# NegBio

**Repository Path**: ma-lechi/NegBio

## Basic Information

- **Project Name**: NegBio
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-05-12
- **Last Updated**: 2022-05-12

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

.. image:: https://raw.githubusercontent.com/ncbi-nlp/NegBio/master/images/negbio.png?raw=true
   :target: https://raw.githubusercontent.com/ncbi-nlp/NegBio/master/images/negbio.png?raw=true
   :alt: NegBio

-----------------------

.. image:: https://img.shields.io/travis/yfpeng/NegBio/master.svg
   :target: https://travis-ci.org/yfpeng/NegBio
   :alt: Build status

.. image:: https://img.shields.io/pypi/v/negbio.svg
   :target: https://pypi.python.org/pypi/negbio
   :alt: PyPI version

.. image:: https://img.shields.io/readthedocs/negbio.svg
   :target: http://negbio.readthedocs.io
   :alt: RTD version


NegBio is a high-performance NLP tool for negation and uncertainty detection in clinical texts (e.g. radiology reports).


Get started
===========

Install NegBio
~~~~~~~~~~~~~~

1. Installing from source (recommended)

    .. code-block:: bash

         $ git clone https://github.com/ncbi-nlp/NegBio.git
         $ cd /path/to/negbio
         $ python setup.py install --user
         $ export PATH=~/.local/bin:$PATH

2. Installing from pip

    .. code-block:: bash

        $ pip install negbio




Prepare the dataset
~~~~~~~~~~~~~~~~~~~

The inputs can be in either plain text or `BioC <http://bioc.sourceforge.net/>`_ format.
If the reports are in plain text, each report needs to be in a single file.
Some examples can be found in the ``examples`` folder.

Run the script
~~~~~~~~~~~~~~

There are two ways to run the pipeline.

**NOTE**: If you want to process a lot of reports (e.g., > 1000), it is recommended to run the pipeline step-by-step.
See `User guide <https://negbio.readthedocs.io/en/latest/user_guide.html>`_.


Using the CheXpert algorithm
____________________________

If you want to use the `CheXpert <https://github.com/stanfordmlgroup/chexpert-labeler>`_ method, run one of the following lines

.. code-block:: bash

   $ main_chexpert text --output=examples examples/00000086.txt examples/00019248.txt

.. code-block:: bash

   $ main_chexpert bioc --output=examples examples/1.xml


Using MetaMap
_____________

If you want to use MetaMap, run the following command by replacing ``<METAMAP_BINARY>`` with the actual **ABSOLUTE**
path, such as **META_MAP_HOME/bin/metamap16**

.. code-block:: bash

   $ main_mm text --metamap=<METAMAP_BINARY> --output=examples examples/00000086.txt \
        examples/00019248.txt

.. code-block:: bash

   $ main_mm bioc --metamap=<METAMAP_BINARY> --output=examples examples/1.xml


Documentation
=============

negbio `documentation <http://negbio.readthedocs.io/en/latest/>`_ is available on Read The Docs.

See `Getting Started <http://negbio.readthedocs.io/en/latest/getting_started.html>`_ for installation and basic
information. To contribute to negbio, read our `contribution guide </CONTRIBUTING.md>`_.

Citing NegBio
=============

If you're running the NegBio pipeline, please cite:

*  Peng Y, Wang X, Lu L, Bagheri M, Summers RM, Lu Z. `NegBio: a high-performance tool for negation and uncertainty
   detection in radiology reports <https://arxiv.org/abs/1712.05898>`_. *AMIA 2018 Informatics Summit*. 2018.
*  Wang X, Peng Y, Lu L, Bagheri M, Lu Z, Summers R. `ChestX-ray8: Hospital-scale Chest X-ray database and benchmarks
   on weakly-supervised classification and localization of common thorax diseases <https://arxiv.org/abs/1705.02315>`_.
   *IEEE Conference on Computer Vision and Pattern Recognition (CVPR)*. 2017, 2097-2106.

Acknowledgments
===============

This work was supported by the Intramural Research Programs of the National Institutes of Health, National Library of
Medicine and Clinical Center.

We are grateful to the authors of NegEx, MetaMap, Stanford CoreNLP, Bllip parser, and CheXpert labeler for making
their software tools publicly available.

We thank Dr. Alexis Allot for the helpful discussion.

Disclaimer
==========
This tool shows the results of research conducted in the Computational Biology Branch, NCBI. The information produced
on this website is not intended for direct diagnostic use or medical decision-making without review and oversight
by a clinical professional. Individuals should not change their health behavior solely on the basis of information
produced on this website. NIH does not independently verify the validity or utility of the information produced
by this tool. If you have questions about the information produced on this website, please see a health care
professional. More information about NCBI's disclaimer policy is available.