Literature DB >> 8947694

Automating concept identification in the electronic medical record: an experiment in extracting dosage information.

D A Evans1, N D Brownlow, W R Hersh, E M Campbell.   

Abstract

We discuss the development and evaluation of an automated procedure for extracting drug-dosage information from clinical narratives. The process was developed rapidly using existing technology and resources, including categories of terms from UMLS96. Evaluations over a large training and smaller test set of medical records demonstrate an approximately 80% rate of exact and partial matches' on target phrases, with few false positives and a modest rate of false negatives. The results suggest a strategy for automating general concept identification in electronic medical records.

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Year:  1996        PMID: 8947694      PMCID: PMC2233218     

Source DB:  PubMed          Journal:  Proc AMIA Annu Fall Symp        ISSN: 1091-8280


  2 in total

1.  Computerized extraction of coded findings from free-text radiologic reports. Work in progress.

Authors:  P J Haug; D L Ranum; P R Frederick
Journal:  Radiology       Date:  1990-02       Impact factor: 11.105

2.  A general natural-language text processor for clinical radiology.

Authors:  C Friedman; P O Alderson; J H Austin; J J Cimino; S B Johnson
Journal:  J Am Med Inform Assoc       Date:  1994 Mar-Apr       Impact factor: 4.497

  2 in total
  24 in total

1.  UMLS concept indexing for production databases: a feasibility study.

Authors:  P Nadkarni; R Chen; C Brandt
Journal:  J Am Med Inform Assoc       Date:  2001 Jan-Feb       Impact factor: 4.497

2.  HPARSER: extracting formal patient data from free text history and physical reports using natural language processing software.

Authors:  J L Sponsler
Journal:  Proc AMIA Symp       Date:  2001

3.  Linguistic approach for identification of medication names and related information in clinical narratives.

Authors:  Thierry Hamon; Natalia Grabar
Journal:  J Am Med Inform Assoc       Date:  2010 Sep-Oct       Impact factor: 4.497

4.  Extracting Rx information from clinical narrative.

Authors:  James G Mork; Olivier Bodenreider; Dina Demner-Fushman; Rezarta Islamaj Dogan; François-Michel Lang; Zhiyong Lu; Aurélie Névéol; Lee Peters; Sonya E Shooshan; Alan R Aronson
Journal:  J Am Med Inform Assoc       Date:  2010 Sep-Oct       Impact factor: 4.497

5.  Improving textual medication extraction using combined conditional random fields and rule-based systems.

Authors:  Domonkos Tikk; Illés Solt
Journal:  J Am Med Inform Assoc       Date:  2010 Sep-Oct       Impact factor: 4.497

6.  Extracting medical information from narrative patient records: the case of medication-related information.

Authors:  Louise Deléger; Cyril Grouin; Pierre Zweigenbaum
Journal:  J Am Med Inform Assoc       Date:  2010 Sep-Oct       Impact factor: 4.497

7.  High accuracy information extraction of medication information from clinical notes: 2009 i2b2 medication extraction challenge.

Authors:  Jon Patrick; Min Li
Journal:  J Am Med Inform Assoc       Date:  2010 Sep-Oct       Impact factor: 4.497

8.  Lancet: a high precision medication event extraction system for clinical text.

Authors:  Zuofeng Li; Feifan Liu; Lamont Antieau; Yonggang Cao; Hong Yu
Journal:  J Am Med Inform Assoc       Date:  2010 Sep-Oct       Impact factor: 4.497

9.  Extracting medication information from clinical text.

Authors:  Ozlem Uzuner; Imre Solti; Eithon Cadag
Journal:  J Am Med Inform Assoc       Date:  2010 Sep-Oct       Impact factor: 4.497

10.  Recognizing Medication related Entities in Hospital Discharge Summaries using Support Vector Machine.

Authors:  Son Doan; Hua Xu
Journal:  Proc Int Conf Comput Ling       Date:  2010-08
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