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

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arxiv 2204.06604 v5 pith:4VCDLATP submitted 2022-04-13 cs.CL

Irene Li , Keen You , Yujie Qiao , Lucas Huang , Chia-Chun Hsieh , Benjamin Rosand , Jeremy Goldwasser , Dragomir Radev This is my paper · ORCID
classification cs.CL
keywords informationpartprocessingclinicalehrkitelectronicfunctionshealth
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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.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Comprehensive Survey of Electronic Health Record Modeling: From Deep Learning Approaches to Large Language Models

    cs.LG 2025-07 reject novelty 4.0 of 10

    A survey that taxonomizes EHR modeling research into data-centric, architectural, learning-focused, multimodal, and LLM-based categories, with datasets and metrics.

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