def fetch_abstract(pmid):
print(pmid)
Entrez.email = '*****@*****.**'
handle = efetch(db='pubmed', id=pmid, retmode='xml')
xml_data = read(handle)
return xml_data
def fetch_abstract(pmid):
##
## Return abstract of a given
## article using pmid
##
## => Return None when pmid can't be return
## (can happen when article is in chinese)
##
try:
handle = efetch(
db='pubmed',
id=pmid,
retmode='xml',
)
xml_data = read(handle)
xml_data = xml_data['PubmedArticle'][0]
except:
return None
try:
article = xml_data['MedlineCitation']['Article']
abstract = article['Abstract']['AbstractText'][0]
return abstract
except IndexError:
return None
except KeyError:
return None
except:
return None
def author(pmid):
handle = efetch(db='pubmed', id=pmid, retmode='xml')
xml_data = read(handle)['PubmedArticle'][0]
data = xml_data['MedlineCitation']['Article']['AuthorList']
author_list = []
for n in range(len(data)):
author = data[n]
name = author['ForeName'] + ' ' + author['LastName']
aff_info = author['AffiliationInfo']
if aff_info:
aff = aff_info[0]['Affiliation']
else:
aff = ''
author_list.append('#Name ' + name)
if aff:
author_list.append('#Affiliation ' + aff)
author_str = ' '.join(author_list)
return author_str
def specialization(author,affiliation):
# import libraries
import wikipedia
import re
from Bio.Entrez import efetch, read
author = '"'+author+'"'
# Find ID's for doctor + affiliation
ids = []
results = search('%s[AUTH] AND %s[AFFL]' % (author,affiliation))['IdList']
for i in results:
ids.append(i)
num_paper = len(ids)
# get abstracts from list of ID's
query_abstracts = ''
keywords = []
query_keywords = ''
query_title = ''
for i in ids:
xml_data = read(efetch(db='pubmed', id=i, retmode='xml'))
try:
abstract = xml_data['PubmedArticle'][0]['MedlineCitation']['Article']['Abstract']['AbstractText']
query_abstracts = query_abstracts + str(abstract) + ' '
except:
print('Paper with ID: ' + i + ' has no abstract')
#get keuywords from ID's
if xml_data['PubmedArticle'][0]['MedlineCitation']['KeywordList'] != []:
for x in xml_data['PubmedArticle'][0]['MedlineCitation']['KeywordList'][0] :
keywords.append(str(re.sub("[^a-zA-Z]", " ", x)))
query_keywords = query_keywords + x + ' '
#get paper titel from ID's
try:
query_title = query_title + ' ' + xml_data['PubmedArticle'][0]['MedlineCitation']['Article']['ArticleTitle']
except:
print('Paper with ID: ' + i + ' has no title')
# get wiki pages first sentence of keywords
query_wiki = ''
for keyword in keywords:
try:
page = wikipedia.summary(keyword,sentences = 1)
query_wiki = query_wiki + ' ' + str(re.sub("[^a-zA-Z]", " ", page))
except:
print('Disambiguation error for keyword: '+keyword+', action: keyword excluded')
# find specialism
corpus = query_abstracts + ' ' + query_keywords + ' ' + query_wiki + ' ' + query_title
specialization = str(spec_search(corpus))
if num_paper == 0:
print('no papers found')
specialization = []
else:
print('this doctor is specialized in: '+specialization)
return specialization
def fetch_abstract(self,pmid): handle = efetch(db='pubmed', id=pmid, retmode='xml',email='*****@*****.**',retmax=1000) xml_data = read(handle)[0] try: article = xml_data['MedlineCitation']['Article'] abstract = article['Abstract']['AbstractText'][0] return abstract except (IndexError, KeyError): return None
def fetch_abstract(pmid):
handle = efetch(db='pubmed', id=pmid, retmode='xml')
xml_data = read(handle)[0]
try:
article = xml_data['MedlineCitation']['Article']
abstract = article['Abstract']['AbstractText'][0]
return abstract
except IndexError:
return None
def fetch_abstract(pmid):
handle = efetch(db='pubmed', id=pmid, retmode='xml')
xml_data = read(handle)
try:
article = xml_data['PubmedArticle'][0]['MedlineCitation']['Article']
abstract = article['Abstract']['AbstractText'][0]
title = article['ArticleTitle']
return abstract, title
except (IndexError, KeyError) as _:
return None
def fetch_abstract(pmid):
"""Pass in an article id."""
