| Name | medical-named-entity-recognition JSON |
| Version |
0.2
JSON |
| download |
| home_page | https://fastdatascience.com/drug-named-entity-recognition-python-library |
| Summary | Medical Named Entity Recognition library to find and resolve disease names in a string (disease named entity linking) |
| upload_time | 2024-10-20 19:31:43 |
| maintainer | None |
| docs_url | None |
| author | Thomas Wood |
| requires_python | >=3.6 |
| license | MIT License Copyright (c) 2023 Fast Data Science Ltd (https://fastdatascience.com). Maintainer: Thomas Wood. Tutorial at https://fastdatascience.com/drug-named-entity-recognition-python-library/ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
medicine
bio
biomedical
medical
pharma
pharmaceutical
ner
nlp
named entity recognition
natural language processing
named entity linking
clinical
ehr
electronic health record
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
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# Medical Named Entity Recognition Python library by Fast Data Science
## Finds disease names
<!-- badges: start -->
[](https://pypi.org/project/medical-named-entity-recognition/) [](https://github.com/fastdatascience/medical_named_entity_recognition/releases) [](https://github.com/fastdatascience/medical_named_entity_recognition/blob/main/LICENSE)
[](https://pypi.org/project/medical_named_entity_recognition/)
[](https://github.com/fastdatascience/medical_named_entity_recognition/releases) [](https://pypi.org/project/medical_named_entity_recognition/)
[](https://github.com/fastdatascience/medical_named_entity_recognition/forks)
<!-- badges: end -->
# ⚕️ Medical Named Entity Recognition
Developed by Fast Data Science, https://fastdatascience.com
Source code at https://github.com/fastdatascience/medical_named_entity_recognition
This library is in Beta.
## 😊 Who worked on the Medical Named Entity Recognition library?
The tool was developed by:
* Thomas Wood ([Fast Data Science](https://fastdatascience.com))
# 💻Installing Medical Named Entity Recognition Python package
You can install Medical Named Entity Recognition from [PyPI](https://pypi.org/project/drug-named-entity-recognition).
```
pip install medical-named-entity-recognition
```
If you get an error installing Medical Named Entity Recognition, try making a new Python environment in Conda (`conda create -n test-env; conda activate test-env`) or Venv (`python -m testenv; source testenv/bin/activate` / `testenv\Scripts\activate`) and then installing the library.
# 💡Usage examples
You must first tokenise your input text using a tokeniser of your choice (NLTK, spaCy, etc).
You pass a list of strings to the `find_diseases` function.
Example 1
```
import re
re_tokenise = re.compile(r"((?:\w|'|’)+)")
from medical_named_entity_recognition import find_diseases
tokens = re_tokenise.findall("cystic fibrosis")
find_diseases(tokens)
```
outputs a list of tuples.
```
[({'mesh_id': 'D019005',
'mesh_tree': ['C16.320.190', 'C16.614.213', 'C08.381.187', 'C06.689.202'],
'name': 'Cystic Fibrosis',
'synonyms': ['cystic fibrosis',
'mucoviscidosis',
'pancreas fibrocystic diseases',
'pancreas fibrocystic disease',
'cystic fibrosis, pulmonary',
'cystic fibrosis, pancreatic',
'pancreatic cystic fibrosis',
'fibrosis, cystic',
'pulmonary cystic fibrosis',
'fibrocystic disease of pancreas',
'cystic fibrosis of pancreas'],
'is_brand': False,
'match_type': 'exact',
'matching_string': 'cystic fibrosis'},
0,
1)]
```
# Interested in other kinds of named entity recognition (NER)? 💊 Drug names (medicines), pharma, 💸Finances, 🎩company names, 🌎countries, 🗺️locations, proteins, 🧬genes, 🧪molecules?
If your NER problem is common across industries and likely to have been seen before, there may be an off-the-shelf NER tool for your purposes, such as our [Country Named Entity Recognition](https://fastdatascience.com/natural-language-processing/country-named-entity-recognition/) Python library or our [Drug Named Entity Recognition](https://fastdatascience.com/ai-in-pharma/drug-named-entity-recognition-python-library/) library. Dictionary-based named entity recognition is not always the solution, as sometimes the total set of entities is an open set and can't be listed (e.g. personal names), so sometimes a bespoke trained NER model is the answer. For tasks like finding email addresses or phone numbers, regular expressions (simple rules) are sufficient for the job.
If your named entity recognition or named entity linking problem is very niche and unusual, and a product exists for that problem, that product is likely to only solve your problem 80% of the way, and you will have more work trying to fix the final mile than if you had done the whole thing manually. Please [contact Fast Data Science](http://fastdatascience.com/contact) and we'll be glad to discuss. For example, we've worked on [a consultancy engagement to find molecule names in papers, and match author names to customers](https://fastdatascience.com/ai-in-pharma/finding-molecules-and-proteins-in-scientific-literature/) where the goal was to trace molecule samples ordered from a pharma company and identify when the samples resulted in a publication. For this case, there was no off-the-shelf library that we could use.
