Super Learner Analysis of Electronic Adherence Data Improves Viral Prediction and May Provide Strategies for Selective HIV RNA Monitoring.
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Improving antiretroviral therapy adherence in resource-limited settings at scale: a discussion of interventions and recommendationsMoving Beyond Directly Observed Therapy for Tuberculosis.Constrained binary classification using ensemble learning: an application to cost-efficient targeted PrEP strategies.Comparison of six methods to estimate adherence in an ART-naïve cohort in a resource-poor setting: which best predicts virological and resistance outcomes?Prediction of absolute risk of acute graft-versus-host disease following hematopoietic cell transplantation.Predicting Outcome of Endovascular Treatment for Acute Ischemic Stroke: Potential Value of Machine Learning Algorithms
P2860
Super Learner Analysis of Electronic Adherence Data Improves Viral Prediction and May Provide Strategies for Selective HIV RNA Monitoring.
description
2015 nî lūn-bûn
@nan
2015 թուականի Մայիսին հրատարակուած գիտական յօդուած
@hyw
2015 թվականի մայիսին հրատարակված գիտական հոդված
@hy
2015年の論文
@ja
2015年論文
@yue
2015年論文
@zh-hant
2015年論文
@zh-hk
2015年論文
@zh-mo
2015年論文
@zh-tw
2015年论文
@wuu
name
Super Learner Analysis of Elec ...... Selective HIV RNA Monitoring.
@ast
Super Learner Analysis of Elec ...... Selective HIV RNA Monitoring.
@en
type
label
Super Learner Analysis of Elec ...... Selective HIV RNA Monitoring.
@ast
Super Learner Analysis of Elec ...... Selective HIV RNA Monitoring.
@en
prefLabel
Super Learner Analysis of Elec ...... Selective HIV RNA Monitoring.
@ast
Super Learner Analysis of Elec ...... Selective HIV RNA Monitoring.
@en
P2093
P2860
P1476
Super Learner Analysis of Elec ...... Selective HIV RNA Monitoring.
@en
P2093
Carol Golin
David Etoori
Erin LeDell
Honghu Liu
Ira B Wilson
Jessica E Haberer
Joshua Schwab
Judith A Erlen
Julia Arnsten
Kathy Goggin
P2860
P304
P356
10.1097/QAI.0000000000000548
P407
P577
2015-05-01T00:00:00Z