pmid = str(pmid)
try:
handle = efetch(db='pubmed', id=pmid, retmode='xml')
xml_data = read(handle)[0]
article = xml_data['MedlineCitation']['Article']
abstract = article['Abstract']['AbstractText'][0]
return abstract
except Exception as e:
return '{}: {}'.format(e.__class__.__name__, e)
def fetch_abstract(pmid):
"""Pass in an article id."""
pmid = str(pmid)
try:
handle = efetch(db='pubmed', id=pmid, retmode='xml')
xml_data = read(handle)[0]
article = xml_data['MedlineCitation']['Article']
abstract = article['Abstract']['AbstractText'][0]
return abstract
except Exception as e :
return '{}: {}'.format(e.__class__.__name__, e)
def search(self, search_term, retmax=2000):
handle = esearch(db=self.db, term=search_term, retmax=retmax)
self.search_result = read(handle)
self.ids = [str(i) for i in self.search_result.get('IdList')]
print('No. of results: ', len(self.ids))
if len(self.ids) == retmax:
print('###! There might be more results available !###')
return self
def fetch_abstract(pmid):
Entrez.email = "*****@*****.**"
handle = efetch(db='pubmed', id=pmid, retmode='xml')
xml_data = read(handle)[0]
try:
article = xml_data['MedlineCitation']['Article']
abstract = article['Abstract']['AbstractText'][0]
return abstract
except IndexError:
return None
def get_mesh_from_pmid(self, user):
Entrez.email = user.email
handle = efetch(db="pubmed", id=str(self.pmid), retmode="xml")
xml_data = read(handle)[0]
# Skips articles without MeSH terms
if u'MeshHeadingList' in xml_data["MedlineCitation"]:
for mesh in xml_data["MedlineCitation"][u'MeshHeadingList']:
major = "N"
qualifiers = mesh[u'QualifierName']
if len(qualifiers) > 0:
major = str(qualifiers[0].attributes.items()[0][1])
descr = mesh[u'DescriptorName']
name = descr.title()
def get_mesh_from_pmid(self, user):
Entrez.email = user.email
handle = efetch(db = "pubmed", id = str(self.pmid), retmode = "xml")
xml_data = read(handle)[0]
# Skips articles without MeSH terms
if u'MeshHeadingList' in xml_data["MedlineCitation"]:
for mesh in xml_data["MedlineCitation"][u'MeshHeadingList']:
major = "N"
qualifiers = mesh[u'QualifierName']
if len(qualifiers) > 0:
major = str(qualifiers[0].attributes.items()[0][1])
descr = mesh[u'DescriptorName']
name = descr.title()
def fetch_article(pmid): """ Test function => Not working """ handle = efetch(db='pubmed', id=pmid, retmode='xml', ) xml_data = read(handle)[0] try: article = xml_data['MedlineCitation']['Article'] abstract = article['Abstract']['AbstractText'][0] return article except IndexError: return None
def fetch_abstract(pmids):
pmid_str = ",".join(pmids)
try:
handle = efetch(db='pubmed', id=pmid_str, retmode='xml')
except urllib.error.HTTPError:
handle = efetch(db='pubmed', id=pmid_str, retmode='xml')
xml_data = read(handle)['PubmedArticle']
try:
articles = [rec['MedlineCitation'] for rec in xml_data]
except KeyError:
articles = None
return articles
def date(pmid):
handle = efetch(db='pubmed', id=pmid, retmode='xml')
xml_data = read(handle)['PubmedArticle'][0]
data = xml_data['MedlineCitation']['Article']['Journal']['JournalIssue'][
'PubDate']
if 'Day' in data:
day = data['Day']
else:
day = ''
return data['Year'] + ' ' + data['Month'] + ' ' + day
def get_mesh(pmid):
# call PubMed API
handle = efetch(db='pubmed', id=str(pmid), retmode='xml')
xml_data = read(handle)[0]
# skip articles without MeSH terms
if u'MeshHeadingList' in xml_data['MedlineCitation']:
for mesh in xml_data['MedlineCitation'][u'MeshHeadingList']:
# grab the qualifier major/minor flag, if any
major = 'N'
qualifiers = mesh[u'QualifierName']
if len(qualifiers) > 0:
major = str(qualifiers[0].attributes.items()[0][1])
# grab descriptor name
descr = mesh[u'DescriptorName']
name = descr.title()
yield(name, major)
def get_metadata_from_PMID( pmid ):
"""This function will take an input PMID and parse the attributes I am interested in for the cytoscape plugin...."""
