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A Big Data COVID-19 literature pattern discovery using NLP

Petousis, P.; Stylianou, V.

2022-06-03 bioinformatics
10.1101/2022.06.01.494451 bioRxiv
Show abstract

As our collective knowledge about COVID-19 continues to grow at an exponential rate, it becomes more difficult to organize and observe emerging trends. In this work, we built an open source methodology that uses topic modeling and a pretrained BERT model to organize large corpora of COVID-19 publications into topics over time and over location. Additionally, it assesses the association of medical keywords against COVID-19 over time. These analyses are then automatically pushed into an open source web application that allows a user to obtain actionable insights from across the globe.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

1
Bioinformatics
◐ 1204 papers in training set
Top 3%
9.6%
Similar papers in this journal
2
Journal of Biomedical Informatics
○ 47 papers in training set
Top 0.2%
7.7%
Similar papers in this journal
3
GigaScience
◐ 212 papers in training set
Top 0.2%
7.7%
Similar papers in this journal
4
BMC Bioinformatics
○ 457 papers in training set
Top 1%
6.6%
Similar papers in this journal
5
PLOS ONE
● 5266 papers in training set
Top 28%
5.4%
Similar papers in this journal
6
BioData Mining
○ 22 papers in training set
Top 0.1%
4.7%
7
Database
◐ 61 papers in training set
Top 0.1%
4.7%
8
Journal of Medical Internet Research
◐ 87 papers in training set
Top 0.6%
4.3%
50% of probability mass above
9
PLOS Digital Health
● 106 papers in training set
Top 2%
4.0%
10
BMC Medical Informatics and Decision Making
○ 43 papers in training set
Top 0.5%
4.0%
11
Journal of the American Medical Informatics Association
◐ 71 papers in training set
Top 0.9%
3.4%
12
Computers in Biology and Medicine
○ 128 papers in training set
Top 1%
3.2%
13
IEEE Access
● 35 papers in training set
Top 0.4%
2.7%
14
Artificial Intelligence in Medicine
○ 17 papers in training set
Top 0.2%
2.6%
15
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 2%
2.3%
16
IEEE Journal of Biomedical and Health Informatics
● 37 papers in training set
Top 0.5%
2.3%
17
Scientific Reports
○ 3612 papers in training set
Top 50%
1.9%
18
Bioinformatics Advances
◐ 203 papers in training set
Top 3%
1.6%
19
IEEE/ACM Transactions on Computational Biology and Bioinformatics
● 38 papers in training set
Top 0.6%
1.5%
20
BMC Research Notes
○ 33 papers in training set
Top 0.4%
1.5%
21
Computer Methods and Programs in Biomedicine
○ 28 papers in training set
Top 0.6%
1.3%
22
PLOS Computational Biology
● 1863 papers in training set
Top 18%
1.0%
23
JAMIA Open
◐ 42 papers in training set
Top 1%
1.0%
24
Neuroinformatics
○ 46 papers in training set
Top 0.7%
1.0%
25
iScience
○ 1154 papers in training set
Top 33%
0.9%
26
Gigabyte
● 62 papers in training set
Top 1%
0.8%
27
Patterns
○ 78 papers in training set
Top 3%
0.6%
28
BMJ Health & Care Informatics
● 15 papers in training set
Top 1%
0.6%
29
Briefings in Bioinformatics
◐ 354 papers in training set
Top 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.