# Natural Language Processing for EHR-Based Computational Phenotyping

arXiv: https://arxiv.org/abs/1806.04820 · HTML: https://mlsanity.com/paper/1806.04820/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2018.

## Built on by (most cited first, 18 of 18)

- [Scaling Federated Learning for Fine-tuning of Large Language Models](https://mlsanity.com/paper/2102.00875/) (2021)
- [Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review](https://mlsanity.com/paper/2107.02975/) (2021)
- [Natural Language Processing in Electronic Health Records in Relation to Healthcare Decision-making: A Systematic Review](https://mlsanity.com/paper/2306.12834/) (2023)
- [Machine Learning for Multimodal Electronic Health Records-based Research: Challenges and Perspectives](https://mlsanity.com/paper/2111.04898/) (2021)
- [FedRDMA: Communication-Efficient Cross-Silo Federated LLM via Chunked RDMA Transmission](https://mlsanity.com/paper/2403.00881/) (2024)
- [Unsupervised EHR-based Phenotyping via Matrix and Tensor Decompositions](https://mlsanity.com/paper/2209.00322/) (2022)
- [PrecLLM: A Privacy-Preserving Framework for Efficient Clinical Annotation Extraction from Unstructured EHRs using Small-Scale LLMs](https://mlsanity.com/paper/2412.02868/) (2024)
- [Early Prediction of Mortality in Critical Care Setting in Sepsis Patients Using Structured Features and Unstructured Clinical Notes](https://mlsanity.com/paper/2112.01230/) (2021)
- [Multimodal Machine Learning in Precision Health](https://mlsanity.com/paper/2204.04777/) (2022)
- [Deep Stable Representation Learning on Electronic Health Records](https://mlsanity.com/paper/2209.01321/) (2022)
- [Using Large Language Models to Generate Clinical Trial Tables and Figures](https://mlsanity.com/paper/2409.12046/) (2024)
- [HARMON-E: Hierarchical Agentic Reasoning for Multimodal Oncology Notes to Extract Structured Data](https://mlsanity.com/paper/2512.19864/) (2025)
- [Natural language processing to identify lupus nephritis phenotype in electronic health records](https://mlsanity.com/paper/2112.10821/) (2021)
- [AD-BERT: Using Pre-trained contextualized embeddings to Predict the Progression from Mild Cognitive Impairment to Alzheimer's Disease](https://mlsanity.com/paper/2212.06042/) (2022)
- [Unsupervised Learning to Subphenotype Delirium Patients from Electronic Health Records](https://mlsanity.com/paper/2111.00592/) (2021)
- [Collaborative learning of common latent representations in routinely collected multivariate ICU physiological signals](https://mlsanity.com/paper/2402.17917/) (2024)
- [Assessing the Impact of the Quality of Textual Data on Feature Representation and Machine Learning Models](https://mlsanity.com/paper/2502.08669/) (2025)
- [A survey of using EHR as real-world evidence for discovering and validating new drug indications](https://mlsanity.com/paper/2505.24767/) (2025)

## Abstract (arXiv)

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