handle = efetch(db='pubmed', id=pmid, retmode='xml')
xml_data = read(handle)[0]
verbose_output = False
try:
date_completed = format_ddate( xml_data['MedlineCitation']['DateCompleted'] )
except:
print "Data Completed not available??",pmid
try:
otherID = xml_data['MedlineCitation']['OtherID']
except:
print "Other ID Not availble??",pmid
try:
MeshHeadings = xml_data['MedlineCitation']['MeshHeadingList']
except:
print "Unable to get mesheadings for",pmid
try:
article = xml_data['MedlineCitation']['Article']
if verbose_output: print xml_data
#print date_completed,otherID
for author in article['AuthorList']:
#author_key = { 'LastNAme': author['LastName'], 'Initials': author['Initials'] }
author_key = author['LastName'] + ','+ author['Initials']
#print author['LastName'],author['Initials'],author,'MOO'
if author_key in global_author_list:
global_author_list[ author_key ] +=1
#print "adding author"
else:
global_author_list[ author_key ] = 1
#print "I ADDED AN AUTHOR..."
#return abstract
except IndexError:
return None
except:
print "unable to process",pmid
print "Unexpected error:", sys.exc_info()[0]
try:
abstract = article['Abstract']['AbstractText'][0]
except:
print "Unable to get abstract for",pmid
print "Unexpected error:", sys.exc_info()[0]
def get_article_title(pmid):
"""
Connect to pubmed database and get the article
title of the given pmid.
Return NA if faild
"""
handle = efetch(
db='pubmed',
id=pmid,
retmode='xml',
)
xml_data = read(handle)
xml_data = xml_data['PubmedArticle'][0]
try:
title = xml_data['MedlineCitation']['Article']['ArticleTitle']
except:
title = "NA"
return title
def getDocument(self,pmid):
#this method will return all associated attributes for an article
#including Article Title , Publication Date , Authors' Names , Citations......etc.
# it will return it as a python dictionary suitable for storage in mongodb
handle = efetch(db='pubmed', id=str(pmid), retmode='xml')
xml_data = read(handle)[0]
article = dict(id = pmid,Title = str(xml_data['MedlineCitation']['Article'][u'ArticleTitle'])
, Abstract=str(self.safeAbstract(xml_data['MedlineCitation'],u'Abstract')),
DateCompleted="{}/{}/{}".format(self.safeDateCompleted(xml_data['MedlineCitation'],'DateCompleted','Day'),
self.safeDateCompleted(xml_data['MedlineCitation'],'DateCompleted','Month'),
self.safeDateCompleted(xml_data['MedlineCitation'],'DateCompleted','Year'),),
DateRevised="{}/{}/{}".format(self.safeDateCompleted(xml_data['MedlineCitation'],'DateRevised','Day'),
self.safeDateCompleted(xml_data['MedlineCitation'],'DateRevised','Month'),
self.safeDateCompleted(xml_data['MedlineCitation'],'DateRevised','Year')))
return (xml_data,article)
def fetch_article(pmid): """ Test function => Not working """ handle = efetch(db='pubmed', id=pmid, retmode='xml', ) informations = read(handle) stuff = informations[u'PubmedArticle'][0] date = stuff[u'PubmedData']["History"][1] month = date[u'Month'] day = date[u'Day'] year = date[u'Year'] print month print day print year return "choucroute"
def fetch_abstract(pmid): """ Retrun abstract of a given article using pmid => Return None when pmid can't be return (can happen when article is in chinese) """ handle = efetch(db='pubmed', id=pmid, retmode='xml', ) xml_data = read(handle) xml_data = xml_data['PubmedArticle'][0] try: article = xml_data['MedlineCitation']['Article'] abstract = article['Abstract']['AbstractText'][0] return abstract except IndexError: return None except KeyError: return None
def get_country_publication_stat(run_folder):
##
## Get the publications stats for country.
## get the list of pmid retrieved from the
## meta folder and connect to the NCBI to fecth
## publications informations, parse it to get the
## country of publication.
##
## return a dictionnary
##
## init structure
country_to_count = {}
## get list of PMID to process
meta_file_list = glob.glob(run_folder+"/meta/*.csv")
for meta_file in meta_file_list:
meta_file_in_array = meta_file.split("/")
file_name = meta_file_in_array[-1]
file_name_in_array = file_name.split(".")
