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https://github.com/medspacy/medspacy

Library for clinical NLP with spaCy.
https://github.com/medspacy/medspacy
clinical-nlp medspacy nlp nlp-library pipeline spacy
Last synced: about 2 months ago
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Library for clinical NLP with spaCy.
Host: GitHub
URL: https://github.com/medspacy/medspacy
Owner: medspacy
License: mit
Created: 2020-06-16T18:34:16.000Z (over 4 years ago)
Default Branch: master
Last Pushed: 2024-07-15T09:54:27.000Z (2 months ago)
Last Synced: 2024-07-20T01:12:19.045Z (2 months ago)
Topics: clinical-nlp, medspacy, nlp, nlp-library, pipeline, spacy
Language: Jupyter Notebook
Homepage:
Size: 1.63 MB
Stars: 499
Watchers: 16
Forks: 87
Open Issues: 36
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project

README

        [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

![Build Status](https://github.com/medspacy/medspacy/workflows/medspacy/badge.svg)

# medspacy

Library for clinical NLP with spaCy. 

![alt text](./images/medspacy_logo.png "medSpaCy logo")

**MedSpaCy is currently in beta.**

# Overview

MedSpaCy is a library of tools for performing clinical NLP and text processing tasks with the popular [spaCy](https://spacy.io) 

framework. The `medspacy` package brings together a number of other packages, each of which implements specific 

functionality for common clinical text processing specific to the clinical domain, such as sentence segmentation, 

contextual analysis and attribute assertion, and section detection.

`medspacy` is modularized so that each component can be used independently. All of `medspacy` is designed to be used 

as part of a `spacy` processing pipeline. Each of the following modules is available as part of `medspacy`:

- `medspacy.preprocess`: Destructive preprocessing for modifying clinical text before processing

- `medspacy.sentence_splitter`: Clinical sentence segmentation

- `medspacy.ner`: Utilities for extracting concepts from clinical text

- `medspacy.context`: Implementation of the [ConText](https://www.sciencedirect.com/science/article/pii/S1532046409000744)

for detecting semantic modifiers and attributes of entities, including negation and uncertainty

- `medspacy.section_detection`: Clinical section detection and segmentation

- `medspacy.postprocess`: Flexible framework for modifying and removing extracted entities

- `medspacy.io`: Utilities for converting processed texts to structured data and interacting with databases

- `medspacy.visualization`: Utilities for visualizing concepts and relationships extracted from text

- `SpacyQuickUMLS`: UMLS concept extraction compatible with spacy and medspacy implemented by [our fork](https://github.com/medspacy/QuickUMLS) of [QuickUMLS](https://github.com/Georgetown-IR-Lab/QuickUMLS). More detail on this component, how to use it, how to generate UMLS resources beyond the small UMLS sample can be found in [this notebook](notebooks/11a-QuickUMLS_Extraction_Defaults.ipynb).

Future work could include I/O, relations extraction, and pre-trained clinical models.

**As of 10/2/2021 (version 0.2.0.0), medspaCy supports spaCy v3**

# Language support

As of May 2024, medspacy has been restructured to allow distributing rules and resources in languages besides English.

**Please note that some languages are effectively "empty" from the perspective of medspacy. We've had requests to allow some of these languages, but we have no rules since the medspacy team works primarily in English. Please see the table below for a summary of what is available in each language, as they each vary. As of this writing, the languages have been sorted in order of what rules/maturity they currently support. If you are reading this and you have rules or would like to develop rules in another language, please consider contributing. We can help get your work integrated.**

| Language         | ConText rules | Section Rules | QuickUMLS sample (and unit test) |

|------------------|---------------|---------------|------------------------|

| English (en) | Yes | Yes | Yes |

| French (fr) | Yes | Very few | Yes |

| Dutch (nl) | Yes | No | No |

| Spanish (es) | No | Very few | Yes |

| Polish (pl) | No | No | No |

| Portuguese (pt) | No | No | Yes |

| Italian (it) | No | No | Yes |

| German (de) | No | No | No |

Regarding the rules that are available in these languages, a few citations to mention:

* French ConText Rules (adapted): https://hal-lirmm.ccsd.cnrs.fr/lirmm-01656834/document

* Dutch ConText Rules: Paper https://www.medrxiv.org/content/10.1101/2024.03.14.24304289v1.full and Code https://github.com/mi-erasmusmc/medspacy_dutch

# Usage

## Installation

You can install `medspacy` using `setup.py`:

```bash

python setup.py install

```

Or with pip:

```bash

pip install medspacy

```

To install a previous version which uses spaCy 2:

```bash

pip install medspacy==medspacy 0.1.0.2

