# EHRKit: A Python Natural Language Processing Toolkit for Electronic Health Record Texts

arXiv: https://arxiv.org/abs/2204.06604 · HTML: https://mlsanity.com/paper/2204.06604/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 40 references that are in the map.
- First posted on arXiv 13 Apr 2022.

## Builds on (most cited first, 24 of 24)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Big Bird: Transformers for Longer Sequences](https://mlsanity.com/paper/2007.14062/) (2020)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission](https://mlsanity.com/paper/1904.05342/) (2019)
- [A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents](https://mlsanity.com/paper/1804.05685/) (2018)
- [ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing](https://mlsanity.com/paper/1902.07669/) (2019)
- [Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis](https://mlsanity.com/paper/1706.03446/) (2017)
- [MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III](https://mlsanity.com/paper/1907.08322/) (2019)
- [SciFive: a text-to-text transformer model for biomedical literature](https://mlsanity.com/paper/2106.03598/) (2021)
- [ICD Coding from Clinical Text Using Multi-Filter Residual Convolutional Neural Network](https://mlsanity.com/paper/1912.00862/) (2019)
- [Pre-trained Summarization Distillation](https://mlsanity.com/paper/2010.13002/) (2020)
- [UmlsBERT: Clinical Domain Knowledge Augmentation of Contextual Embeddings Using the Unified Medical Language System Metathesaurus](https://mlsanity.com/paper/2010.10391/) (2020)
- [Launching into clinical space with medspaCy: a new clinical text processing toolkit in Python](https://mlsanity.com/paper/2106.07799/) (2021)
- [Neural Natural Language Processing for Unstructured Data in Electronic Health Records: a Review](https://mlsanity.com/paper/2107.02975/) (2021)
- [Hierarchical Learning for Generation with Long Source Sequences](https://mlsanity.com/paper/2104.07545/) (2021)
- [Attention-based Neural Text Segmentation](https://mlsanity.com/paper/1808.09935/) (2018)
- [Medical Text Classification using Convolutional Neural Networks](https://mlsanity.com/paper/1704.06841/) (2017)
- [A Neural Topic-Attention Model for Medical Term Abbreviation Disambiguation](https://mlsanity.com/paper/1910.14076/) (2019)
- [R-VGAE: Relational-variational Graph Autoencoder for Unsupervised Prerequisite Chain Learning](https://mlsanity.com/paper/2004.10610/) (2020)
- [Unsupervised Clinical Language Translation](https://mlsanity.com/paper/1902.01177/) (2019)
- [Efficient Variational Graph Autoencoders for Unsupervised Cross-domain Prerequisite Chains](https://mlsanity.com/paper/2109.08722/) (2021)

## Built on by (most cited first, 1 of 1)

- [Integrating UMLS Knowledge into Large Language Models for Medical Question Answering](https://mlsanity.com/paper/2310.02778/) (2023)

## Abstract (arXiv)

The Electronic Health Record (EHR) is an essential part of the modern medical system and impacts healthcare delivery, operations, and research. Unstructured text is attracting much attention despite structured information in the EHRs and has become an exciting research field. The success of the recent neural Natural Language Processing (NLP) method has led to a new direction for processing unstructured clinical notes. In this work, we create a python library for clinical texts, EHRKit. This library contains two main parts: MIMIC-III-specific functions and tasks specific functions. The first part introduces a list of interfaces for accessing MIMIC-III NOTEEVENTS data, including basic search, information retrieval, and information extraction. The second part integrates many third-party libraries for up to 12 off-shelf NLP tasks such as named entity recognition, summarization, machine translation, etc.