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medical-case-report-corpus

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text stringlengths 0 83
-DOCSTART- -X- -X- -X- O

An O O O O
unusual O O O O
case O O O O
of O O O O
chronic O O O O
prostatitis O O O B-condition
caused O O O I-condition
by O O O I-condition
Haemophilus O O O I-condition
influenzae O O O I-condition
in O O O O
an O O O O
elderly O O O B-factor
Saudi O O O I-factor
patient O O O O
: O O O O
A O O O O
case O O O O
report O O O O
and O O O O
literature O O O O
review O O O O
Warning O O O O
: O O O O
O O O O

Here O O O O
, O O O O
we O O O O
report O O O O
a O O O O
rare O O O O
case O O O O
of O O O O
chronic O O O O
prostatitis O O O O
in O O O O
a O O O O
52-year-old O O O B-factor
male O O O B-factor
with O O O O
benign O O O B-factor
prostatic O O O I-factor
hypertrophy O O O I-factor
and O O O O
discuss O O O O
the O O O O
possible O O O O
underestimation O O O O
of O O O O
the O O O O
true O O O O
incidence O O O O
of O O O O
O O O O
H. O O O B-finding
influenzae O O O I-finding
O O O I-finding
in O O O I-finding
genitourinary O O O I-finding
infections O O O I-finding
. O O O O

CASE O O O O
REPORT O O O O
A O O O O
52-year-old O O O B-case
Saudi O O O I-case
man O O O I-case
presented O O O O
on O O O O
the O O O O
July O O O O
1 O O O O
, O O O O
2013 O O O O
, O O O O
to O O O O
the O O O O
urology O O O O
clinic O O O O
complaining O O O O
of O O O O
urinary O O O B-finding
urgency O O O I-finding
, O O O O
dysuria O O O B-finding
, O O O O
frequency O O O B-finding
and O O O O
burning O O O B-finding
sensation O O O I-finding
on O O O I-finding
ejaculation O O O I-finding
. O O O O

He O O O O
also O O O O

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Mirror for https://github.com/adahealth/medical_case_report_corpus/tree/master

We present a new corpus comprising annotations of medical entities in case reports, originating from PubMed Central’s open access library. In the case reports, we annotate cases, conditions, findings, factors and negation modifiers. Moreover, where applicable, we annotate relations between these entities. As such, this is the first corpus of this kind made available to the scientific community in English. It enables the initial investigation of automatic information extraction from case reports through tasks like Named Entity Recognition, Relation Extraction and (sentence/paragraph) relevance detection. Additionally, we present four strong baseline systems for the detection of medical entities made available through the annotated dataset.

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