```

### Requirements

The following packages are required and installed when `medspacy` is installed:

- spaCy v3

- [pyrush](https://github.com/medspacy/PyRuSH)

If you download other models, you can use them by providing the model itself or model name to `medspacy.load(model_name)`:

```python

import spacy; import medspacy

# Option 1: Load default

nlp = medspacy.load()

# Option 2: Load from existing model

nlp = spacy.load("en_core_web_sm", disable={"ner"})

nlp = medspacy.load(nlp)

# Option 3: Load from model name

nlp = medspacy.load("en_core_web_sm", disable={"ner"})

```

    

## Basic Usage

Here is a simple example showing how to implement and visualize a simple rule-based pipeline using `medspacy`:

```python

import medspacy

from medspacy.ner import TargetRule

from medspacy.visualization import visualize_ent

# Load medspacy model

nlp = medspacy.load()

print(nlp.pipe_names)

text = """

Past Medical History:

1. Atrial fibrillation

2. Type II Diabetes Mellitus

Assessment and Plan:

There is no evidence of pneumonia. Continue warfarin for Afib. Follow up for management of type 2 DM.

"""

# Add rules for target concept extraction

target_matcher = nlp.get_pipe("medspacy_target_matcher")

target_rules = [

    TargetRule("atrial fibrillation", "PROBLEM"),

    TargetRule("atrial fibrillation", "PROBLEM", pattern=[{"LOWER": "afib"}]),

    TargetRule("pneumonia", "PROBLEM"),

    TargetRule("Type II Diabetes Mellitus", "PROBLEM", 

              pattern=[

                  {"LOWER": "type"},

                  {"LOWER": {"IN": ["2", "ii", "two"]}},

                  {"LOWER": {"IN": ["dm", "diabetes"]}},

                  {"LOWER": "mellitus", "OP": "?"}

              ]),

    TargetRule("warfarin", "MEDICATION")

]

target_matcher.add(target_rules)

doc = nlp(text)

visualize_ent(doc)

```

`Output:`

![alt text](./images/simple_text_visualization.png "Example of clinical text processed by medSpaCy")

For more detailed examples and explanations of each component, see the [notebooks](./notebooks) folder.

# Citing medspaCy

If you use medspaCy in your work, consider citing our paper! Presented at the AMIA Annual Symposium 2021, [preprint available on Arxiv](http://arxiv.org/abs/2106.07799).

```

H. Eyre, A.B. Chapman, K.S. Peterson, J. Shi, P.R. Alba, M.M. Jones, T.L. Box, S.L. DuVall, O. V Patterson,

Launching into clinical space with medspaCy: a new clinical text processing toolkit in Python,

AMIA Annu. Symp. Proc. 2021 (in Press. (n.d.). 

http://arxiv.org/abs/2106.07799.

```

```

@Article{medspacy,

   Author="Eyre, H.  and Chapman, A. B.  and Peterson, K. S.  and Shi, J.  and Alba, P. R.  and Jones, M. M.  and Box, T. L.  and DuVall, S. L.  and Patterson, O. V. ",

   Title="{{L}aunching into clinical space with medspa{C}y: a new clinical text processing toolkit in {P}ython}",

   Journal="AMIA Annu Symp Proc",

   Year="2021",

   Volume="2021",

   Pages="438--447"

}

}

```

# Made with medSpaCy

Here are some links to projects or tutorials which use medSpacy. If you have a project which uses medSpaCy which you'd like to use, let us know!

- [VA_COVID-19_NLP_BSV](https://github.com/abchapman93/VA_COVID-19_NLP_BSV): An NLP pipeline for [identifying positive cases of COVID-19](https://aclanthology.org/2020.nlpcovid19-acl.10/) from clinical text. Deployed as part of the Department of Veterans Affairs response to COVID-19

- [clinspacy](https://ml4lhs.github.io/clinspacy/index.html): An R wrapper for spaCy, sciSpaCy, and medSpaCy for performing clinical NLP and UMLS linking in R

- [mimic34md2020_materials](https://github.com/Melbourne-BMDS/mimic34md2020_materials): A crash course in clinical data science from the University of Melbourne. For medSpaCy materials, see `notebooks/nlp-*.ipynb`

- [ReHouSED NLP](https://github.com/abchapman93/ReHouSED): An NLP pipeline for [studying Veteran housing stability](https://www.sciencedirect.com/science/article/pii/S153204642100232X) and distinguishing between Veterans who are currently unstably housed and those who have exited homelessness