Estimate the similarity of medical concepts based on Unified Medical Language System (UMLS) and WordNet
First of all, please install Perl environment (Strawberry).
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Install MySQL and MySQL Workbench and the MySQL Home folder should not have space in its path;
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Download the UMLS and extract the subset;
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Goto UMLS's META and NET folders and Load UMLS data into MySQL database with scripts;
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Install necessary libs with 'cpanm' command with the flag
--forcelike below:cpanm UMLS::Interface --force cpanm UMLS::Similarity --forceErrors may occur in the above process, just ignore them.
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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.
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Solution: Copying C:\strawberry\c\bin\libmysql.dll_ to c:\strawberry\perl\vendor\lib\auto\mysql
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Finished!
- Download the WordNet-2.1 if you want to use WordNet Similarity (if not, please skip)
- Set WNHome environment variables (if you need to use WordNet Similarity)
- Install
WordNet::QueryDataviacpanmcommand in perl - Install
WordNet::Similarityviacpanmcommand in perl - Finished!
pip install umls-similarity
- 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
Example Code 1: Estimate similarity between two medical concepts using UMLS
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
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])This project is a wrapper of the Perl library of UMLS::Similarity and UMLS::Interface.
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.
The umls-similarity Python package is provided by Donghua Chen.
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