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EHRKit-2022

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

[![Python 3.6.13](https://img.shields.io/badge/python-3.6.13-green.svg)](https://www.python.org/downloads/release/python-360/)

This library aims at processing medical texts in electronic health records. We provide specific functions to access the [MIMIC-III](https://physionet.org/content/mimiciii-demo/) record efficiently; the method includes searching by record ID, searching similar records, searching with an input query. We also support functions for some NLP tasks, including abbreviation disambiguation, extractive and abstractive summarization. For more specific evaluaiton, please check this [pre-print]([url](https://arxiv.org/abs/2204.06604)).

Moreover, if users want to deal with general medical texts, we integrate third-party libraries, including [hugginface](https://huggingface.co/), [scispacy](https://allenai.github.io/scispacy/), [allennlp](https://github.com/allenai/allennlp), [stanza](https://stanfordnlp.github.io/stanza/), and so on. Please checkout the special verison of this library, [EHRKit-WF](https://github.com/Yale-LILY/EHRKit/tree/master/wrapper_functions).





## Table of Contents

1. [Updates](#updates)
2. [Data](#data)
...
- Negation Detection
- Section Detection
- UMLS Concept Extraction



	a/README.md		b/README.md
1	<p align="center">
2	<img src="https://github.com/Yale-LILY/EHRKit/blob/master/wrapper_functions/EHRLogo.png?format=raw" alt="EHRKit"/>
3	</p>
4		1
5		2
6	# EHRKit: A Python Natural Language Processing Toolkit for Electronic Health Record Texts	3	# EHRKit: A Python Natural Language Processing Toolkit for Electronic Health Record Texts
7		4
8	[![Python 3.6.13](https://img.shields.io/badge/python-3.6.13-green.svg)](https://www.python.org/downloads/release/python-360/)	5	[![Python 3.6.13](https://img.shields.io/badge/python-3.6.13-green.svg)](https://www.python.org/downloads/release/python-360/)
9		6
10	This library aims at processing medical texts in electronic health records. We provide specific functions to access the [MIMIC-III](https://physionet.org/content/mimiciii-demo/) record efficiently; the method includes searching by record ID, searching similar records, searching with an input query. We also support functions for some NLP tasks, including abbreviation disambiguation, extractive and abstractive summarization. For more specific evaluaiton, please check this [pre-print]([url](https://arxiv.org/abs/2204.06604)).	7	This library aims at processing medical texts in electronic health records. We provide specific functions to access the [MIMIC-III](https://physionet.org/content/mimiciii-demo/) record efficiently; the method includes searching by record ID, searching similar records, searching with an input query. We also support functions for some NLP tasks, including abbreviation disambiguation, extractive and abstractive summarization. For more specific evaluaiton, please check this [pre-print]([url](https://arxiv.org/abs/2204.06604)).
11		8
12	Moreover, if users want to deal with general medical texts, we integrate third-party libraries, including [hugginface](https://huggingface.co/), [scispacy](https://allenai.github.io/scispacy/), [allennlp](https://github.com/allenai/allennlp), [stanza](https://stanfordnlp.github.io/stanza/), and so on. Please checkout the special verison of this library, [EHRKit-WF](https://github.com/Yale-LILY/EHRKit/tree/master/wrapper_functions).	9	Moreover, if users want to deal with general medical texts, we integrate third-party libraries, including [hugginface](https://huggingface.co/), [scispacy](https://allenai.github.io/scispacy/), [allennlp](https://github.com/allenai/allennlp), [stanza](https://stanfordnlp.github.io/stanza/), and so on. Please checkout the special verison of this library, [EHRKit-WF](https://github.com/Yale-LILY/EHRKit/tree/master/wrapper_functions).
13		10
14	<p align="center">	11
15	<img src="https://github.com/Yale-LILY/EHRKit-2022/blob/main/ehrkit.jpg?format=raw" alt="EHRKit"/>
16	</p>
17		12
18	## Table of Contents	13	## Table of Contents
19		14
20	1. [Updates](#updates)	15	1. [Updates](#updates)
21	2. [Data](#data)	16	2. [Data](#data)
	...		...
149	- Negation Detection	144	- Negation Detection
150	- Section Detection	145	- Section Detection
151	- UMLS Concept Extraction	146	- UMLS Concept Extraction
152		147
153		148