A pheontype defining patients with strong evidence of having been diagnosed with colorectal cancer (cases) and patients who clearly do not have such diagnoses (controls). This phenotype is being used for sequencing studies. The only NLP involved in this phenotype is a very simple string search applied to pathology reports.
Crohn's Disease phenotype algorithm for the DNA DataBank Demonstration Project. Case records are required to have more than 2 occurrences of ICD 9 codes and medications. Control records are required to not have ICD 9 codes or keyword mention of crohn* or Regional enteritis and excludes additional phenotypes as defined by ICD 9 codes and keywords.
Data source summary:
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Diagnostic Codes? |
Please see the PubMed reference below for background information about this phenotype & algorithm (a copy also attached).
Selects patients with presumptive Alzheimer's disease. The algorithm is looking for records with a minimum of 5 visits with the Diagnosis of interest, using ICD 9 codes OR a dementia drug fill.This algorithm was validated against a curated research cohort.
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.[1] Electronic health record (EHR) data linked to large, multi-site biobanks[2] facilitate exploration of the genetic component of depression.
APT-DLD
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.
Algorithm to identify patients with diabetic retinopathy.
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.
This algorithm builds off prior phenotyping work from Pacheco & Thompson available in the PheKB phenotype "Diverticulosis and Diverticulitis" as well as the manuscripts from Joo et al (2023)(1) and De Roo et al (2023) (2) . The objective is to approximate diverticular disease severity from the electronic medical record into groups of asymptomatic diverticulosis, mild diverticulitis, and severe diverticulitis.