Enabling phenotypic big data with PheNorm

Abstract

The accuracy of the PheNorm algorithms is on par with algorithms trained with annotated samples. PheNorm fully automates the generation of accurate phenotyping algorithms and demonstrates the capacity for EHR-driven annotations to scale to the next level - phenotypic big data.

Stratifying risk for dementia onset using large‐scale electronic health record data: A retrospective cohort study

“…Finally, the claims‐based outcome is likely imperfect and may be particularly so for earlier onset or less common forms of dementia; however, we note that dementia is typically underdiagnosed (which would bias the present study toward the null hypothesis) and prior formal validation has found positive predictive value of dementia codes to be >75% in most health systems . Further work is needed on highly scalable computed phenotypes as many existing approaches consume a full medical record and thus are poorly suited to time‐to‐event analysis . The NLP approach reported here could contribute to that effort.…”

“…52,53 Further work is needed on highly scalable computed phenotypes as many existing approaches consume a full medical record and thus are poorly suited to time-toevent analysis. 54,55 The NLP approach reported here could contribute to that effort.…”

The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities

“…Unstructured data have also been used to define phenotypes, particularly for diseases with unreliable ICD9 classifications such as some psychiatric diseases, using natural language processing methods . Such methods can also be used to obtain patient measures such as smoking status .…”

“…Some challenges include dealing with misspellings, tenses, alternative phrasing, negation, and defining a trained dictionary of words and phrases that may correspond to a particular phenotype. Algorithms are usually trained using expert annotations, but new methods have attempted to automate this step as well . Additional machine learning methods have also been used to define phenotypes (eg, imaging analytics from medical imaging datasets) using a broad spectrum of patient information …”

“…Unstructured data have also been used to define phenotypes, particularly for diseases with unreliable ICD9 classifications such as some psychiatric diseases, using natural language processing methods. [52][53][54][55][56][57][58][59][60] Such methods can also be used to obtain patient measures such as smoking status. 52 Natural language processing methods mine free text such as narrative doctor's notes for words or phrases to develop a model combining structured and unstructured data to classify each patient as having or not having the phenotype of interest.…”