Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources - Wikidata - awgldk - TriplyDB

Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources

Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources

http://www.wikidata.org/entity/Q28597791

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Trends in biomedical informatics: automated topic analysis of JAMIA articles Learning statistical models of phenotypes using noisy labeled training data.A comparison between physicians and computer algorithms for form CMS-2728 data reporting.Clinical Research Informatics for Big Data and Precision Medicine.Ensembles of NLP Tools for Data Element Extraction from Clinical Notes.Discriminative and Distinct Phenotyping by Constrained Tensor Factorization.Clinical phenotyping in selected national networks: demonstrating the need for high-throughput, portable, and computational methods.Study design for non-recurring, time-to-event outcomes in the presence of error-prone diagnostic tests or self-reports Biomedical informatics advancing the national health agenda: the AMIA 2015 year-in-review in clinical and consumer informatics.Text Mining for Precision Medicine: Bringing Structure to EHRs and Biomedical Literature to Understand Genes and Health.Aspiring to Unintended Consequences of Natural Language Processing: A Review of Recent Developments in Clinical and Consumer-Generated Text Processing.The Biobank Portal for Partners Personalized Medicine: A Query Tool for Working with Consented Biobank Samples, Genotypes, and Phenotypes Using i2b2.Implementation of Electronic Consent at a Biobank: An Opportunity for Precision Medicine Research.Clinical Natural Language Processing in 2015: Leveraging the Variety of Texts of Clinical Interest.Automated learning of domain taxonomies from text using background knowledge.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.Applying deep neural networks to unstructured text notes in electronic medical records for phenotyping youth depression.A Computable Phenotype Improves Cohort Ascertainment in a Pediatric Pulmonary Hypertension Registry.Unravelling the human genome-phenome relationship using phenome-wide association studies.Demographic and Indication-Specific Characteristics Have Limited Association With Social Network Engagement: Evidence From 24,954 Members of Four Health Care Support Groups.The use of electronic health records for psychiatric phenotyping and genomics.Biomechanisms of Comorbidity: Reviewing Integrative Analyses of Multi-omics Datasets and Electronic Health Records.EHR-based phenotyping: Bulk learning and evaluation.Bridging semantics and syntax with graph algorithms-state-of-the-art of extracting biomedical relations.The utility of including pathology reports in improving the computational identification of patients.Surrogate-assisted feature extraction for high-throughput phenotyping.PheKB: a catalog and workflow for creating electronic phenotype algorithms for transportability.Clinical Informatics Researcher's Desiderata for the Data Content of the Next Generation Electronic Health Record.Learning Bundled Care Opportunities from Electronic Medical Records.Enabling phenotypic big data with PheNorm.Automated disease cohort selection using word embeddings from Electronic Health Records.Mining 100 million notes to find homelessness and adverse childhood experiences: 2 case studies of rare and severe social determinants of health in electronic health records.High-fidelity phenotyping: richness and freedom from bias.Performing an Informatics Consult: Methods and Challenges.Development of an automated phenotyping algorithm for hepatorenal syndrome.High Throughput Phenotyping for Dimensional Psychopathology in Electronic Health Records.Type 2 diabetes genetic loci informed by multi-trait associations point to disease mechanisms and subtypes: A soft clustering analysis

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Toward high-throughput phenotyping: unbiased automated feature extraction and selection from knowledge sources

http://www.wikidata.org/entity/Q28597791

description

2015 nî lūn-bûn

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2015 թուականի Սեպտեմբերին հրատարակուած գիտական յօդուած

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2015 թվականի սեպտեմբերին հրատարակված գիտական հոդված

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2015年の論文

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2015年論文

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2015年論文

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2015年論文

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2015年論文

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2015年論文

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2015年论文

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Toward high-throughput phenoty ...... lection from knowledge sources

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Toward high-throughput phenoty ...... lection from knowledge sources

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Toward high-throughput phenoty ...... lection from knowledge sources

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Journal of the American Medical Informatics Association

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Toward high-throughput phenoty ...... lection from knowledge sources

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Katherine P Liao

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Shawn N Murphy

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Stanley Y Shaw

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Susanne E Churchill

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Tianxi Cai

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Vivian S Gainer

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P2860

QT interval and antidepressant use: a cross sectional study of electronic health records

A method and knowledge base for automated inference of patient problems from structured data in an electronic medical record

Effect of statin treatment on short term mortality after pneumonia episode: cohort study

The eMERGE Network: a consortium of biorepositories linked to electronic medical records data for conducting genomic studies

The Unified Medical Language System (UMLS): integrating biomedical terminology.

Empirical assessment of methods for risk identification in healthcare data: results from the experiments of the Observational Medical Outcomes Partnership.

"Understanding" medical school curriculum content using KnowledgeMap

Evaluation of the predictive value of ICD-9-CM coded administrative data for venous thromboembolism in the United States.

Secondary use of EHR data for correlated comorbidity prevalence estimate.

Cardiovascular morbidity and mortality in rheumatoid arthritis.

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993-1000

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928042

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10.1093/JAMIA/OCV034

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5

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22

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Peter Szolovits

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2015-09-01T00:00:00Z

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25929596

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4986664

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