Evaluating natural language processors in the clinical domain.
about
Chapter 1: Biomedical knowledge integrationPreparing an annotated gold standard corpus to share with extramural investigators for de-identification researchIndexFinder: a method of extracting key concepts from clinical texts for indexingEmpirical data for the semantic interpretation of prepositional phrases in medical documents.Generating a reliable reference standard set for syndromic case classification.Machine learning and radiology.Automated extraction and normalization of findings from cancer-related free-text radiology reports.A study of biomedical concept identification: MetaMap vs. people.Mining FDA drug labels for medical conditionsComparing natural language processing tools to extract medical problems from narrative text.Structured representation of the pharmacodynamics section of the summary of product characteristics for antibiotics: application for automated extraction and visualization of their antimicrobial activity spectraOpen Source Clinical NLP - More than Any Single System.Conceptual knowledge acquisition in biomedicine: A methodological review.Large-scale evaluation of automated clinical note de-identification and its impact on information extraction.The age-phenome databaseComputational semantics in clinical text.Building an automated problem list based on natural language processing: lessons learned in the early phase of development.Building and evaluation of a structured representation of pharmacokinetics information presented in SPCs: from existing conceptual views of pharmacokinetics associated with natural language processing to object-oriented design.MedSynDiKATe--design considerations for an ontology-based medical text understanding systemGeneration and evaluation of intraoperative inferences for automated health care briefings on patient status after bypass surgery.A study of communication in the Cardiac Surgery Intensive Care Unit and its implications for automated briefing.Developing and evaluating an automated appendicitis risk stratification algorithm for pediatric patients in the emergency department.CogStack - experiences of deploying integrated information retrieval and extraction services in a large National Health Service Foundation Trust hospital.
P2860
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P2860
Evaluating natural language processors in the clinical domain.
description
1998 nî lūn-bûn
@nan
1998年の論文
@ja
1998年学术文章
@wuu
1998年学术文章
@zh
1998年学术文章
@zh-cn
1998年学术文章
@zh-hans
1998年学术文章
@zh-my
1998年学术文章
@zh-sg
1998年學術文章
@yue
1998年學術文章
@zh-hant
name
Evaluating natural language processors in the clinical domain.
@en
Evaluating natural language processors in the clinical domain.
@nl
type
label
Evaluating natural language processors in the clinical domain.
@en
Evaluating natural language processors in the clinical domain.
@nl
prefLabel
Evaluating natural language processors in the clinical domain.
@en
Evaluating natural language processors in the clinical domain.
@nl
P1476
Evaluating natural language processors in the clinical domain
@en
P2093
C Friedman
G Hripcsak
P304
P577
1998-11-01T00:00:00Z