Limestone: high-throughput candidate phenotype generation via tensor factorization.
about
Building bridges across electronic health record systems through inferred phenotypic topics.Trends in biomedical informatics: automated topic analysis of JAMIA articlesA comparison between physicians and computer algorithms for form CMS-2728 data reporting.Smartphone dependence classification using tensor factorizationDiscriminative and Distinct Phenotyping by Constrained Tensor Factorization.Clinical phenotyping in selected national networks: demonstrating the need for high-throughput, portable, and computational methods.Health Informatics via Machine Learning for the Clinical Management of PatientsImplications of non-stationarity on predictive modeling using EHRsDISCOVERING PATIENT PHENOTYPES USING GENERALIZED LOW RANK MODELSIn silico methods for drug repurposing and pharmacologyPatient Stratification Using Electronic Health Records from a Chronic Disease Management Program.Coronary artery disease risk assessment from unstructured electronic health records using text mining.Large-Scale Discovery of Disease-Disease and Disease-Gene AssociationsLearning Clinical Workflows to Identify Subgroups of Heart Failure Patients.A knowledge-based, automated method for phenotyping in the EHR using only clinical pathology reports.A Graph Based Methodology for Temporal Signature Identification from HER.PheKnow-Cloud: A Tool for Evaluating High-Throughput Phenotype Candidates using Online Medical Literature.Electronic phenotyping with APHRODITE and the Observational Health Sciences and Informatics (OHDSI) data network.Tensor Factorization for Precision Medicine in Heart Failure with Preserved Ejection Fraction.Tensor factorization toward precision medicine.Clinical risk prediction by exploring high-order feature correlations.Federated Tensor Factorization for Computational Phenotyping.The influence of big (clinical) data and genomics on precision medicine and drug development.Learning Bundled Care Opportunities from Electronic Medical Records.Phenotyping of Korean patients with better-than-expected efficacy of moderate-intensity statins using tensor factorization.Rubik
P2860
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P2860
Limestone: high-throughput candidate phenotype generation via tensor factorization.
description
2014 nî lūn-bûn
@nan
2014年の論文
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2014年学术文章
@wuu
2014年学术文章
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2014年学术文章
@zh-cn
2014年学术文章
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2014年学术文章
@zh-my
2014年学术文章
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2014年學術文章
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name
Limestone: high-throughput candidate phenotype generation via tensor factorization.
@en
Limestone: high-throughput candidate phenotype generation via tensor factorization.
@nl
type
label
Limestone: high-throughput candidate phenotype generation via tensor factorization.
@en
Limestone: high-throughput candidate phenotype generation via tensor factorization.
@nl
prefLabel
Limestone: high-throughput candidate phenotype generation via tensor factorization.
@en
Limestone: high-throughput candidate phenotype generation via tensor factorization.
@nl
P2093
P1476
Limestone: high-throughput candidate phenotype generation via tensor factorization
@en
P2093
Bradley A Malin
Jimeng Sun
Joyce C Ho
Joydeep Ghosh
Steve R Steinhubl
Walter F Stewart
P304
P356
10.1016/J.JBI.2014.07.001
P50
P577
2014-07-16T00:00:00Z