Natural Language Processing for EHR-Based Computational Phenotyping
arXiv:1806.04820
Abstract
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
References in corpus (10)
- SMOTE: Synthetic Minority Over-sampling Technique
- Efficient Estimation of Word Representations in Vector Space
- Natural Language Processing (almost) from Scratch
- Learning to Diagnose with LSTM Recurrent Neural Networks
- Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Path
- Leveraging Deep Neural Networks and Knowledge Graphs for Entity Disambiguation
- Combining Neural Networks and Log-linear Models to Improve Relation Extraction
- Unsupervised Ensemble Ranking of Terms in Electronic Health Record Notes Based on Their Importance to Patients
- Comparing Rule-Based and Deep Learning Models for Patient Phenotyping
- Toward Mention Detection Robustness with Recurrent Neural Networks