pmid = file_name_in_array[0]
## get country publication
try:
handle = efetch(db='pubmed', id=pmid, retmode='xml', )
informations = read(handle)
stuff = informations[u'PubmedArticle'][0]
country = stuff[u'MedlineCitation'][u'MedlineJournalInfo'][u'Country']
print country # to delete
except:
country = "NA"
## fill dictionnary
if(country not in country_to_count.keys()):
country_to_count[country] = 1
else:
country_to_count[country] += 1
return country_to_count
def date(pmid):
handle = efetch(db='pubmed', id=pmid, retmode='xml')
xml_data = read(handle)['PubmedArticle']
if xml_data:
xml_list = xml_data[0]
data = xml_list['MedlineCitation']['Article']['Journal'][
'JournalIssue']['PubDate']
if 'Day' in data:
day = data['Day']
else:
day = ''
if 'Month' in data:
month = data['Month'] + ' '
else:
month = ''
if 'Year' in data:
year = data['Year'] + ' '
else:
year = ''
return year + month + day
else:
return ''
def fetch_article(pmid):
"""
Test function
=> Not working
"""
handle = efetch(
db='pubmed',
id=pmid,
retmode='xml',
)
informations = read(handle)
stuff = informations[u'PubmedArticle'][0]
date = stuff[u'PubmedData']["History"][1]
month = date[u'Month']
day = date[u'Day']
year = date[u'Year']
print month
print day
print year
return "choucroute"
ifsituationlist = []
ifyearlist = []
sumimpactfactor = 0
repeatcount = 0
for ID in IdList:
allIdLink.append("https://pubmed.ncbi.nlm.nih.gov/" + ID + "/")
repeatcount += 1
if repeatcount == 1:
pass
else:
allgenename.append("")
issn = []
handle = efetch(db="pubmed", id=ID, retmode="xml")
xml_data = read(handle)
articledata = xml_data["PubmedArticle"][0]["MedlineCitation"][
"Article"]
try:
titledata = articledata["ArticleTitle"]
except:
titledata = []
print("False:titledata")
strtitledata = str(titledata)
titlelist.append(strtitledata)
try:
yeardata = articledata["Journal"]["JournalIssue"]["PubDate"][
"MedlineDate"]
except:
def evaluate_article(pmid):
##
## [IN PROGRESS]
##
## -> Test if the abstract is cool
## -> return true or false
##
##------------------------##
## Parameters for filters ##
##------------------------##
oldest_year_authorized = 2008
authorized_languages = [u'eng']
valid_article = False
check_date = True
check_language = True
validation_check_keywords_1 = False
validation_check_keywords_2 = False
##---------------##
## The Easy Part ##
##---------------##
## get meta data on the articles
try:
handle = efetch(
db='pubmed',
id=pmid,
retmode='xml',
)
informations = read(handle)
stuff = informations[u'PubmedArticle'][0]
## get date from the history attribute, select
## the date of acceptation.
date = stuff[u'PubmedData']["History"][1]
month = date[u'Month']
day = date[u'Day']
year = date[u'Year']
## get the name of the review
journal_name = informations[u'PubmedArticle'][0][u'MedlineCitation'][
u'MedlineJournalInfo'][u'MedlineTA']
## get the keywords for the articles
## the format is a bit strange, may have to be carreful
## with this data (mix of strings and unicode elements)
keywords_list = informations[u'PubmedArticle'][0][u'MedlineCitation'][
u'KeywordList']
## Get the author's conflict of interest,
## because we can.
try:
conflict_of_interest = informations[u'PubmedArticle'][0][
u'MedlineCitation'][u'CoiStatement']
except:
conflict_of_interest = "NA"
## Get title of the article
article_title = informations[u'PubmedArticle'][0][u'MedlineCitation'][
u'Article'][u'ArticleTitle']
## Get language of the article
article_language = informations[u'PubmedArticle'][0][
u'MedlineCitation'][u'Article'][u'Language'][0]
except:
return (False, False, False)
