Combining free text and structured electronic medical record entries to detect acute respiratory infections.
about
Extracting information from the text of electronic medical records to improve case detection: a systematic reviewIdentification of insomnia in a sleep center population using electronic health data sources and the insomnia severity indexUsing Anchors to Estimate Clinical State without Labeled DataUsing electronic medical record data to report laboratory adverse events.Epidemic surveillance using an electronic medical record: an empiric approach to performance improvement.Impact of data fragmentation across healthcare centers on the accuracy of a high-throughput clinical phenotyping algorithm for specifying subjects with type 2 diabetes mellitusExtracting diagnoses and investigation results from unstructured text in electronic health records by semi-supervised machine learning.The absence of longitudinal data limits the accuracy of high-throughput clinical phenotyping for identifying type 2 diabetes mellitus subjectsEmergency Medical Text Classifier: New system improves processing and classification of triage notes.Optimising use of electronic health records to describe the presentation of rheumatoid arthritis in primary care: a strategy for developing code listsISS--an electronic syndromic surveillance system for infectious disease in rural China.Optimising the use of electronic health records to estimate the incidence of rheumatoid arthritis in primary care: what information is hidden in free text?Using the electronic medical record to identify community-acquired pneumonia: toward a replicable automated strategy.Evaluation of smoking status identification using electronic health records and open-text information in a large mental health case register.A sustainable strategy to prevent misuse of antibiotics for acute respiratory infectionsA review of approaches to identifying patient phenotype cohorts using electronic health records.TEPAPA: a novel in silico feature learning pipeline for mining prognostic and associative factors from text-based electronic medical records.The Diagnosis-Wide Landscape of Hospital-Acquired AKI.Decision support during electronic prescription to stem antibiotic overuse for acute respiratory infections: a long-term, quasi-experimental study.Extracting Surveillance Data from Templated Sections of an Electronic Medical Note: Challenges and Opportunities.Computerized Text Analysis to Enhance Automated Pneumonia Detection.Automated Surveillance of Outpatients with Pneumonia: A Performance Evaluation.Can long-term historical data from electronic medical records improve surveillance for epidemics of acute respiratory infections? A systematic evaluation.Development of an automated phenotyping algorithm for hepatorenal syndrome.
P2860
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P2860
Combining free text and structured electronic medical record entries to detect acute respiratory infections.
description
2010 nî lūn-bûn
@nan
2010 թուականի Հոկտեմբերին հրատարակուած գիտական յօդուած
@hyw
2010 թվականի հոտեմբերին հրատարակված գիտական հոդված
@hy
2010年の論文
@ja
2010年論文
@yue
2010年論文
@zh-hant
2010年論文
@zh-hk
2010年論文
@zh-mo
2010年論文
@zh-tw
2010年论文
@wuu
name
Combining free text and struct ...... acute respiratory infections.
@ast
Combining free text and struct ...... acute respiratory infections.
@en
type
label
Combining free text and struct ...... acute respiratory infections.
@ast
Combining free text and struct ...... acute respiratory infections.
@en
prefLabel
Combining free text and struct ...... acute respiratory infections.
@ast
Combining free text and struct ...... acute respiratory infections.
@en
P2093
P2860
P1433
P1476
Combining free text and struct ...... acute respiratory infections.
@en
P2093
Adi Gundlapallli
Brett South
Ericka Kalp
Frank C Curriero
Greg E Glass
Jill A Anthony
Sylvain DeLisle
P2860
P304
P356
10.1371/JOURNAL.PONE.0013377
P407
P577
2010-10-14T00:00:00Z