The algorithm uses Structured Query Language to identify AAA cases, controls, and excludes from the Electronic Medical Record. AAA cases were defined as meeting at least one of three criteria: had a AAA repair procedure (Case Type 1), had at least one vascular clinic encounter with a diagnosis of ruptured AAA (Case Type 2), or had at least two vascular clinic encounters with a diagnosis of unruptured AAA (Case Type 3).
Algorithm to select subjects with "normal" electrocardiograms. Subjects do not have heart disease, interfering medications, or abnormal electrolytes at the time of the normal ECG. Individuals may, however, develop abnormalities later in life.
Hypothetical timeline for a single patient:
An algorithm for finding patients with diverticulosis, and of those, patients who also have diverticulitis, and to also find control patients. Control patients will have had a colonoscopy but have no evidence of diverticula.
Simple NLP (a portable program is posted here, with instructions, and support is availabe from NU as needed) of colonoscopy reports is the gold standard algorithm, but if the text of colonoscopy reports is not available, an alternate algorithm using CPT & ICD-9 codes can be used, which is also posted.
This algorithm predicts those who are going to be exposed to warfarin, simvastatin, or clopidogrel as three medications that have known pharmacogenomic influences. This algorithm was used to select individuals for the Vanderbilt PREDICT (Pharmacogenomic Resource for Enhanced Decisions in Care & Treatment) program, which prospectively tests individuals at risk of needing medications whose efficacy is effected by genetic variants.
For more information on PREDICT, see http://mydruggenome.org.
This algorithm describes the ongoing resistant hypertension algorithm used in eMERGE, which was a network phenotype within the eMERGE-I and eMERGE-II sites.
This rheumatoid arthritis (RA) algorithm was created using a machine-learning logistic regression model.
- The computable phenotype for the Sleep Apnea Patient Centered Outcomes Network uses existing and well established ICD codes for different types of sleep apnea including 327.23 (adult and pediatric obstructive sleep apnea), 780.51 (insomnia with sleep apnea), 780.53 (hypersomnia with sleep apnea), and 780.57 (unspecified sleep apnea).
Phenotyping algorithm for the identification of patients with type 1 or type 2 diabetes mellitus (DM) preoperatively using routinely available clinical data from electronic