##----------------##
## The Smart Part ##
##----------------##
## run further analysis on the abstract using nltk
## fetch the abstract and convert it to
## a nltk text object.
abstract_file_name = "abstract/" + str(pmid) + "_abstract.txt"
abstract = fetch_abstract(pmid)
if (abstract):
save_abstract(abstract, abstract_file_name)
abstract_text = load_text(abstract_file_name)
## Play with tokenization and chunking
## Get all the commun names in the abstract
names_found_in_abstract = []
try:
tokens = nltk.word_tokenize(abstract.encode('utf8'))
tagged = nltk.pos_tag(tokens)
entities = nltk.chunk.ne_chunk(tagged)
except:
print "[WARNINGS] => can't perform nlp operation"
entities = []
for item in entities:
try:
if (item[1] in ["NN", "NNS", "NNP"]):
if (item[0] not in names_found_in_abstract):
names_found_in_abstract.append(item[0])
except:
## Somethig went wrong
choucroute = True
## -> Biology keywords check
## -> Artificial intelligence keywords check
IA_keywords = [
"algorithm", "machine"
"learning", "neural", "network", "statistic", "deep",
"classification", "model"
]
Clinical_keywords = [
"Sjogren", "sjogren", "lupus", "autoimmunity", "rhumatoid",
"arthrisis", "RA", "SjS", "SLE"
]
for item in names_found_in_abstract:
if (item in IA_keywords):
validation_check_keywords_1 = True
if (item in Clinical_keywords):
validation_check_keywords_2 = True
##--------------##
## PASS OR FAIL ##
##--------------##
## General check phase
easy_check_passed = False
smart_check_passed = False
## Basic check on meta data
## - check date
if (int(year) < int(oldest_year_authorized)):
check_date = False
## - check language
if (article_language not in authorized_languages):
check_language = False
## Easy Filter
if (check_date and check_language):
easy_check_passed = True
## Complex filter
if (validation_check_keywords_1 and validation_check_keywords_2):
smart_check_passed = True
## Global check
if (easy_check_passed and smart_check_passed):
valid_article = True
##-------------##
## SAVING DATA ##
##-------------##
## Write and delete files
if (valid_article):
## Save meta data in a text file
## for further use
title_line = u'>Title;' + unicode(article_title) + u"\n"
date_line = u'>Date;' + unicode(day) + u"/" + unicode(
month) + u"/" + unicode(year) + u"\n"
journal_line = u">Journal;" + unicode(journal_name) + u"\n"
conflict_of_interest_line = u">Conflict;" + unicode(
conflict_of_interest) + u"\n"
meta_data = open("meta/" + str(pmid) + ".csv", "w")
meta_data.write(title_line.encode('utf8'))
meta_data.write(date_line.encode('utf8'))
meta_data.write(journal_line.encode('utf8'))
meta_data.write(conflict_of_interest_line.encode('utf8'))
meta_data.close()
else:
## Delete the abstract
try:
if (abstract):
os.remove(abstract_file_name)
except:
print "[WARNING] => can't delete " + str(abstract_file_name)
##------------------##
## RETURN SOMETHING ##
##------------------##
## return True if the article pass the
## evaluation, else False.
return (valid_article, easy_check_passed, smart_check_passed)
def get_metadata_from_PMID( pmid, output_errors=False, dump_xml=False ):
"""This function will take an input PMID and parse the attributes I am interested in for the cytoscape plugin...."""
handle = efetch(db='pubmed', id=pmid, retmode='xml')
xml_data = read(handle)[0]
verbose_output = False
# output_errors= False
author_affiliation_list = []
cur_paper_author_list = []
try:
date_completed = format_ddate( xml_data['MedlineCitation']['DateCompleted'] )
except:
print "Date Completed not available??",pmid
## Will try date created
#date_completed = None
#date_created = format_ddate( xml_data['MedlineCitation']['DateCreated'] )
## I am removing the difference between date completed and created-- it doens't really matter for my purposes
date_completed = format_ddate( xml_data['MedlineCitation']['DateCreated'] )
#fp_error.write('Date Completed Not Avaiable:\n'+str(xml_data)+'\n\n')
try:
otherID = xml_data['MedlineCitation']['OtherID']
except:
print "Other ID Not availble??",pmid
try:
MeshHeadings = xml_data['MedlineCitation']['MeshHeadingList']
except:
print "Unable to get mesheadings for",pmid
if output_errors: fp_error.write('MESH NOT AVAIABLE:\n'+str(xml_data)+'\n\n')
try:
article = xml_data['MedlineCitation']['Article']
if verbose_output: print xml_data
for author in article['AuthorList']:
#author_key = { 'LastNAme': author['LastName'], 'Initials': author['Initials'] }
#print author
if 'LastName' in author:
author_key = author['LastName'] + ','+ author['Initials']
#print author,author_key
cur_paper_author_list.append(author_key)
elif 'CollectiveName' in author:
print "FOUND A COLLECTION EXAMPLE",author
if 'Affiliation' in author:
author_affil = author['Affiliation']
author_affiliation_list.append( (author, author_affil) )
# print author_affil
# sys.exit()
except NameError as e:
print e
except IndexError:
return None
except:
print "unable to proces article tag",pmid
print "Unexpected error parsing author string:", sys.exc_info()[0]
if output_errors: fp_error.write('Article NOT AVAILABLE\n'+str(xml_data)+'\n\n')
print author
#print xml_data
try:
abstract = article['Abstract']['AbstractText'][0]
except:
print "Unable to get abstract for",pmid
if dump_xml:
print xml_data
return xml_data
else:
return { 'auth_list': cur_paper_author_list, 'affiliations': author_affiliation_list, 'publication_date': date_completed }
def evaluate_article(pmid):
##
## [IN PROGRESS]
##
## -> Test if the abstract is cool
## -> return true or false
##
##------------------------##
## Parameters for filters ##
##------------------------##
oldest_year_authorized = 2008
authorized_languages = [u'eng']
valid_article = False
check_date = True
check_language = True
validation_check_keywords_1 = False
validation_check_keywords_2 = False
##---------------##
## The Easy Part ##
##---------------##
## get meta data on the articles
try:
handle = efetch(db='pubmed', id=pmid, retmode='xml', )
informations = read(handle)
stuff = informations[u'PubmedArticle'][0]
## get date from the history attribute, select
## the date of acceptation.
date = stuff[u'PubmedData']["History"][1]
month = date[u'Month']
day = date[u'Day']
year = date[u'Year']
## get the name of the review
journal_name = informations[u'PubmedArticle'][0][u'MedlineCitation'][u'MedlineJournalInfo'][u'MedlineTA']
## get the keywords for the articles
## the format is a bit strange, may have to be carreful
## with this data (mix of strings and unicode elements)
keywords_list = informations[u'PubmedArticle'][0][u'MedlineCitation'][u'KeywordList']
## Get the author's conflict of interest,
## because we can.
try:
conflict_of_interest = informations[u'PubmedArticle'][0][u'MedlineCitation'][u'CoiStatement']
except:
conflict_of_interest = "NA"
## Get title of the article
article_title = informations[u'PubmedArticle'][0][u'MedlineCitation'][u'Article'][u'ArticleTitle']
## Get language of the article
article_language = informations[u'PubmedArticle'][0][u'MedlineCitation'][u'Article'][u'Language'][0]
except:
return (False,False,False)
##----------------##
## The Smart Part ##
##----------------##
## run further analysis on the abstract using nltk
## fetch the abstract and convert it to
## a nltk text object.
abstract_file_name = "abstract/"+str(pmid)+"_abstract.txt"
abstract = fetch_abstract(pmid)
if(abstract):
save_abstract(abstract, abstract_file_name)
abstract_text = load_text(abstract_file_name)
## Play with tokenization and chunking
## Get all the commun names in the abstract
names_found_in_abstract = []
try:
tokens = nltk.word_tokenize(abstract.encode('utf8'))
tagged = nltk.pos_tag(tokens)
entities = nltk.chunk.ne_chunk(tagged)
except:
print "[WARNINGS] => can't perform nlp operation"
entities = []
for item in entities:
try:
if(item[1] in ["NN", "NNS", "NNP"]):
if(item[0] not in names_found_in_abstract):
names_found_in_abstract.append(item[0])
except:
## Somethig went wrong
choucroute = True
## -> Biology keywords check
## -> Artificial intelligence keywords check
IA_keywords = ["algorithm", "machine" "learning", "neural", "network", "statistic", "deep", "classification", "model"]
Clinical_keywords = ["Sjogren" ,"sjogren", "lupus", "autoimmunity", "rhumatoid", "arthrisis", "RA", "SjS", "SLE"]
for item in names_found_in_abstract:
if(item in IA_keywords):
validation_check_keywords_1 = True
if(item in Clinical_keywords):
validation_check_keywords_2 = True
##--------------##
## PASS OR FAIL ##
##--------------##
## General check phase
easy_check_passed = False
smart_check_passed = False
## Basic check on meta data
## - check date
if(int(year) < int(oldest_year_authorized)):
check_date = False
## - check language
if(article_language not in authorized_languages):
check_language = False
## Easy Filter
if(check_date and check_language):
easy_check_passed = True
## Complex filter
if(validation_check_keywords_1 and validation_check_keywords_2):
smart_check_passed = True
## Global check
if(easy_check_passed and smart_check_passed):
valid_article = True
##-------------##
## SAVING DATA ##
##-------------##
## Write and delete files
if(valid_article):
## Save meta data in a text file
## for further use
title_line = u'>Title;'+unicode(article_title)+u"\n"
date_line = u'>Date;'+unicode(day)+u"/"+unicode(month)+u"/"+unicode(year)+u"\n"
journal_line = u">Journal;"+unicode(journal_name)+u"\n"
conflict_of_interest_line = u">Conflict;"+unicode(conflict_of_interest)+u"\n"
meta_data = open("meta/"+str(pmid)+".csv", "w")
meta_data.write(title_line.encode('utf8'))
meta_data.write(date_line.encode('utf8'))
meta_data.write(journal_line.encode('utf8'))
meta_data.write(conflict_of_interest_line.encode('utf8'))
meta_data.close()
else:
## Delete the abstract
try:
if(abstract):
os.remove(abstract_file_name)
except:
print "[WARNING] => can't delete "+str(abstract_file_name)
