Script annotates clinical concepts to ontology terms using the NCBO API

Clinical_Concept_Annotator

##########################################################################################
# Concept_Annotator.py
# Purpose: script annotates clinical concepts to ontology terms using the NCBO API
# version 1.0.0
##########################################################################################


import urllib2
import json

import pandas as pd

REST_URL = "http://data.bioontology.org"
API_KEY = "3da0dc97-0afd-45a0-9ae8-ddc16f7df642"

def get_json(url):
    opener = urllib2.build_opener()
    opener.addheaders = [('Authorization', 'apikey token=' + API_KEY)]
    return json.loads(opener.open(url).read())

def print_annotations(annotations, get_class=True):
    matches = []

    for result in annotations:
        class_details = get_json(result["annotatedClass"]["links"]["self"]) if get_class else result["annotatedClass"]
        matches.append([class_details["@id"], class_details["prefLabel"]])

    return matches




# Annotate using the provided text
# read in results
df = pd.read_csv('RareDisease_Medications.csv')


# read out results
results = {}

for index, row in df.iterrows():
    key = row['concept_code']
    concept = row['ac_name']
    annotations = get_json(REST_URL + "/annotator?ontologies=RXNORM,MESH&longest_only=true&text=" +
                           urllib2.quote(concept))
    print "\n" + str(key), str(concept)
    print print_annotations(annotations)

    results[key] = print_annotations(annotations)

# Process results and write to file
myfile = open('RxNORM_Mapping.txt', 'w')
for keys, value in results.items():
    print keys
    for item in value:
        if "MESH" in item[0]:
            myfile.write(str(keys) + "\t" + str(item[0].split("/")[-1]) + "\n")

myfile.close()