Back
Top 0.2%
14.6%
Top 56%
11.0%
Top 0.2%
9.0%
Top 2%
7.8%
Top 38%
6.4%
Top 1%
6.4%
Top 1%
6.4%
#1
5.9%
Top 5%
5.6%
Top 0.9%
3.9%
Top 2%
2.8%
Top 1%
1.9%
Top 10%
1.9%
Top 16%
1.9%
Top 12%
1.9%
Top 2%
1.5%
Top 3%
1.3%
Top 57%
1.3%
Top 2%
0.9%
Top 2%
0.9%
Automatic identification of risk factors for SARS-CoV-2 positivity and severe clinical outcomes of COVID-19 using Data Mining and Natural Language Processing
2021-03-26
health informatics
Title + abstract only
View on medRxiv
Show abstract
ObjectivesSeveral risk factors have been identified for severe clinical outcomes of COVID-19 caused by SARS-CoV-2. Some can be found in structured data of patients Electronic Health Records. Others are included as unstructured free-text, and thus cannot be easily detected automatically. We propose an automated real-time detection of risk factors using a combination of data mining and Natural Language Processing (NLP). Material and methodsPatients were categorized as negative or positive for SAR...
Predicted journal destinations
1
Journal of Medical Internet Research
81 training papers
2
PLOS ONE
1737 training papers
3
JAMIA Open
35 training papers
4
Journal of the American Medical Informatics Association
53 training papers
5
Scientific Reports
701 training papers
6
BMC Medical Informatics and Decision Making
36 training papers
7
Journal of Biomedical Informatics
37 training papers
8
JMIR Medical Informatics
16 training papers
9
PLOS Digital Health
88 training papers
10
International Journal of Medical Informatics
25 training papers
11
Computers in Biology and Medicine
39 training papers
12
BMC Medical Research Methodology
41 training papers
13
npj Digital Medicine
85 training papers
14
Frontiers in Public Health
135 training papers
15
International Journal of Environmental Research and Public Health
116 training papers
16
JMIR Formative Research
31 training papers
17
JMIR Public Health and Surveillance
45 training papers
18
BMJ Open
553 training papers
19
Frontiers in Digital Health
18 training papers
20
Patterns
15 training papers