For a problem like identifying country names in English, which is a closed set with well-known variants and aliases, an off-the-shelf library is usually available. You may wish to try our [Country Named Entity Recognition](https://fastdatascience.com/natural-language-processing/country-named-entity-recognition/) library, also open-source and under MIT license.
For identifying a set of molecules manufactured by a particular company, this is the kind of task more suited to a [consulting engagement](https://fastdatascience.com/natural-language-processing/nlp-consultant/).
# Requirements
Python 3.9 and above
## ✉️Who to contact?
You can contact Thomas Wood or the Fast Data Science team at https://fastdatascience.com/.
## Contributing to the Medical Named Entity Recognition library
If you'd like to contribute to this project, you can contact us at https://fastdatascience.com/ or make a pull request on our [Github repository](https://github.com/fastdatascience/medical_named_entity_recognition). You can also [raise an issue](https://github.com/fastdatascience/medical_named_entity_recognition/issues).
## Developing the Medical Named Entity Recognition library
### Automated tests
Test code is in **tests/** folder using [unittest](https://docs.python.org/3/library/unittest.html).
The testing tool `tox` is used in the automation with GitHub Actions CI/CD.
### Use tox locally
Install tox and run it:
```
pip install tox
tox
```
In our configuration, tox runs a check of source distribution using [check-manifest](https://pypi.org/project/check-manifest/) (which requires your repo to be git-initialized (`git init`) and added (`git add .`) at least), setuptools's check, and unit tests using pytest. You don't need to install check-manifest and pytest though, tox will install them in a separate environment.
The automated tests are run against several Python versions, but on your machine, you might be using only one version of Python, if that is Python 3.9, then run:
```
tox -e py39
```
Thanks to GitHub Actions' automated process, you don't need to generate distribution files locally. But if you insist, click to read the "Generate distribution files" section.
### 🤖 Continuous integration/deployment to PyPI
This package is based on the template https://pypi.org/project/example-pypi-package/
This package
- uses GitHub Actions for both testing and publishing
- is tested when pushing `master` or `main` branch, and is published when create a release
- includes test files in the source distribution
- uses **setup.cfg** for [version single-sourcing](https://packaging.python.org/guides/single-sourcing-package-version/) (setuptools 46.4.0+)
## 🧍Re-releasing the package manually
The code to re-release Medical Named Entity Recognition on PyPI is as follows:
```
source activate py311
pip install twine
rm -rf dist
python setup.py sdist
twine upload dist/*
```
## 😊 Who worked on the Medical Named Entity Recognition library?
The tool was developed by:
* Thomas Wood ([Fast Data Science](https://fastdatascience.com))
# 🤝Compatibility with other natural language processing libraries
The Medical Named Entity Recognition library is independent of other NLP tools and has no dependencies. You don't need any advanced system requirements and the tool is lightweight. However, it combines well with other libraries such as [spaCy](https://spacy.io) or the [Natural Language Toolkit (NLTK)](https://www.nltk.org/api/nltk.tokenize.html).
## 📜License of Medical Named Entity Recognition library
MIT License. Copyright (c) 2024 [Fast Data Science](https://fastdatascience.com)
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"description": "\n\n<a href=\"https://fastdatascience.com\"><span align=\"left\">\ud83c\udf10 fastdatascience.com</span></a>\n<a href=\"https://www.linkedin.com/company/fastdatascience/\"><img align=\"left\" src=\"https://raw.githubusercontent.com//harmonydata/.github/main/profile/linkedin.svg\" alt=\"Fast Data Science | LinkedIn\" width=\"21px\"/></a>\n<a href=\"https://twitter.com/fastdatascienc1\"><img align=\"left\" src=\"https://raw.githubusercontent.com//harmonydata/.github/main/profile/x.svg\" alt=\"Fast Data Science | X\" width=\"21px\"/></a>\n<a href=\"https://www.instagram.com/fastdatascience/\"><img align=\"left\" src=\"https://raw.githubusercontent.com//harmonydata/.github/main/profile/instagram.svg\" alt=\"Fast Data Science | Instagram\" width=\"21px\"/></a>\n<a href=\"https://www.facebook.com/fastdatascienceltd\"><img align=\"left\" src=\"https://raw.githubusercontent.com//harmonydata/.github/main/profile/fb.svg\" alt=\"Fast Data Science | Facebook\" width=\"21px\"/></a>\n<a href=\"https://www.youtube.com/channel/UCLPrDH7SoRT55F6i50xMg5g\"><img