PyUMLS-Similarity 0.0.7

This package computes a variety of similarity metrics between concepts present in the UMLS database

Overview

This package computes a variety of similarity metrics between concepts present in the UMLS (Unified Medical Language System) database. It serves as a Python wrapper based off the Perl module developed by Bridget McInnes and Ted Pedersen, offering an accessible and user-friendly interface for Python users.

Installation

To install PyUMLS_Similarity, run the following command:

pip install pyumls_similarity

Prerequisites

Before using the PyUMLS_Similarity package, ensure that you have the following prerequisites installed and set up:

Strawberry Perl

The package requires Strawberry Perl to run Perl scripts. Download and install it from Strawberry Perl's official website.

MySQL

A local MySQL database instance is required to store and access UMLS data. Download and install MySQL from MySQL's official download page.

UMLS Data

You need to have a local instance of the UMLS installed in MySQL. This involves downloading UMLS data and importing it into your MySQL database. Follow the guidelines provided by the UMLS for obtaining a license and downloading the UMLS data.

UMLS-Interface and UMLS-Similarity Perl Modules

The package depends on the UMLS-Interface and UMLS-Similarity Perl modules. After installing Strawberry Perl, install these modules using CPAN:

cpanm UMLS::Interface
cpanm UMLS::Similarity

Usage

Below are some examples of how to use the PyUMLS_Similarity package.

Start by initiating an instance of the PyUMLS_Similarity class:

from pyumls_similarity import PyUMLS_Similarity

mysql_info = {
    "username": "your_username",
    "password": "your_password",
    "hostname": "localhost",
    "socket": "your_socket",
    "database": "umls"
}

umls_sim = PyUMLS_Similarity(mysql_info=mysql_info)

Computing Multiple Similarity Metrics

You can compute similarity metrics between UMLS concepts as shown below:

cui_pairs = [
    ('C0018563', 'C0037303'),
    ('C0035078', 'C0035078'),
]

# Compute similarity using specific measures
measures = ['lch', 'wup']
similarity_df = umls_sim.similarity(cui_pairs, measures)

An example output would look something like this:

	Term 1	Term 2	CUI 1	CUI 2	lch	wup
0	hand	skull	C0018563	C0037303	0.500	0.700
1	Renal failure	Kidney failure	C0035078	C0035078	1.000	1.000
2	Heart	Myocardium	C0018787	C0027061	0.823	0.875

Finding Shortest Path

To find the shortest path between concepts:

shortest_path_df = umls_sim.find_shortest_path(cui_pairs)

Finding Least Common Subsumer

To find the least common subsumer (LCS) of concepts:

lcs_df = umls_sim.find_least_common_subsumer(cui_pairs)

Concurrency

PyUMLS_Similarity also supports running tasks concurrently for efficiency:

tasks = [
    {'function': 'similarity', 'arguments': (cui_pairs, measures)},
    {'function': 'shortest_path', 'arguments': (cui_pairs)},
    {'function': 'lcs', 'arguments': (cui_pairs)}
]

results = umls_sim.run_concurrently(tasks)

Acknowledgements

This package is based on the Perl module developed by Bridget McInnes and Ted Pedersen.