Facets (new session)
Description
Metadata
Settings
owl:sameAs
Inference Rule:
b3s
b3sifp
dbprdf-label
facets
http://dbpedia.org/resource/inference/rules/dbpedia#
http://dbpedia.org/resource/inference/rules/opencyc#
http://dbpedia.org/resource/inference/rules/umbel#
http://dbpedia.org/resource/inference/rules/yago#
http://dbpedia.org/schema/property_rules#
http://www.ontologyportal.org/inference/rules/SUMO#
http://www.ontologyportal.org/inference/rules/WordNet#
http://www.w3.org/2002/07/owl#
ldp
oplweb
skos-trans
virtrdf-label
None
About:
COVID-19 Recurrent Varies with Different Combinatorial Medical Treatments Determined by Machine Learning Approaches
Goto
Sponge
NotDistinct
Permalink
An Entity of Type :
schema:ScholarlyArticle
, within Data Space :
wasabi.inria.fr
associated with source
document(s)
Type:
Academic Article
research paper
schema:ScholarlyArticle
New Facet based on Instances of this Class
Attributes
Values
type
Academic Article
research paper
schema:ScholarlyArticle
isDefinedBy
Covid-on-the-Web dataset
has title
COVID-19 Recurrent Varies with Different Combinatorial Medical Treatments Determined by Machine Learning Approaches
Creator
Liu, Lei
Wang, Song
Huang, Jia
Qiu, Liping
Cui, Xinping
Liao, Jiayu
Madahar, Vipul
Way, George
Xu, Zehui
Ye, Fangfan
Ye, Manhua
Zeng, Manfei
Zhai, Song
Zhu, Tengfei
Source
MedRxiv
abstract
Various medical treatments for COVID-19 are attempted. After patients are discharged, SARS-CoV-2 recurring cases are reported and the recurrence could profoundly impact patient healthcare and social economics. To date, no data on the effects of medical treatments on recurrence has been published. We analyzed the treatment data of combinations of ten different drugs for the recurring cases in a single medical center, Shenzhen, China. A total of 417 patients were considered and 414 of them were included in this study (3 deaths) with mild-to-critical COVID-19. Patients were treated by 10 different drug combinations and followed up for recurrence for 28 days quarantine after being discharged from the medical center between February and May, 2020. We applied the Synthetic Minority Oversampling Technique (SMOTE) to overcome the rare recurring events in certain age groups and performed Virtual Twins (VT) analysis facilitated by random forest regression for medical treatment-recurrence classification. Among those drug combinations, Methylprednisolone/Interferon/Lopinavir/Ritonavir/Arbidol led to the lowest recurring rate (0.133) as compared to the average recurring rate (0.203). For the younger group (age 20-27) or the older group (age 60-70), the optimal drug combinations are different, but the above combination is still the second best. For obese patients, the combination of Ribavirin/Interferon/Lopinavir/Ritonavir/Arbidol led to the lowest recurring rate for age group of 20-50, whereas the combination of Interferon/Lopinavir/Ritonavir/Arbidol led to lowest recurring rate for age group of 50-70. The insights into combinatorial therapy we provided here shed lights on the use of a combination of (biological and chemical) anti-virus therapy and/or anti-cytokine storm as a potentially effective therapeutic treatment for COVID-19.
has issue date
2020-08-01
(
xsd:dateTime
)
bibo:doi
10.1101/2020.07.29.20164699
has license
medrxiv
sha1sum (hex)
737dccffd711ad2000309f71bb802eede65c5735
schema:url
https://doi.org/10.1101/2020.07.29.20164699
resource representing a document's title
COVID-19 Recurrent Varies with Different Combinatorial Medical Treatments Determined by Machine Learning Approaches
resource representing a document's body
covid:737dccffd711ad2000309f71bb802eede65c5735#body_text
is
schema:about
of
named entity 'days'
named entity 'Synthetic Minority Oversampling Technique'
named entity 'patients'
named entity 'Determined'
named entity 'DRUGS'
named entity '414'
named entity 'DEATHS'
named entity 'DISCHARGED'
named entity 'RIBAVIRIN'
named entity 'RARE'
named entity 'BEING'
named entity 'COMBINATIONS'
named entity 'DRUG COMBINATIONS'
named entity 'LED'
named entity 'TREATMENTS'
named entity 'QUARANTINE'
named entity 'MILD'
named entity 'CASES'
named entity 'STUDY'
named entity 'chemical'
named entity 'random forest'
named entity 'Various'
named entity 'total'
named entity 'Shenzhen'
named entity 'therapeutic'
named entity 'biological'
named entity 'medical treatments'
named entity 'combination'
named entity 'age'
named entity 'drug'
named entity 'patients'
named entity 'compared'
named entity 'medical'
named entity 'Machine Learning'
named entity 'obese'
named entity 'Methylprednisolone'
named entity 'random forest'
named entity 'Arbidol'
named entity 'COVID-19'
named entity 'Combinatorial'
named entity 'COVID-19'
named entity 'peer review'
named entity 'infection'
named entity '21 days'
named entity 'IFNa'
named entity 'virus shedding'
named entity 'statistical analysis'
named entity 'preprint'
named entity 'Ritonavir'
named entity 'mixed infections'
named entity 'Lopinavir'
named entity 'Arbidol'
named entity 'drug treatment'
named entity 'cough'
named entity 'statistical analysis'
named entity 'transfection'
named entity 'preprint'
named entity 'SARS-CoV-2'
named entity 'Arbidol'
named entity 'Favipiravir'
named entity 'death rate'
named entity 'quarantine'
named entity 'IFNa'
named entity 'IFNa'
named entity 'medRxiv'
named entity 'viral infection'
named entity 'asymptomatic'
named entity 'peer review'
named entity 'viruses'
named entity 'preprint'
named entity 'IFN'
named entity 'treatment groups'
◂◂ First
◂ Prev
Next ▸
Last ▸▸
Page 1 of 9
Go
Faceted Search & Find service v1.13.91 as of Mar 24 2020
Alternative Linked Data Documents:
Sponger
|
ODE
Content Formats:
RDF
ODATA
Microdata
About
OpenLink Virtuoso
version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software