align=\"left\" src=\"https://raw.githubusercontent.com//harmonydata/.github/main/profile/yt.svg\" alt=\"Fast Data Science | YouTube\" width=\"21px\"/></a>\n<a href=\"https://g.page/fast-data-science\"><img align=\"left\" src=\"https://raw.githubusercontent.com//harmonydata/.github/main/profile/google.svg\" alt=\"Fast Data Science | Google\" width=\"21px\"/></a>\n<a href=\"https://medium.com/fast-data-science\"><img align=\"left\" src=\"https://raw.githubusercontent.com//harmonydata/.github/main/profile/medium.svg\" alt=\"Fast Data Science | Medium\" width=\"21px\"/></a>\n<a href=\"https://mastodon.social/@fastdatascience\"><img align=\"left\" src=\"https://raw.githubusercontent.com//harmonydata/.github/main/profile/mastodon.svg\" alt=\"Fast Data Science | Mastodon\" width=\"21px\"/></a>\n\n# Medical Named Entity Recognition Python library by Fast Data Science\n\n## Finds disease names\n\n<!-- badges: start -->\n\n[](https://pypi.org/project/medical-named-entity-recognition/) [](https://github.com/fastdatascience/medical_named_entity_recognition/releases) [](https://github.com/fastdatascience/medical_named_entity_recognition/blob/main/LICENSE)\n[](https://pypi.org/project/medical_named_entity_recognition/)\n [](https://github.com/fastdatascience/medical_named_entity_recognition/releases) [](https://pypi.org/project/medical_named_entity_recognition/)\n[](https://github.com/fastdatascience/medical_named_entity_recognition/forks)\n\n<!-- badges: end -->\n\n# \u2695\ufe0f Medical Named Entity Recognition\n\nDeveloped by Fast Data Science, https://fastdatascience.com\n\nSource code at https://github.com/fastdatascience/medical_named_entity_recognition\n\nThis library is in Beta.\n\n## \ud83d\ude0a Who worked on the Medical Named Entity Recognition library?\n\nThe tool was developed by:\n\n* Thomas Wood ([Fast Data Science](https://fastdatascience.com))\n\n# \ud83d\udcbbInstalling Medical Named Entity Recognition Python package\n\nYou can install Medical Named Entity Recognition from [PyPI](https://pypi.org/project/drug-named-entity-recognition).\n\n```\npip install medical-named-entity-recognition\n```\n\nIf you get an error installing Medical Named Entity Recognition, try making a new Python environment in Conda (`conda create -n test-env; conda activate test-env`) or Venv (`python -m testenv; source testenv/bin/activate` / `testenv\\Scripts\\activate`) and then installing the library.\n\n# \ud83d\udca1Usage examples\n\nYou must first tokenise your input text using a tokeniser of your choice (NLTK, spaCy, etc).\n\nYou pass a list of strings to the `find_diseases` function.\n\nExample 1\n\n```\nimport re\nre_tokenise = re.compile(r\"((?:\\w|'|\u2019)+)\")\nfrom medical_named_entity_recognition import find_diseases\ntokens = re_tokenise.findall(\"cystic fibrosis\")\nfind_diseases(tokens)\n```\n\noutputs a list of tuples.\n\n```\n[({'mesh_id': 'D019005',\n 'mesh_tree': ['C16.320.190', 'C16.614.213', 'C08.381.187', 'C06.689.202'],\n 'name': 'Cystic Fibrosis',\n 'synonyms': ['cystic fibrosis',\n 'mucoviscidosis',\n 'pancreas fibrocystic diseases',\n 'pancreas fibrocystic disease',\n 'cystic fibrosis, pulmonary',\n 'cystic fibrosis, pancreatic',\n 'pancreatic cystic fibrosis',\n 'fibrosis, cystic',\n 'pulmonary cystic fibrosis',\n 'fibrocystic disease of pancreas',\n 'cystic fibrosis of pancreas'],\n 'is_brand': False,\n 'match_type': 'exact',\n 'matching_string': 'cystic fibrosis'},\n 0,\n 1)]\n```\n\n\n# Interested in other kinds of named entity recognition (NER)? \ud83d\udc8a Drug names (medicines), pharma, \ud83d\udcb8Finances, \ud83c\udfa9company names, \ud83c\udf0ecountries, \ud83d\uddfa\ufe0flocations, proteins, \ud83e\uddecgenes, \ud83e\uddeamolecules?\n\nIf your NER problem is common across industries and likely to have been seen before, there may be an off-the-shelf NER tool for your purposes, such as our [Country Named Entity Recognition](https://fastdatascience.com/natural-language-processing/country-named-entity-recognition/) Python library or our [Drug Named Entity Recognition](https://fastdatascience.com/ai-in-pharma/drug-named-entity-recognition-python-library/) library. Dictionary-based named entity recognition is not always the solution, as sometimes the total set of entities is an open set and can't be listed (e.g. personal names), so sometimes a bespoke trained NER model is the answer. For tasks like finding email addresses or phone numbers, regular expressions (simple rules) are sufficient for the job.