  This article reviews recent advances in applying natural language processing
(NLP) to Electronic Health Records (EHRs) for computational phenotyping.
NLP-based computational phenotyping has numerous applications including
diagnosis categorization, novel phenotype discovery, clinical trial screening,
pharmacogenomics, drug-drug interaction (DDI) and adverse drug event (ADE)
detection, as well as genome-wide and phenome-wide association studies.
Significant progress has been made in algorithm development and resource
construction for computational phenotyping. Among the surveyed methods,
well-designed keyword search and rule-based systems often achieve good
performance. However, the construction of keyword and rule lists requires
significant manual effort, which is difficult to scale. Supervised machine
learning models have been favored because they are capable of acquiring both
classification patterns and structures from data. Recently, deep learning and
unsupervised learning have received growing attention, with the former favored
for its performance and the latter for its ability to find novel phenotypes.
Integrating heterogeneous data sources have become increasingly important and
have shown promise in improving model performance. Often better performance is
achieved by combining multiple modalities of information. Despite these many
advances, challenges and opportunities remain for NLP-based computational
phenotyping, including better model interpretability and generalizability, and
proper characterization of feature relations in clinical narratives
