ICD 10 Codes
Autoimmune diseases (AID) refer to destructive conditions involving an aberrant chronic activation of the adaptive immune system, where the immune cells instead of producing antibodies to attack foreign invaders, mistakenly attack the body’s own healthy cells. While autoimmune diseases are heterogeneous according to symptoms, lesion types, and prognosis, and are usually studied in isolation according to groups based on organ system; various autoimmunity diseases share similar immune effector mechanisms. Recent genetic studies suggest that many autoimmune and chronic autoinflammatory condi
eMERGE-IV BMI Algorithm adapted from Geisinger Extreme Obesity Algorithm (2013). BMI is being implemented as a quantitative trait. PheKB maintains a catalog of the Geisinger Extreme Obesity algorithm, on which this is based (Phenotpe 121). This algorithm is for analysis. Sites only contributing covariates can simply compile the designated data.
Clinical care guidelines recommend that newly diagnosed prostate cancer patients at high risk for metastatic spread receive a bone scan prior to treatment and that low risk patients not receive it. The objective was to develop an automated pipeline to interrogate heterogeneous data to evaluate the use of bone scans using a two different Natural Language Processing (NLP) approaches.
Breast cancer is the most common cancer and the second leading cause of cancer-related death among women in the U.S. Known breast cancer risk factors include age, race/ethnicity, reproductive factors, and benign breast disease. Family history of breast cancer and hereditary cancer syndromes, such as BRCA1/BRCA2 mutations, confer the strongest risk for this disease.
Depression accounts for substantial morbidity and mortality worldwide and risk of experiencing it may have a genetic component. Depressive disorders manifest along a gradient from mild to severe. Electronic health record (EHR) data linked to large, multi-site biobanks facilitate exploration of the genetic component of depression.
Version 1.0, July 2020
Automated Phenotyping Tool for identifying DLD cases in health-systems data (APT-DLD) is an algorithm for classifying/identifying developmental language disorder cases in electronic health records system data. APT-DLD can be used to:
1. Identify pediatric DLD cases from electronic health record systems using ICD9 and ICD10 codes
2. Study epidemiology and population-level charateristics of DLD from EHRs
The How-To guide for using APT-DLD is provided in the files listed below.
Described in this document are the Stanford University algorithms for extracting both cases and controls of digital rectal examination (DRE) from electronic health records (EHR) of prostate cancer patients. DRE is a clinical procedure, part of a set of quality metrics used to determine quality care for these patients. In this regard, DRE is defined as quality care when it is performed within a time period of up to six months before first treatment for prostate cancer. For the purposes of this algorithm a case is defined as DRE documented, whereas a control is DRE not documented.