We used a multi-modal strategy consisting of structured database querying, natural language processing on free-text documents, and optical character recognition on scanned clinical images to identify cataract subjects and related cataract attributes.
Note: Attached documents contain full case definition and two different control definitions. One is for controls with 2 years of follow up, the other for controls with 1 year of follow up. All available controls with 2 years of follow up were used in Vanderbilt's study. The control population was supplemented by controls with only 1 year of follow up. At the time of study, many of the available controls had experienced their qualifying events somewhat recently and 2 years had not yet passed for full follow up.
Clostridium difficile, also known as "C. diff," is a species of bacteria that causes severe diarrhea and other intestinal disease when competing bacteria in the gut have been wiped out by antibiotics (see Wikipedia entry). In rare cases a C. diff infection can progress to toxic megacolon which can be life-threatening. In a very small percentage of the adult population C. difficile bacteria naturally reside in the gut. Other people accidentally ingest spores of the bacteria while patients in a hospital or nursing home.
Validation:
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? |
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.
An algorithm for finding patients with diverticulosis, and of those, patients who also have diverticulitis, and to also find control patients. Control patients will have had a colonoscopy but have no evidence of diverticula.
Simple NLP (a portable program is posted here, with instructions, and support is availabe from NU as needed) of colonoscopy reports is the gold standard algorithm, but if the text of colonoscopy reports is not available, an alternate algorithm using CPT & ICD-9 codes can be used, which is also posted.
An algorithm to identify inpatients who have had an acute episode of drug induced liver injury (DILI).
Summary of drug-induced liver injury algorithm
Inclusion criteria
A. Suspect DILI? (NOTE: baseline population is institution specific. See institution implementation details)
1. Liver injury AND Exposure to drug (NOTE: medications are institution specific. See institution implementation details)
2. Temporal relationship of exposure to drug and liver injury diagnosis.
Familial hypercholesterolemia (FH) is a relatively common Mendelian genetic disorder that is associated with elevated plasma low-density lipoprotein cholesterol (LDL-C) levels and dramatically increased lifetime risk for premature atherosclerotic cardiovascular disease (ASCVD). FH can be diagnosed based on clinical presentation and/or genetic testing results, with a positive genetic testing considered to be the “gold standard”.