Phenotype Description: Patients on statins for primary prevention who develop an AMI or 1st AMI.
Below are algorithms used to identify AMI and 1st AMI cohort at BioVU. If you have questions regarding any of the information presented on this page, you may contact either:
Wei-Qi Wei at wei-qi.wei@vanderbilt.edu
Joshua Denny at josh.denny@vanderbilt.edu
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.
This document describes the Stanford University algorithm to extract individuals with diabetes and the type of diabetes from electronic health records (EHRs). There are two main tasks of this phenotype development: 1) to extract patients with diabetes (gestational diabetes is excluded), and 2) to discriminate between type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM). Instead of identifying all diabetes cases, we aim to reduce the number of false positives in our diabetes cohort.
Phenotype Algorithm for Type 1 Diabetes – eMERGE Phase-IV Program
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.
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.
Recently published GWAS of VTE done by Mayo: http://www.ncbi.nlm.nih.gov/pubmed/22672568