Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives.

ABSTRACT: In secondary analysis of electronic health records, a crucial task consists in correctly identifying the patient cohort under investigation. In many cases, the most valuable and relevant information for an accurate classification of medical conditions exist only in clinical narratives. Therefore, it is necessary to use natural language processing (NLP) techniques to extract and evaluate these narratives. The most commonly used approach to this problem relies on extracting a number of clinician-defined medical concepts from text and using machine learning techniques to identify whether a particular patient has a certain condition. However, recent advances in deep learning and NLP enable models to learn a rich representation of (medical) language. Convolutional neural networks (CNN) for text

SUBMITTER: Gehrmann S 

PROVIDER: S-EPMC5813927 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature