Naïve Electronic Health Record phenotype identification for Rheumatoid arthritis.
about
Extracting information from the text of electronic medical records to improve case detection: a systematic reviewToward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sourcesA gene-based association method for mapping traits using reference transcriptome dataBiobanks and electronic medical records: enabling cost-effective researchSecondary use of clinical data: the Vanderbilt approachComputational phenotype discovery using unsupervised feature learning over noisy, sparse, and irregular clinical dataApplying active learning to high-throughput phenotyping algorithms for electronic health records dataBig Data Science: Opportunities and Challenges to Address Minority Health and Health Disparities in the 21st CenturyEmploying computers for the recruitment into clinical trials: a comprehensive systematic review.Design patterns for the development of electronic health record-driven phenotype extraction algorithms.Toward personalizing treatment for depression: predicting diagnosis and severity.Optimising use of electronic health records to describe the presentation of rheumatoid arthritis in primary care: a strategy for developing code listsRelational machine learning for electronic health record-driven phenotyping.Optimising the use of electronic health records to estimate the incidence of rheumatoid arthritis in primary care: what information is hidden in free text?Integration of sequence data from a Consanguineous family with genetic data from an outbred population identifies PLB1 as a candidate rheumatoid arthritis risk geneDevelopment and validation of an electronic phenotyping algorithm for chronic kidney disease.Intelligent use and clinical benefits of electronic health records in rheumatoid arthritisText Mining for Precision Medicine: Bringing Structure to EHRs and Biomedical Literature to Understand Genes and Health.Desiderata for computable representations of electronic health records-driven phenotype algorithms.Reviewing 741 patients records in two hours with FASTVISU.ICD-9 tobacco use codes are effective identifiers of smoking statusElectronic medical record phenotyping using the anchor and learn framework.Patient Stratification Using Electronic Health Records from a Chronic Disease Management Program.Use of a support vector machine for categorizing free-text notes: assessment of accuracy across two institutions.Using association rule mining for phenotype extraction from electronic health records.A review of approaches to identifying patient phenotype cohorts using electronic health records.A systematic comparison of feature space effects on disease classifier performance for phenotype identification of five diseases.PheKnow-Cloud: A Tool for Evaluating High-Throughput Phenotype Candidates using Online Medical Literature.An Empirical Study for Impacts of Measurement Errors on EHR based Association Studies.Surrogate-assisted feature extraction for high-throughput phenotyping.Federated Tensor Factorization for Computational Phenotyping.The influence of big (clinical) data and genomics on precision medicine and drug development.Development of an automated phenotyping algorithm for hepatorenal syndrome.Open Globe Injury Patient Identification in Warfare Clinical Notes.
P2860
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P2860
Naïve Electronic Health Record phenotype identification for Rheumatoid arthritis.
description
2011 nî lūn-bûn
@nan
2011年の論文
@ja
2011年論文
@yue
2011年論文
@zh-hant
2011年論文
@zh-hk
2011年論文
@zh-mo
2011年論文
@zh-tw
2011年论文
@wuu
2011年论文
@zh
2011年论文
@zh-cn
name
Naïve Electronic Health Record phenotype identification for Rheumatoid arthritis.
@ast
Naïve Electronic Health Record phenotype identification for Rheumatoid arthritis.
@en
type
label
Naïve Electronic Health Record phenotype identification for Rheumatoid arthritis.
@ast
Naïve Electronic Health Record phenotype identification for Rheumatoid arthritis.
@en
prefLabel
Naïve Electronic Health Record phenotype identification for Rheumatoid arthritis.
@ast
Naïve Electronic Health Record phenotype identification for Rheumatoid arthritis.
@en
P2093
P2860
P1476
Naïve Electronic Health Record phenotype identification for Rheumatoid arthritis.
@en
P2093
Anne E Eyler
Joshua C Denny
Robert J Carroll
P2860
P304
P577
2011-10-22T00:00:00Z