\n\nIf your named entity recognition or named entity linking problem is very niche and unusual, and a product exists for that problem, that product is likely to only solve your problem 80% of the way, and you will have more work trying to fix the final mile than if you had done the whole thing manually. Please [contact Fast Data Science](http://fastdatascience.com/contact) and we'll be glad to discuss. For example, we've worked on [a consultancy engagement to find molecule names in papers, and match author names to customers](https://fastdatascience.com/ai-in-pharma/finding-molecules-and-proteins-in-scientific-literature/) where the goal was to trace molecule samples ordered from a pharma company and identify when the samples resulted in a publication. For this case, there was no off-the-shelf library that we could use.\n\nFor a problem like identifying country names in English, which is a closed set with well-known variants and aliases, an off-the-shelf library is usually available. You may wish to try our [Country Named Entity Recognition](https://fastdatascience.com/natural-language-processing/country-named-entity-recognition/) library, also open-source and under MIT license.\n\nFor identifying a set of molecules manufactured by a particular company, this is the kind of task more suited to a [consulting engagement](https://fastdatascience.com/natural-language-processing/nlp-consultant/).\n\n\n# Requirements\n\nPython 3.9 and above\n\n## \u2709\ufe0fWho to contact?\n\nYou can contact Thomas Wood or the Fast Data Science team at https://fastdatascience.com/.\n\n\n## Contributing to the Medical Named Entity Recognition library\n\nIf you'd like to contribute to this project, you can contact us at https://fastdatascience.com/ or make a pull request on our [Github repository](https://github.com/fastdatascience/medical_named_entity_recognition). You can also [raise an issue](https://github.com/fastdatascience/medical_named_entity_recognition/issues). \n\n## Developing the Medical Named Entity Recognition library\n\n### Automated tests\n\nTest code is in **tests/** folder using [unittest](https://docs.python.org/3/library/unittest.html).\n\nThe testing tool `tox` is used in the automation with GitHub Actions CI/CD.\n\n### Use tox locally\n\nInstall tox and run it:\n\n```\npip install tox\ntox\n```\n\nIn our configuration, tox runs a check of source distribution using [check-manifest](https://pypi.org/project/check-manifest/) (which requires your repo to be git-initialized (`git init`) and added (`git add .`) at least), setuptools's check, and unit tests using pytest. You don't need to install check-manifest and pytest though, tox will install them in a separate environment.\n\nThe automated tests are run against several Python versions, but on your machine, you might be using only one version of Python, if that is Python 3.9, then run:\n\n```\ntox -e py39\n```\n\nThanks to GitHub Actions' automated process, you don't need to generate distribution files locally. But if you insist, click to read the \"Generate distribution files\" section.\n\n### \ud83e\udd16 Continuous integration/deployment to PyPI\n\nThis package is based on the template https://pypi.org/project/example-pypi-package/\n\nThis package\n\n- uses GitHub Actions for both testing and publishing\n- is tested when pushing `master` or `main` branch, and is published when create a release\n- includes test files in the source distribution\n- uses **setup.cfg** for [version single-sourcing](https://packaging.python.org/guides/single-sourcing-package-version/) (setuptools 46.4.0+)\n\n## \ud83e\uddcdRe-releasing the package manually\n\nThe code to re-release Medical Named Entity Recognition on PyPI is as follows:\n\n```\nsource activate py311\npip install twine\nrm -rf dist\npython setup.py sdist\ntwine upload dist/*\n```\n\n## \ud83d\ude0a Who worked on the Medical Named Entity Recognition library?\n\nThe tool was developed by:\n\n* Thomas Wood ([Fast Data Science](https://fastdatascience.com))\n\n# \ud83e\udd1dCompatibility with other natural language processing libraries\n\nThe Medical Named Entity Recognition library is independent of other NLP tools and has no dependencies. You don't need any advanced system requirements and the tool is lightweight. However, it combines well with other libraries such as [spaCy](https://spacy.io) or the [Natural Language Toolkit (NLTK)](https://www.nltk.org/api/nltk.tokenize.html).\n\n## \ud83d\udcdcLicense of Medical Named Entity Recognition library\n\nMIT License. Copyright (c) 2024 [Fast Data Science](https://fastdatascience.com)\n",
"bugtrack_url": null,
"license": "MIT License Copyright (c) 2023 Fast Data Science Ltd (https://fastdatascience.com). Maintainer: Thomas Wood. Tutorial at https://fastdatascience.com/drug-named-entity-recognition-python-library/ Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ",
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