Objective
Observational studies examining outcomes among opioid-exposed infants are limited by phenotype algorithms that may under identify opioid-exposed infants without neonatal opioid withdrawal syndrome (NOWS). We developed and validated the performance of different phenotype algorithms to identify opioid-exposed infants using electronic health record (EHR) data.
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.
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.
Peripheral Arterial Disease (PAD) is prevalent with approximately 10-12 million adults in US affected. For those with PAD, morbidity and mortality are high and quality of life is markedly impaired. The genetic basis of PAD is poorly understood and is the focus of the Mayo Clinic Electronic Medical Records and Genomics (eMERGE) Network study.
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:
https://phekb.org/phenotype/benign-prostatic-hyperplasia-bph
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:
Identify bacterial pneumonia, similar to that reported with genetic association risk in CD143 and TLR4 A229G in literature.
This rheumatoid arthritis (RA) algorithm was created using a machine-learning logistic regression model.
Rheumatoid Arthritis phenotype algorithm for the DNA Databank demonstration project.
|
|
Record Count |
Diagnostic Codes? |
Text Keywords? |
Narrative Notes to search |
Other rules |
|
Cases |