ICD 9 Codes

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.

Final

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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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.

Final

White Blood Cell Indices

Genetic variation that predicts white blood count (WBC) and it differential, a marker of the health of the immune system.

WBC is unique among the identified inflammatory predictors of chronic disease in that it has been routinely measured in healthy patients in an unbiased way for the duration of the electronic medical record data. 

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Final

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