Literature DB >> 16779051

Detecting possible vaccination reactions in clinical notes.

Brian Hazlehurst1, John Mullooly, Allison Naleway, Brad Crane.   

Abstract

The Vaccine Safety Datalink is a collaboration between the CDC and eight large HMO's to investigate adverse events following immunization through analysis of medical care databases and patients' medical charts. We modified an existing system called MediClass that uses natural language processing (NLP) and knowledge-based methods to classify clinical encounters recorded in electronic medical records (EMRs). We developed the knowledge necessary for MediClass to detect possible vaccine reactions in the outpatient, ED, and telephone encounters recorded in the EMR of a large HMO. We first trained the system using a manually coded gold standard training set, and achieved high sensitivity and specificity. We then ran a large set of post-immunization encounter records through MediClass to see if our method would generalize. Compared to methods that use administrative and clinical codes assigned to the EMR by clinicians, the system significantly improves the positive predictive value for detecting possible vaccine reactions.

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Year:  2005        PMID: 16779051      PMCID: PMC1560600     

Source DB:  PubMed          Journal:  AMIA Annu Symp Proc        ISSN: 1559-4076


  14 in total

1.  Coding neuroradiology reports for the Northern Manhattan Stroke Study: a comparison of natural language processing and manual review.

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Journal:  Comput Biomed Res       Date:  2000-02

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Authors:  M Fiszman; W W Chapman; D Aronsky; R S Evans; P J Haug
Journal:  J Am Med Inform Assoc       Date:  2000 Nov-Dec       Impact factor: 4.497

3.  The HL7 Clinical Document Architecture.

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Journal:  J Am Med Inform Assoc       Date:  2001 Nov-Dec       Impact factor: 4.497

4.  MediClass: A system for detecting and classifying encounter-based clinical events in any electronic medical record.

Authors:  Brian Hazlehurst; H Robert Frost; Dean F Sittig; Victor J Stevens
Journal:  J Am Med Inform Assoc       Date:  2005-05-19       Impact factor: 4.497

5.  Using computerized data to identify adverse drug events in outpatients.

Authors:  B Honigman; J Lee; J Rothschild; P Light; R M Pulling; T Yu; D W Bates
Journal:  J Am Med Inform Assoc       Date:  2001 May-Jun       Impact factor: 4.497

Review 6.  Natural language processing and the representation of clinical data.

Authors:  N Sager; M Lyman; C Bucknall; N Nhan; L J Tick
Journal:  J Am Med Inform Assoc       Date:  1994 Mar-Apr       Impact factor: 4.497

7.  The Vaccine Adverse Event Reporting System (VAERS).

Authors:  R T Chen; S C Rastogi; J R Mullen; S W Hayes; S L Cochi; J A Donlon; S G Wassilak
Journal:  Vaccine       Date:  1994-05       Impact factor: 3.641

8.  Automating a severity score guideline for community-acquired pneumonia employing medical language processing of discharge summaries.

Authors:  C Friedman; C Knirsch; L Shagina; G Hripcsak
Journal:  Proc AMIA Symp       Date:  1999

9.  Identification of suspected tuberculosis patients based on natural language processing of chest radiograph reports.

Authors:  N L Jain; C A Knirsch; C Friedman; G Hripcsak
Journal:  Proc AMIA Annu Fall Symp       Date:  1996

10.  The incident reporting system does not detect adverse drug events: a problem for quality improvement.

Authors:  D J Cullen; D W Bates; S D Small; J B Cooper; A R Nemeskal; L L Leape
Journal:  Jt Comm J Qual Improv       Date:  1995-10
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  6 in total

1.  MediClass: A system for detecting and classifying encounter-based clinical events in any electronic medical record.

Authors:  Brian Hazlehurst; H Robert Frost; Dean F Sittig; Victor J Stevens
Journal:  J Am Med Inform Assoc       Date:  2005-05-19       Impact factor: 4.497

2.  Using electronic medical records to enhance detection and reporting of vaccine adverse events.

Authors:  Virginia L Hinrichsen; Benjamin Kruskal; Megan A O'Brien; Tracy A Lieu; Richard Platt
Journal:  J Am Med Inform Assoc       Date:  2007-08-21       Impact factor: 4.497

Review 3.  Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review.

Authors:  Yuan Luo; William K Thompson; Timothy M Herr; Zexian Zeng; Mark A Berendsen; Siddhartha R Jonnalagadda; Matthew B Carson; Justin Starren
Journal:  Drug Saf       Date:  2017-11       Impact factor: 5.606

Review 4.  What can natural language processing do for clinical decision support?

Authors:  Dina Demner-Fushman; Wendy W Chapman; Clement J McDonald
Journal:  J Biomed Inform       Date:  2009-08-13       Impact factor: 6.317

5.  An evaluation of a natural language processing tool for identifying and encoding allergy information in emergency department clinical notes.

Authors:  Foster R Goss; Joseph M Plasek; Jason J Lau; Diane L Seger; Frank Y Chang; Li Zhou
Journal:  AMIA Annu Symp Proc       Date:  2014-11-14

6.  The Food and Drug Administration Biologics Effectiveness and Safety Initiative Facilitates Detection of Vaccine Administrations From Unstructured Data in Medical Records Through Natural Language Processing.

Authors:  Matthew Deady; Hussein Ezzeldin; Kerry Cook; Douglas Billings; Jeno Pizarro; Amalia A Plotogea; Patrick Saunders-Hastings; Artur Belov; Barbee I Whitaker; Steven A Anderson
Journal:  Front Digit Health       Date:  2021-12-22
  6 in total

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