Academic Commons

Articles

Development and validation of a classification approach for extracting severity automatically from electronic health records

Boland, Mary Regina; Tatonetti, Nicholas P.; Hripcsak, George

Background:
Electronic Health Records (EHRs) contain a wealth of information useful for studying clinical phenotype-genotype relationships. Severity is important for distinguishing among phenotypes; however other severity indices classify patient-level severity (e.g., mild vs. acute dermatitis) rather than phenotype-level severity (e.g., acne vs. myocardial infarction). Phenotype-level severity is independent of the individual patient’s state and is relative to other phenotypes. Further, phenotype-level severity does not change based on the individual patient. For example, acne is mild at the phenotype-level and relative to other phenotypes. Therefore, a given patient may have a severe form of acne (this is the patient-level severity), but this does not effect its overall designation as a mild phenotype at the phenotype-level.

Methods:
We present a method for classifying severity at the phenotype-level that uses the Systemized Nomenclature of Medicine – Clinical Terms. Our method is called the Classification Approach for Extracting Severity Automatically from Electronic Health Records (CAESAR). CAESAR combines multiple severity measures – number of comorbidities, medications, procedures, cost, treatment time, and a proportional index term. CAESAR employs a random forest algorithm and these severity measures to discriminate between severe and mild phenotypes.

Results:
Using a random forest algorithm and these severity measures as input, CAESAR differentiates between severe and mild phenotypes (sensitivity = 91.67, specificity = 77.78) when compared to a manually evaluated reference standard (k = 0.716).

Conclusions:
CAESAR enables researchers to measure phenotype severity from EHRs to identify phenotypes that are important for comparative effectiveness research.

Keywords:
Electronic Health Records Phenotype Health status indicators Data mining Outcome assessment (Health Care)

Files

  • thumnail for 13326_2015_Article_10.pdf 13326_2015_Article_10.pdf binary/octet-stream 2.33 MB Download File

Also Published In

Title
Journal of Biomedical Semantics
DOI
https://doi.org/10.1186/s13326-015-0010-8

More About This Work

Academic Units
Biomedical Informatics
Published Here
July 31, 2015