Comparison of MetaMap and cTAKES for entity extraction in clinical notes

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Comparison of MetaMap and cTAKES for entity extraction in clinical notes

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wetenschappelijk artikel

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наукова стаття, опублікована у вересні 2018

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Comparison of MetaMap and cTAKES for entity extraction in clinical notes

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Comparison of MetaMap and cTAKES for entity extraction in clinical notes

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Comparison of MetaMap and cTAKES for entity extraction in clinical notes

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Comparison of MetaMap and cTAKES for entity extraction in clinical notes

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Comparison of MetaMap and cTAKES for entity extraction in clinical notes

@en

Comparison of MetaMap and cTAKES for entity extraction in clinical notes

@nl

P1476

Comparison of MetaMap and cTAKES for entity extraction in clinical notes

@en

P2093

Ruth Reátegui

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Sylvie Ratté

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P2860

Using Electronic Patient Records to Discover Disease Correlations and Stratify Patient Cohorts

Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program

Comparison and evaluation of pathway-level aggregation methods of gene expression data.

Recognizing obesity and comorbidities in sparse data.

Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications.

An overview of MetaMap: historical perspective and recent advances

Evaluating the state of the art in disorder recognition and normalization of the clinical narrative.

Using text mining techniques to extract phenotypic information from the PhenoCHF corpus

Combining rules and machine learning for extraction of temporal expressions and events from clinical narratives.

Improving the dictionary lookup approach for disease normalization using enhanced dictionary and query expansion.

P2888

s12911-018-0654-2

P304

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P31

scholarly article

P356

10.1186/S12911-018-0654-2

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P407

English

P433

Suppl 3

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P478

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P577

2018-09-14T00:00:00Z

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P6179

1106989845

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P698

30255810

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P921

health policy

P932

6157281

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