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A Comprehensive Typing System for Information Extraction from Clinical Narratives

Caufield, J. H.; Zhou, Y.; Bai, Y.; Liem, D. A.; Garlid, A. O.; Chang, K.-W.; Sun, Y.; Ping, P.; Wang, W.

2019-10-22 health informatics
10.1101/19009118 medRxiv
Show abstract

We have developed ACROBAT (Annotation for Case Reports using Open Biomedical Annotation Terms), a typing system for detailed information extraction from clinical text. This resource supports detailed identification and categorization of entities, events, and relations within clinical text documents, including clincal case reports (CCRs) and the free-text components of electronic health records. Using ACROBAT and the text of 200 CCRs, we annotated a wide variety of real-world clinical disease presentations. The resulting dataset, MACCROBAT2018, is a rich collection of annotated clinical language appropriate for training biomedical natural language processing systems.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

1
Scientific Data
○ 209 papers in training set
Top 0.1%
26.6%
Similar papers in this journal
2
Journal of the American Medical Informatics Association
◐ 71 papers in training set
Top 0.2%
13.1%
Similar papers in this journal
3
JAMIA Open
◐ 42 papers in training set
Top 0.3%
6.3%
Similar papers in this journal
4
Database
◐ 61 papers in training set
Top 0.1%
5.6%
Similar papers in this journal
50% of probability mass above
5
JMIR Medical Informatics
◐ 18 papers in training set
Top 0.1%
4.9%
6
BMC Medical Informatics and Decision Making
○ 43 papers in training set
Top 0.4%
4.9%
Similar papers in this journal
7
Bioinformatics
◐ 1204 papers in training set
Top 5%
3.2%
8
Journal of Biomedical Informatics
○ 47 papers in training set
Top 0.5%
3.2%
9
Scientific Reports
○ 3612 papers in training set
Top 37%
2.8%
10
PLOS ONE
● 5266 papers in training set
Top 39%
2.6%
11
npj Digital Medicine
○ 118 papers in training set
Top 2%
2.1%
12
Nature Communications
○ 5641 papers in training set
Top 44%
1.7%
13
Genetics in Medicine
○ 78 papers in training set
Top 0.6%
1.5%
14
Journal of Medical Internet Research
◐ 87 papers in training set
Top 2%
1.1%
15
iScience
○ 1154 papers in training set
Top 26%
1.1%
16
Nature Medicine
○ 125 papers in training set
Top 2%
1.1%
17
BMJ Health & Care Informatics
● 15 papers in training set
Top 0.9%
0.9%
18
BMC Medical Research Methodology
○ 47 papers in training set
Top 1%
0.9%
19
Computer Methods and Programs in Biomedicine
○ 28 papers in training set
Top 0.9%
0.9%
20
Clinical Trials
○ 11 papers in training set
Top 0.3%
0.8%
21
eLife
● 5828 papers in training set
Top 66%
0.8%
22
Med
○ 39 papers in training set
Top 0.7%
0.8%
23
Patterns
○ 78 papers in training set
Top 3%
0.8%
24
BioData Mining
○ 22 papers in training set
Top 0.7%
0.8%
25
European Journal of Human Genetics
○ 58 papers in training set
Top 1%
0.8%
26
GigaScience
◐ 212 papers in training set
Top 4%
0.8%
27
JCO Clinical Cancer Informatics
● 22 papers in training set
Top 0.8%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.