Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/dhchenx/umls-similarity

Estimate similarity of medical concepts based on Unified Medical Language System (UMLS)
https://github.com/dhchenx/umls-similarity

medical-concept medical-terminology semantic-similarity umls umls-metathesaurus umls-similarity unified-medical-language-system

Last synced: about 2 months ago
JSON representation

Estimate similarity of medical concepts based on Unified Medical Language System (UMLS)

Awesome Lists containing this project

README 

        
# UMLS-Similarity



Estimate the similarity of medical concepts based on Unified Medical Language System (UMLS) and WordNet



## Installation



First of all, please install Perl environment ([Strawberry](https://strawberryperl.com/)).



### For UMLS use:

1. Install MySQL and MySQL Workbench and the MySQL Home folder should not have space in its path;

2. Download the UMLS and extract the subset;

3. Goto UMLS's META and NET folders and Load UMLS data into MySQL database with [scripts](https://www.nlm.nih.gov/research/umls/implementation_resources/scripts/README_RRF_MySQL_Output_Stream.html);

4. Install necessary libs with 'cpanm' command with the flag `--force` like below:

   ```

   cpanm UMLS::Interface --force

   

   cpanm UMLS::Similarity --force

   ```

   Errors may occur in the above process, just ignore them. 

5. Please check if you have installed ```DBI```, ```DBD::mysql```; install them if not;



    - Issue: mysql.xs.dll not found problem, please found more details in [link](https://github.com/perl5-dbi/DBD-mysql/issues/318).



    - Solution: Copying C:\strawberry\c\bin\libmysql.dll_ to c:\strawberry\perl\vendor\lib\auto\mysql 



6. Finished!



### For WordNet use (skip it if not)

1. Download the [WordNet-2.1](https://wordnet.princeton.edu/download/old-versions) if you want to use WordNet Similarity (if not, please skip)

2. Set WNHome environment variables (if you need to use WordNet Similarity)

3. Install ```WordNet::QueryData``` via `cpanm` command in perl

4. Install ```WordNet::Similarity``` via `cpanm` command in perl

5. Finished!



### Finally, install our Python package `umls-similrity` via pip



```pip

pip install umls-similarity

```



### Available similarity measures



- Leacock and Chodorow (1998) referred to as lch

- Wu and Palmer (1994) referred to as  wup

- Zhong, et al. (2002) referred to as zhong

- The basic path measure referred to as path

- The undirected path measure referred to as upath

- Rada, et. al. (1989) referred to as cdist

- Nguyan and Al-Mubaid (2006) referred to as nam

- Resnik (1996) referred to as res

- Lin (1988) referred to as lin

- Jiang and Conrath (1997) referred to as jcn

- The vector measure referred to as vector

- Pekar and Staab (2002) referred to as pks

- Pirro and Euzenat (2010) referred to as faith

- Maedche and Staab (2001) referred to as cmatch

- Batet, et al (2011) referred to as batet

- Sanchez, et al. (2012) referred to as sanchez



## Let Codes Speak



Example Code 1: Estimate similarity between two medical concepts using UMLS



```python

from umls_similarity.umls import UMLSSimilarity

import os



if __name__ == "__main__":

    # define MySQL information that stores UMLS data in your computer

    mysql_info = {}

    mysql_info["database"] = "umls"

    mysql_info["username"] = "root"

    mysql_info["password"] = "{I am not gonna tell you}"

    mysql_info["hostname"] = "localhost"



    # Perl bin's path which will be automatically detected by the lib, but you can also manually specify in its constructor

    # perl_bin_path = r"C:\Strawberry\perl\bin\perl"



    # create an instance

    umls_sim = UMLSSimilarity(mysql_info=mysql_info,

                              # perl_bin_path=''

                              )

    

    # show the names of all available measures so you can pass them into the following `measure` parameter

    measures=umls_sim.get_all_measures()

    print(measures)



    # Directly pass two CUIs into the function below:

    sims = umls_sim.similarity(cui1="C0017601", cui2="C0232197", measure="lch")

    print(sims[0])  # only one pair with two concepts

    

    # Or batch process many CUI pairs from a text file where each line is formatted like 'C0006949<>C0031507'

    current_path = os.path.dirname(os.path.realpath(__file__))

    sims = umls_sim.similarity_from_file(current_path + r"\cuis_umls_sim.txt", measure="lch")

    for sim in sims:

        print(sim)

```

Example Code 2: Estimate similarity between concept using WordNet 2.1



```python

from umls_similarity.wordnet import WNSimilarity



if __name__ == "__main__":



    wn_root_path = r"C:\Program Files (x86)\WordNet\2.1"

    # perl_bin_path=r"C:\Strawberry\perl\bin\perl"



    var1 = "dog#n#1"

    var2 = "orange#n#1"



    wn_sim = WNSimilarity(wn_root_path=wn_root_path)



    sims = wn_sim.similarity(var1, var2)

    print(sims)



    for k, v in enumerate(sims):

        print(k, '\t', v, '\t', sims[v])

```



## Credits



This project is a wrapper of the Perl library of [UMLS::Similarity](https://www.d.umn.edu/~tpederse/umls-similarity.html) and [UMLS::Interface](http://www.people.vcu.edu/~btmcinnes/software/umls-interface.html). 



Note: There are plenty of unexpected errors to occur during the installation of the perl library of `UMLS::Similarity`, possibly because I am not an expert about Perl and its library use.



## License

The `umls-similarity` Python package is provided by [Donghua Chen](https://github.com/dhchenx).