Black (African American, West African, Ethiopian, etc.)

Post-event Pain algorithm

Pain is a personal, multidimensional experience in which genetic biomarkers has a main role in determining pain sensitivity, perception and tolerance. Pain is a major concern for surgical patients and post-operative pain management still present a major challenge both in inpatient or outpatient settings. Apart from genetic factors, there are many other variables that may affect pain perception for example, pretreated patients may require less post-surgical medications, and they may recover more quickly.

Owner Phenotyping Groups: 
eMERGE CCHMC Group
View Phenotyping Groups: 
eMERGE Phenotype WG
Final

SLE (Systemic Lupus Erythematosus) using SLICC (Systemic Lupus Internation Collaborating Clinics) Criteria

Systemic Lupus Erythematosus (SLE) is a chronic, systemic autoimmune disease that can affect many parts of the body including skin, lungs, brain, heart, kidneys, joints, and blood vessels. SLE presentation can vary significantly between patients. Because of this, it can be challenging to identify a patient as having SLE. Between 300,000 and 2,000,000 people in the US are estimated to have SLE. Determination of an exact number of people affected is challenging as the disease is difficult to identify given the diverse presentations and the length of time it may take for symptoms to appear.

Owner Phenotyping Groups: 
eMERGE Northwestern Group
View Phenotyping Groups: 
eMERGE Mayo Group
eMERGE Phenotype WG
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

Type1 or Type 2 Diabetes Mellitus

Phenotyping algorithm for the identification of patients with type 1 or type 2 diabetes mellitus (DM) preoperatively using routinely available clinical data from electronic
health records.

View Phenotyping Groups: 
American-Swiss Allergy Phenotyping Group
BioVU Phenotyping
Clostridium Difficile Colitis Phenotype Group
Denny’s Group at Vandy
eMERGE CCHMC Group
eMERGE CCHMC/BCH Group
eMERGE CHOP Group
eMERGE Columbia Group
eMERGE Geisinger Group
eMERGE GHC/University of Washington Group
eMERGE Harvard Group
eMERGE Marshfield Group
eMERGE Mayo Group
eMERGE Meharry Group
eMERGE Mount Sinai Group
eMERGE Northwestern Group
eMERGE Phenotype WG
eMERGE Vanderbilt Group
Farr Institute Kidney Working Group
GPC
NIH Collaboratory Group
PCORI CDRN/PPRN
PCORI MidSouth CDRN
PCORI MidSouth CDRN/Duke Group
PCORI MidSouth CDRN/Greenway Group
PCORI MidSouth CDRN/HSSC Group
PCORI MidSouth CDRN/Meharry Group
PCORI MidSouth CDRN/UNC Group
PCORI MidSouth CDRN/VUMC Group
PGPop WG
PGx model group
PGx Workgroup
PheMA Test Group
Precision Medicine and Health Disparities Collaborative
Resistant hypertension WG
Vanderbilt - SD/RD Group
VESPA - Vanderbilt Electronic Systems for Pharmacogenomic Assessment
Final

Urinary Incontinence

Description of a weakly supervised machine learning approach for extracting treatment-related side effects (Urinary Incontinence) following prostate cancer therapy from multiple types of free-text clinical narratives, including progress notes, discharge summaries, history and physical notes. Prostatectomy surgery and radiation therapy are our treatments of interest for prostate cancer.

Final

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