Breast cancer is the most common cancer and the second leading cause of cancer-related death among women in the U.S. Known breast cancer risk factors include age, race/ethnicity, reproductive factors, and benign breast disease. Family history of breast cancer and hereditary cancer syndromes, such as BRCA1/BRCA2 mutations, confer the strongest risk for this disease.
eMERGE Marshfield Group
We used a multi-modal strategy consisting of structured database querying, natural language processing on free-text documents, and optical character recognition on scanned clinical images to identify cataract subjects and related cataract attributes.
Chronic kidney disease (CKD) is defined as an abnormality of kidney structure or function present for longer than 3 months. CKD can occur as a result of heterogeneous disorders affecting the kidney. In the United States, an estimated 13.6% of adults have CKD. Notably, adjusted mortality rates are higher for patients with CKD than those without, and rates increase with CKD stage. The purpose of this algorithm is to enable accurate CKD diagnosis and staging based on EHR data.
A pheontype defining patients with strong evidence of having been diagnosed with colorectal cancer (cases) and patients who clearly do not have such diagnoses (controls). This phenotype is being used for sequencing studies. The only NLP involved in this phenotype is a very simple string search applied to pathology reports.
Algorithm to identify patients with diabetic retinopathy.
Phenotype Description: individuals with sensorineural hearing loss (SNHL)
Below are algorithms used to identify individuals with SNHL at BioVU. If you have questions regarding any of the information presented on this page, you may contact either:
Wei-Qi Wei at email@example.com or Joshua Denny at firstname.lastname@example.org
Algorithm to identify patients with low levels of high-density lipoproteins (HDL).
FLOWCHART of HDL Phenotyping Process
Non-alcoholic fatty liver disease (NAFLD)
The KPWA/UW-led ovarian/uterine cancer phenotype has been validated at Mayo Clinic, the secondary phenotype development site. Validation results at both the primary and secondary sites were strong and the phenotype is ready for network wide implementation. The pseudo code document posted 11/30/2017 is correct as is and should be used by network sites for phenotype implementation. A validated data dictionary of covariates for this phenotype will be added to PheKB by 2/15/2018, but sites are encouraged to begin implementing the phenotype algorithm now.
This is PhEMA (Phenotype Execution Modeling Architecture, projectphema.org)'s implementation of the following BPH (Benign Prostatic Hyperplasia) case algorithm from the following BPH case and control algorithm on PheKB:
Artifacts for this phenotype, inc. an HQMF representation, a KNIME workflow that can run against an i2b2 instance, and a snapshot of the PhAT graphical representation, are posted on GitHub: