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import sys
import json
import datetime
from bs4 import BeautifulSoup
from bs4.element import NavigableString
# from: https://www.ncbi.nlm.nih.gov/books/NBK3827/table/pubmedhelp.T.publication_types/?report=objectonly
PUBMED_RELEASE_TYPE_MAP = {
#Adaptive Clinical Trial
"Address": "speech",
"Autobiography": "book",
#Bibliography
"Biography": "book",
#Case Reports
"Classical Article": "article-journal",
#Clinical Conference
#Clinical Study
#Clinical Trial
#Clinical Trial, Phase I
#Clinical Trial, Phase II
#Clinical Trial, Phase III
#Clinical Trial, Phase IV
#Clinical Trial Protocol
#Clinical Trial, Veterinary
#Collected Works
#Comparative Study
#Congress
#Consensus Development Conference
#Consensus Development Conference, NIH
#Controlled Clinical Trial
"Dataset": "dataset",
#Dictionary
#Directory
#Duplicate Publication
"Editorial": "editorial",
#English Abstract # doesn't indicate that this is abstract-only
#Equivalence Trial
#Evaluation Studies
#Expression of Concern
#Festschrift
#Government Document
#Guideline
"Historical Article": "article-journal",
#Interactive Tutorial
"Interview": "interview",
"Introductory Journal Article": "article-journal",
"Journal Article": "article-journal",
"Lecture": "speech",
"Legal Case": "legal_case",
"Legislation": "legislation",
"Letter": "letter",
#Meta-Analysis
#Multicenter Study
#News
"Newspaper Article": "article-newspaper",
#Observational Study
#Observational Study, Veterinary
#Overall
#Patient Education Handout
#Periodical Index
#Personal Narrative
#Portrait
#Practice Guideline
#Pragmatic Clinical Trial
#Publication Components
#Publication Formats
#Publication Type Category
#Randomized Controlled Trial
#Research Support, American Recovery and Reinvestment Act
#Research Support, N.I.H., Extramural
#Research Support, N.I.H., Intramural
#Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.
#Research Support, U.S. Gov't, P.H.S.
#Review # in the "literature review" sense, not "product review"
#Scientific Integrity Review
#Study Characteristics
#Support of Research
#Systematic Review
"Technical Report": "report",
#Twin Study
#Validation Studies
#Video-Audio Media
#Webcasts
}
MONTH_ABBR_MAP = {
"Jan": 1, "01": 1,
"Feb": 2, "02": 2,
"Mar": 3, "03": 3,
"Apr": 4, "04": 4,
"May": 5, "05": 5,
"Jun": 6, "06": 6,
"Jul": 7, "07": 7,
"Aug": 8, "08": 8,
"Sep": 9, "09": 9,
"Oct": 10, "10": 10,
"Nov": 11, "11": 11,
"Dec": 12, "12": 12,
}
class PubMedParser():
"""
Converts PubMed/MEDLINE XML into in release entity (which can dump as JSON)
TODO: MEDLINE doesn't include PMC/OA license; could include in importer?
TODO: clean (ftfy) title, original title, etc
"""
def __init__(self):
pass
def parse_file(self, handle):
# 1. open with beautiful soup
soup = BeautifulSoup(handle, "xml")
# 2. iterate over articles, call parse_article on each
for article in soup.find_all("PubmedArticle"):
resp = self.parse_article(article)
print(json.dumps(resp))
#sys.exit(-1)
def parse_article(self, a):
medline = a.MedlineCitation
# PubmedData isn't required by DTD, but seems to always be present
pubmed = a.PubmedData
extra = dict()
extra_pubmed = dict()
identifiers = pubmed.ArticleIdList
doi = identifiers.find("ArticleId", IdType="doi")
if doi:
doi = doi.string.lower()
pmcid = identifiers.find("ArticleId", IdType="pmc")
if pmcid:
pmcid = pmcid.string
release_type = None
for pub_type in medline.Article.PublicationTypeList.find_all("PublicationType"):
if pub_type.string in PUBMED_RELEASE_TYPE_MAP:
release_type = PUBMED_RELEASE_TYPE_MAP[pub_type.string]
break
if medline.Article.PublicationTypeList.find(string="Retraction of Publication"):
release_type = "retraction"
retraction_of = medline.find("CommentsCorrections", RefType="RetractionOf")
if retraction_of:
extra_pubmed['retraction_of_raw'] = retraction_of.RefSource.string
extra_pubmed['retraction_of_pmid'] = retraction_of.PMID.string
# everything in medline is published
release_status = "published"
if medline.Article.PublicationTypeList.find(string="Corrected and Republished Article"):
release_status = "updated"
if medline.Article.PublicationTypeList.find(string="Retracted Publication"):
release_status = "retracted"
pages = medline.find('MedlinePgn')
if pages:
pages = pages.string
title = medline.Article.ArticleTitle.string # always present
if title:
if title.endswith('.'):
title = title[:-1]
# this hides some "special" titles, but the vast majority are
# translations; translations don't always include the original_title
if title.startswith('[') and title.endswith(']'):
title = title[1:-1]
else:
# TODO: will filter out later
title = None
original_title = medline.Article.find("VernacularTitle", recurse=False)
if original_title:
original_title = original_title.string or None
if original_title and original_title.endswith('.'):
original_title = original_title[:-1]
