Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges.
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Machine learning for epigenetics and future medical applications.Genotype-driven identification of a molecular network predictive of advanced coronary calcium in ClinSeq® and Framingham Heart Study cohorts.Derivation and validation of different machine-learning models in mortality prediction of trauma in motorcycle riders: a cross-sectional retrospective study in southern Taiwan.Machine learning and medicine: book review and commentary.Prediction models for the risk of spontaneous preterm birth based on maternal characteristics: a systematic review and independent external validation.Predicting deterioration of ventricular function in patients with repaired tetralogy of Fallot using machine learning.Cardiac Auscultation Using Smartphones: Pilot Study.A comparative study of logistic regression based machine learning techniques for prediction of early virological suppression in antiretroviral initiating HIV patientsMachine learning assessment of myocardial ischemia using angiography: Development and retrospective validation
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Moving beyond regression techniques in cardiovascular risk prediction: applying machine learning to address analytic challenges.
description
2017 nî lūn-bûn
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2017年の論文
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2017年学术文章
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2017年学术文章
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2017年学术文章
@zh-hans
2017年学术文章
@zh-my
2017年学术文章
@zh-sg
2017年學術文章
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2017年學術文章
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2017年學術文章
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name
Moving beyond regression techn ...... o address analytic challenges.
@en
type
label
Moving beyond regression techn ...... o address analytic challenges.
@en
prefLabel
Moving beyond regression techn ...... o address analytic challenges.
@en
P2093
P2860
P356
P1476
Moving beyond regression techn ...... o address analytic challenges.
@en
P2093
Ann Marie Navar
Benjamin A Goldstein
Rickey E Carter
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P304
P356
10.1093/EURHEARTJ/EHW302
P577
2017-06-01T00:00:00Z
2017-06-14T00:00:00Z