##------------------##
## RETURN SOMETHING ##
##------------------##
## return True if the article pass the
## evaluation, else False.
return (valid_article, easy_check_passed, smart_check_passed)
def evaluate_article(pmid):
##
## [IN PROGRESS]
##
## -> Test if the abstract is cool
## -> return true or false
##
## TODO : write doc
##
##------------------------##
## Parameters for filters ##
##------------------------##
## initialize parameters
oldest_year_authorized = "NA"
case_report_only = False
case_report_check = False
authorized_languages = []
valid_article = False
check_date = True
check_language = True
validation_check = {}
validation_keywords = {}
exclusion_check = {}
exclusion_keywords = {}
exclusion_keywords_found = False
## test if config file exist
if (os.path.isfile("config.conf")):
config_data = open("config.conf", "r")
validation_keywords_cmpt = 0
exclusion_keywords_cmpt = 0
for line in config_data:
line = line.replace("\n", "")
line_in_array = line.split(";")
if (line_in_array[0] == "min year"):
oldest_year_authorized = line_in_array[1]
elif (line_in_array[0] == "authorized languages"):
languages_list = line_in_array[1].split(",")
for elt in languages_list:
authorized_languages.append(unicode(elt))
elif (line_in_array[0] == "validation keywords"):
validation_keywords_cmpt += 1
validation_check["keywords_" +
str(validation_keywords_cmpt)] = False
validation_keywords["keywords_" +
str(validation_keywords_cmpt)] = []
keywords_list = line_in_array[1].split(",")
for elt in keywords_list:
if (elt not in validation_keywords[
"keywords_" + str(validation_keywords_cmpt)]):
validation_keywords[
"keywords_" +
str(validation_keywords_cmpt)].append(str(elt))
## Retrieve Exclusion list
elif (line_in_array[0] == "exclusion keywords"):
exclusion_keywords_found = True
exclusion_keywords_cmpt += 1
exclusion_check["exclusion_" +
str(exclusion_keywords_cmpt)] = False
exclusion_keywords["exclusion_" +
str(exclusion_keywords_cmpt)] = []
keywords_list = line_in_array[1].split(",")
for elt in keywords_list:
if (elt not in exclusion_keywords[
"exclusion_" + str(exclusion_keywords_cmpt)]):
exclusion_keywords[
"exclusion_" +
str(exclusion_keywords_cmpt)].append(str(elt))
## case report only option
## if nothing is set, default is False
elif (line_in_array[0] == "case report only"
and str(line_in_array[1]) == "True"):
case_report_only = True
config_data.close()
## default configuration
else:
oldest_year_authorized = 2008
authorized_languages = [u'eng']
validation_check["keywords_1"] = False
validation_check["keywords_2"] = False
validation_keywords["keywords_1"] = [
"algorithm", "machine"
"learning", "neural", "network", "statistic", "deep",
"classification", "model"
]
validation_keywords["keywords_2"] = [
"Sjogren", "sjogren", "lupus", "autoimmunity", "rhumatoid",
"arthrisis", "RA", "SjS", "SLE"
]
exclusion_check["exclusion_1"] = False
exclusion_keywords["exclusion_1"] = []
if (not exclusion_keywords_found):
exclusion_check["exclusion_1"] = False
exclusion_keywords["exclusion_1"] = []
##---------------##
## The Easy Part ##
##---------------##
## get meta data on the articles
try:
handle = efetch(
db='pubmed',
id=pmid,
retmode='xml',
)
informations = read(handle)
stuff = informations[u'PubmedArticle'][0]
## get date from the history attribute, select
## the date of acceptation.
date = stuff[u'PubmedData']["History"][1]
month = date[u'Month']
day = date[u'Day']
year = date[u'Year']
## get the name of the review
journal_name = informations[u'PubmedArticle'][0][u'MedlineCitation'][
u'MedlineJournalInfo'][u'MedlineTA']
## get the keywords for the articles
## the format is a bit strange, may have to be carreful
## with this data (mix of strings and unicode elements)
keywords_list = informations[u'PubmedArticle'][0][u'MedlineCitation'][
u'KeywordList']
