Patient clustering with uncoded text in electronic medical records

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Leveraging Food and Drug Administration Adverse Event Reports for the Automated Monitoring of Electronic Health Records in a Pediatric Hospital

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Patient clustering with uncoded text in electronic medical records

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2013 nî lūn-bûn

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2013年の論文

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Patient clustering with uncoded text in electronic medical records

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Patient clustering with uncoded text in electronic medical records

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Patient clustering with uncoded text in electronic medical records

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Patient clustering with uncoded text in electronic medical records

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Geoffrey Ginsburg

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Jared Murray

http://www.w3.org/2001/XMLSchema#string

Joseph E Lucas

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P2860

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

Computer-assisted diagnostic decision support: history, challenges, and possible paths forward.

Antiandrogens in the treatment of priapism.

Detecting adverse events using information technology

A broad-coverage natural language processing system.

Outbreak detection through automated surveillance: a review of the determinants of detection.

Detection of pharmacovigilance-related adverse events using electronic health records and automated methods.

Use abstracted patient-specific features to assist an information-theoretic measurement to assess similarity between medical cases

Comparing ICD9-encoded diagnoses and NLP-processed discharge summaries for clinical trials pre-screening: a case study.

Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: a feasibility study.

P304

592-599

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scholarly article

P478

2013

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Ricardo Henao

Lawrence Carin

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2013-11-16T00:00:00Z

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24551361

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3900202

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Patient clustering with uncoded text in electronic medical records

about

P2860

P2860

Patient clustering with uncoded text in electronic medical records

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type

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prefLabel

P1433

P1476

P2093

P2860

P304

P31

P478

P50

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P698

P932

P1433

P1476

P2093

P2860

P304

P31

P478

P50

P577

P698

P932