Transforming Clinical Data into Actionable Prognosis Models: Machine-Learning Framework and Field-Deployable App to Predict Outcome of Ebola Patients
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Predicting Ebola Severity: A Clinical Prioritization Score for Ebola Virus Disease.Transcriptomic signatures differentiate survival from fatal outcomes in humans infected with Ebola virus.Clinical Laboratory Values as Early Indicators of Ebola Virus Infection in Nonhuman Primates.A survey on Ebola genome and current trends in computational research on the Ebola virus.Machine Learning for Healthcare: On the Verge of a Major Shift in Healthcare Epidemiology.
P2860
Transforming Clinical Data into Actionable Prognosis Models: Machine-Learning Framework and Field-Deployable App to Predict Outcome of Ebola Patients
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2016 nî lūn-bûn
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2016 թուականի Մարտին հրատարակուած գիտական յօդուած
@hyw
2016 թվականի մարտին հրատարակված գիտական հոդված
@hy
2016年の論文
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2016年論文
@yue
2016年論文
@zh-hant
2016年論文
@zh-hk
2016年論文
@zh-mo
2016年論文
@zh-tw
2016年论文
@wuu
name
Transforming Clinical Data int ...... dict Outcome of Ebola Patients
@ast
Transforming Clinical Data int ...... dict Outcome of Ebola Patients
@en
Transforming Clinical Data int ...... dict Outcome of Ebola Patients
@nl
type
label
Transforming Clinical Data int ...... dict Outcome of Ebola Patients
@ast
Transforming Clinical Data int ...... dict Outcome of Ebola Patients
@en
Transforming Clinical Data int ...... dict Outcome of Ebola Patients
@nl
prefLabel
Transforming Clinical Data int ...... dict Outcome of Ebola Patients
@ast
Transforming Clinical Data int ...... dict Outcome of Ebola Patients
@en
Transforming Clinical Data int ...... dict Outcome of Ebola Patients
@nl
P2093
P2860
P921
P1476
Transforming Clinical Data int ...... dict Outcome of Ebola Patients
@en
P2093
Andres Colubri
Kalliroi Retzepi
Terrence Fradet
Tom Silver
P2860
P304
P356
10.1371/JOURNAL.PNTD.0004549
P407
P577
2016-03-01T00:00:00Z