# TODO: happening in alpha order, not handling multi-language well.
# also need to convert lang codes: https://www.nlm.nih.gov/bsd/language_table.html
language = medline.Article.Language
if language:
language = language.string
# TODO: map to two-letter
if language in ("und", "un"):
# "undetermined"
language = None
### Journal/Issue Metadata
# MedlineJournalInfo is always present
container = dict()
container_extra = dict()
mji = medline.MedlineJournalInfo
if mji.find("Country"):
container_extra['country_name'] = mji.Country.string
if mji.find("ISSNLinking"):
container['issnl'] = mji.ISSNLinking.string
journal = medline.Article.Journal
issnp = journal.find("ISSN", IssnType="Print")
if issnp:
container_extra['issnp'] = issnp.string
pub_date = journal.PubDate
release_date = None
if pub_date.find("MedlineDate"):
release_year = int(pub_date.MedlineDate.string.split()[0][:4])
else:
release_year = int(pub_date.Year.string)
if pub_date.find("Day") and pub_date.find("Month"):
release_date = datetime.date(
release_year,
MONTH_ABBR_MAP[pub_date.Month.string],
int(pub_date.Day.string))
release_date = release_date.isoformat()
ji = journal.JournalIssue
volume = None
if ji.find("Volume"):
volume = ji.Volume.string
issue = None
if ji.find("Issue"):
issue = ji.Issue.string
if journal.find("Title"):
container['name'] = journal.Title.string
if extra_pubmed:
extra['pubmed'] = extra_pubmed
if not extra:
extra = None
### Abstracts
# "All abstracts are in English"
abstracts = []
first_abstract = medline.find("AbstractText")
if first_abstract and first_abstract.get('NlmCategory'):
joined = "\n".join([m.get_text() for m in medline.find_all("AbstractText")])
abstracts.append(dict(
content=joined,
mimetype="text/plain",
lang="en",
))
else:
for abstract in medline.find_all("AbstractText"):
abstracts.append(dict(
content=abstract.get_text().strip(),
mimetype="text/plain",
lang="en",
))
if abstract.find('math'):
abstracts.append(dict(
# strip the <AbstractText> tags
content=str(abstract)[14:-15],
mimetype="application/mathml+xml",
lang="en",
))
if not abstracts:
abstracts = None
### Contribs
contribs = []
if medline.AuthorList:
for author in medline.AuthorList.find_all("Author"):
contrib = dict(
role="author",
)
if author.ForeName:
contrib['raw_name'] = "{} {}".format(author.ForeName.string, author.LastName.string)
elif author.LastName:
contrib['raw_name'] = author.LastName.string
contrib_extra = dict()
orcid = author.find("Identifier", Source="ORCID")
if orcid:
# needs re-formatting from, eg, "0000000179841889"
orcid = orcid.string
if orcid.startswith("http://orcid.org/"):
orcid = orcid.replace("http://orcid.org/", "")
elif orcid.startswith("https://orcid.org/"):
orcid = orcid.replace("https://orcid.org/", "")
elif not '-' in orcid:
orcid = "{}-{}-{}-{}".format(
orcid[0:4],
orcid[4:8],
orcid[8:12],
orcid[12:16],
)
contrib_extra['orcid'] = orcid
affiliation = author.find("Affiliation")
if affiliation:
contrib['raw_affiliation'] = affiliation.string
if author.find("EqualContrib"):
# TODO: schema for this?
contrib_extra['equal_contrib'] = True
if contrib_extra:
contrib['extra'] = contrib_extra
contribs.append(contrib)
if medline.AuthorList['CompleteYN'] == 'N':
contribs.append(dict(raw_name="et al."))
if not contribs:
contribs = None
### References
refs = []
if pubmed.ReferenceList:
for ref in pubmed.ReferenceList.find_all('Reference'):
ref_obj = dict()
ref_extra = dict()
ref_pmid = ref.find("ArticleId", IdType="pubmed")
if ref_pmid:
ref_extra['pmid'] = ref_pmid.string
ref_raw = ref.Citation
if ref_raw:
ref_extra['raw'] = ref_raw.string
if ref_extra:
ref_obj['extra'] = ref_extra
refs.append(ref_obj)
if not refs:
refs = None
re = dict(
work_id=None,
title=title,
original_title=original_title,
release_type=release_type,
release_status=release_status,
release_date=release_date,
release_year=release_year,
doi=doi,
pmid=int(medline.PMID.string), # always present
pmcid=pmcid,
#isbn13 # never in Article
volume=volume,
issue=issue,
pages=pages,
#publisher # not included?
language=language,
#license_slug # not in MEDLINE
# content, mimetype, lang
abstracts=abstracts,
# raw_name, role, raw_affiliation, extra
contribs=contribs,
# key, year, container_name, title, locator
# extra: volume, authors, issue, publisher, identifiers
refs=refs,
# name, type, publisher, issnl
# extra: issnp, issne, original_name, languages, country
container=container,
# extra:
# withdrawn_date
# translation_of
# subtitle
# aliases
# container_name
# group-title
# pubmed: retraction refs
extra=extra,
)
return re
if __name__=='__main__':
parser = PubMedParser()
parser.parse_file(open(sys.argv[1]))
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