Using machine learning for concept extraction on clinical documents from multiple data sources.
about
Clinical research informatics: a conceptual perspectiveNamed Entity Recognition in Chinese Clinical Text Using Deep Neural Network.Analysis of medication and indication occurrences in clinical notesFacilitating post-surgical complication detection through sublanguage analysisMission and Sustainability of Informatics for Integrating Biology and the Bedside (i2b2)A computational framework for converting textual clinical diagnostic criteria into the quality data modelEnsembles of NLP Tools for Data Element Extraction from Clinical Notes.Machine learning workflow to enhance predictions of Adverse Drug Reactions (ADRs) through drug-gene interactions: application to drugs for cutaneous diseasesImproving condition severity classification with an efficient active learning based frameworkMedXN: an open source medication extraction and normalization tool for clinical textRecognizing clinical entities in hospital discharge summaries using Structural Support Vector Machines with word representation features.Automated Assessment of Medical Students' Clinical Exposures according to AAMC Geriatric CompetenciesExtracting patient demographics and personal medical information from online health forums.EHR based Genetic Testing Knowledge Base (iGTKB) Development.De-identification of psychiatric intake records: Overview of 2016 CEGS N-GRID shared tasks Track 1.Towards a semantic lexicon for clinical natural language processing.Pooling annotated corpora for clinical concept extraction.Automatically extracting sentences from Medline citations to support clinicians' information needsComprehensive temporal information detection from clinical text: medical events, time, and TLINK identification.An automatic system to identify heart disease risk factors in clinical texts over timeDetecting concept mentions in biomedical text using hidden Markov model: multiple concept types at once or one at a time?Automating annotation of information-giving for analysis of clinical conversation.Correlating Lab Test Results in Clinical Notes with Structured Lab Data: A Case Study in HbA1c and Glucose.Automated annotation and classification of BI-RADS assessment from radiology reports.Detection of clinically important colorectal surgical site infection using Bayesian network.Inter-labeler and intra-labeler variability of condition severity classification models using active and passive learning methods.A sequence labeling approach to link medications and their attributes in clinical notes and clinical trial announcements for information extraction.Automatic identification of heart failure diagnostic criteria, using text analysis of clinical notes from electronic health records.Electronic health records-driven phenotyping: challenges, recent advances, and perspectivesElectronic Health Record Phenotypes for Precision Medicine: Perspectives and Caveats From Treatment of Breast Cancer at a Single Institution.Clinical documentation variations and NLP system portability: a case study in asthma birth cohorts across institutions.Analysis of Clinical Variations in Asthma Care Documented in Electronic Health Records Between Staff and Resident Physicians.Detecting Pharmacovigilance Signals Combining Electronic Medical Records With Spontaneous Reports: A Case Study of Conventional Disease-Modifying Antirheumatic Drugs for Rheumatoid ArthritisExtraction of Ejection Fraction from Echocardiography Notes for Constructing a Cohort of Patients having Heart Failure with reduced Ejection Fraction (HFrEF)
P2860
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P2860
Using machine learning for concept extraction on clinical documents from multiple data sources.
description
2011 nî lūn-bûn
@nan
2011 թուականի Յունիսին հրատարակուած գիտական յօդուած
@hyw
2011 թվականի հունիսին հրատարակված գիտական հոդված
@hy
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
name
Using machine learning for con ...... ts from multiple data sources.
@ast
Using machine learning for con ...... ts from multiple data sources.
@en
type
label
Using machine learning for con ...... ts from multiple data sources.
@ast
Using machine learning for con ...... ts from multiple data sources.
@en
prefLabel
Using machine learning for con ...... ts from multiple data sources.
@ast
Using machine learning for con ...... ts from multiple data sources.
@en
P2860
P1476
Using machine learning for con ...... ts from multiple data sources.
@en
P2093
Hongfang Liu
P2860
P304
P356
10.1136/AMIAJNL-2011-000155
P577
2011-06-27T00:00:00Z