Steroid Induced Osteonecrosis
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Adult
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.
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Type 1 and type 2 Diabetes Mellitus
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.
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Type 1 Diabetes
Phenotype Algorithm for Type 1 Diabetes – eMERGE Phase-IV Program
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Type 2 Diabetes (T2D)
There are two case algorithms provided for T2D. The first (t2d_dprism_ehr_plus_1) is the preferred case algorithm and includes self-reported T2D information collected from survey. The second (t2d_dprism_ehr_1) is an alternative case algorithm that does NOT include self-reported T2D information collected from survey.
We request harmonization based on the preferred algorithm, but if self-reported T2D survey information is not available, the alternative algorithm is acceptable.
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Type 2 Diabetes - PRS Evaluation
NOTE:
The following files were updated on 4/9/2021 so that the output of the #feature table in the eMERGE_IV_OMOP_T2DM_PRS_algorithm script matches the data dictionary.
Files:
- T2DM_DD_Feature_Count_OMOP_2021049.csv
- eMERGE_IV_OMOP_T2DM_PRS_algorithm_20210409.txt
- eMERGE_IV_OMOP_T2DM_PRS_algorithm_20210409.zip
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Type 2 Diabetes Mellitus
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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.
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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.
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Venous Thromboembolism (VTE)
Recently published GWAS of VTE done by Mayo: http://www.ncbi.nlm.nih.gov/pubmed/22672568
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Final