior phenotyping work from Pacheco & Thompson available in the PheKB phenotype "Diverticulosis and Diverticulitis" as well as the manuscripts from Joo et al (2023)(1) and De Roo et al (2023) (2) . The objective is to approximate diverticular disease severity from the electronic medical record into groups of asymptomatic diverticulosis, mild diverticulitis, and severe diverticulitis.

Final

Peanut Allergy

Food allergy is defined as an immune response that occurs reproducibly to a given food, typically an immunoglobulin E (IgE)-mediated clinical reaction to specific protein epitopes.  Over the last 20-30 years, food allergy has grown into a major public health problem.  Peanut allergy is a common type of food allergy that accounts for a disproportionate number of fatal and near-fatal anaphylactic events amongst all the common food allergens.

Owner Phenotyping Groups: 
eMERGE Vanderbilt Group
Vanderbilt - SD/RD Group
VESPA - Vanderbilt Electronic Systems for Pharmacogenomic Assessment
View Phenotyping Groups: 
eMERGE Vanderbilt Group
NIH Collaboratory Group
Vanderbilt - SD/RD Group
VESPA - Vanderbilt Electronic Systems for Pharmacogenomic Assessment
Final

Systemic lupus erythematosus (SLE)

We used Vanderbilt’s Synthetic Derivative (SD), a de-identified version of the EHR, with 2.5 million subjects. We selected all individuals with at least one SLE ICD-9 code (710.0) yielding 5959 individuals. To create a training set, 200 were randomly selected for chart review. A subject was defined as a case if diagnosed with SLE by a rheumatologist, nephrologist, or dermatologist.

Final