## Get the author's conflict of interest,
## because we can.
try:
conflict_of_interest = informations[u'PubmedArticle'][0][
u'MedlineCitation'][u'CoiStatement']
except:
conflict_of_interest = "NA"
## Get title of the article
article_title = informations[u'PubmedArticle'][0][u'MedlineCitation'][
u'Article'][u'ArticleTitle']
## Get language of the article
article_language = informations[u'PubmedArticle'][0][
u'MedlineCitation'][u'Article'][u'Language'][0]
## Get country of publications
country = stuff[u'MedlineCitation'][u'MedlineJournalInfo'][u'Country']
except:
return (False, False, False)
##----------------##
## The Smart Part ##
##----------------##
## run further analysis on the abstract using nltk
##
## WORKING ON EXCLUSION LIST
##
## fetch the abstract and convert it to
## a nltk text object.
abstract_file_name = "abstract/" + str(pmid) + "_abstract.txt"
abstract = fetch_abstract(pmid)
if (abstract):
save_abstract(abstract, abstract_file_name)
abstract_text = load_text(abstract_file_name)
## Play with tokenization and chunking
## Get all the commun names in the abstract
names_found_in_abstract = []
try:
tokens = nltk.word_tokenize(abstract.encode('utf8'))
tagged = nltk.pos_tag(tokens)
entities = nltk.chunk.ne_chunk(tagged)
except:
print "[WARNINGS] => can't perform nlp operation"
entities = []
for item in entities:
try:
if (item[1] in ["NN", "NNS", "NNP"]):
if (item[0] not in names_found_in_abstract):
names_found_in_abstract.append(item[0])
except:
## Somethig went wrong
choucroute = True
## Check validation list
for item in names_found_in_abstract:
for key in validation_keywords.keys():
keywords_validation_list = validation_keywords[key]
if (item in keywords_validation_list):
validation_check[key] = True
## Check exclusion list
for item in names_found_in_abstract:
for key in exclusion_keywords.keys():
exclusion_validation_list = exclusion_keywords[key]
if (item in exclusion_validation_list):
exclusion_check[key] = True
## Check if is a case report
if (case_report_only):
print "[DEBUG] => Case report only"
if (article_is_a_case_report(abstract_file_name)):
case_report_check = True
##--------------##
## PASS OR FAIL ##
##--------------##
## General check phase
easy_check_passed = False
smart_check_passed = True
## Basic check on meta data
## - check date
if (int(year) < int(oldest_year_authorized)):
check_date = False
## - check language
if (article_language not in authorized_languages):
check_language = False
## Easy Filter
if (check_date and check_language):
easy_check_passed = True
## Complex filter (inclusion)
if (False in validation_check.values()):
smart_check_passed = False
## Complex filter (exclusion)
if (True in exclusion_check.values()):
smart_check_passed = False
## Case reprot filter
if (case_report_only and case_report_check):
print "[DEBUG] => EXLUDED"
smart_check_passed = False
## Global check
if (easy_check_passed and smart_check_passed):
valid_article = True
##-------------##
## SAVING DATA ##
##-------------##
## Write and delete files
if (valid_article):
## Save meta data in a text file
## for further use
title_line = u'>Title;' + unicode(article_title) + u"\n"
date_line = u'>Date;' + unicode(day) + u"/" + unicode(
month) + u"/" + unicode(year) + u"\n"
#date_line = '>Date;'+str(day.encode('utf8'))+"/"+str(month.encode(utf8))+"/"+str(year.encode("utf8"))+"\n"
journal_line = u">Journal;" + unicode(journal_name) + u"\n"
country_line = u">Country;" + unicode(country) + u"\n"
conflict_of_interest_line = u">Conflict;" + unicode(
conflict_of_interest) + u"\n"
meta_data = open("meta/" + str(pmid) + ".csv", "w")
meta_data.write(title_line.encode('utf8'))
meta_data.write(date_line.encode('utf8'))
meta_data.write(journal_line.encode('utf8'))
meta_data.write(country_line.encode('utf8'))
meta_data.write(conflict_of_interest_line.encode('utf8'))
meta_data.close()
else:
## Delete the abstract
try:
if (abstract):
os.remove(abstract_file_name)
except:
print "[WARNING] => can't delete " + str(abstract_file_name)
##------------------##
## RETURN SOMETHING ##
##------------------##
## return True if the article pass the
## evaluation, else False.
return (valid_article, easy_check_passed, smart_check_passed)