A Comparative Study of Supervised Learning as Applied to Acronym Expansion in Clinical Reports

1
A Comparative Study of Supervised Learning as
Applied to Acronym Expansion in Clinical
Reports

• Mahesh Joshi, Serguei Pakhomov,
• Ted Pedersen, Christopher G. Chute
• University of Minnesota, Duluth
• Mayo College of Medicine, Rochester

2
Overview

• Acronyms are ambiguous
• in general, and in more specialized domains
• Acronyms can be disambiguated by expansion
• expansions act as senses or definitions
• Acronym expansion can be viewed as word sense
  disambiguation
• supervised learning from annotated examples
• Features trump learning algorithms
• unigrams dominant

3
AMIA - Top Google Results

• American Medical Informatics Association
• Association of Moving Image Archivists
• Anglican Mission in America
• Associcion Mutual Israelita Argentina

4
RN in Wikipedia

• Registered Nurse
• Royal Navy
• Radio National
• Radio Nederland
• Richard Nixon
• Registered Identification Number
• Renovacion Nacional

5
Acronym Ambiguity not just a problem for General
English

• 33 of Acronyms in UMLS are ambiguous
• Liu et. al. AMIA-2001
• 81 of Acronyms in MEDLINE abstracts are
  ambiguous, with an average of 16 expansions
• Liu et. al. AMIA-2002

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We view AE as WSD

• AE
• sense 1 American Eagle
• sense 2 Arab Emirates
• sense 3 acronym expansion
• WSD
• sense 1 Washington School for the Deaf
• sense 2 web server director
• sense 3 word sense disambiguation

7
Methodology

• Identify 16 ambiguous acronyms
• 9 from Pakhomov, et. al. AMIA-2005
• 7 newly annotated for this this study
• Manually annotate in clinical notes
• 7,738 total instances from Mayo Clinic database
  of clinical notes
• Use as training data for supervised learning

8
Acronyms (majority

AC

Acromioclavicular

Antitussive with Codeine

Acid Controller

10 more

APC

Argon Plasma Coagulation

Adenomatous Polyposis Coli

Atrial Premature Contraction

10 more expansions

• LE
• Limited Exam Lower Extremity
• Initials
• 5 more expansions
• PE
• Pulmonary Embolism
• Pressure Equalizing
• Patient Education
• 12 more expansions

9
Acronyms (50

CP

Chest Pain

Cerebral Palsy

Cerebellopontine

19 more expansions

HD

Huntington's Disease

Hemodialysis

Hospital Day

9 more expansions

CF

Cystic Fibrosis

Cold Formula

Complement Fixation

6 more expansions

• MCI
• Mild Cognitive Impairment
• Methylchloroisothiazolinone
• Microwave Communications, Inc.
• 5 more expansions
• ID
• Infectious Disease
• Identification
• Idaho Identified
• 4 more expansions
• LA
• Long Acting
• Person
• Left Atrium
• 5 more expansions

10
Acronyms (majority 80)

• MI
• Myocardial Infarction
• Michigan
• Unknown
• 2 more expansions
• ACA
• Adenocarcinoma
• Anterior Cerebral Artery
• Anterior Communication Artery
• 3 more expansions
• GE
• Gastroesophageal
• General Exam
• Generose
• General Electric
• HA
• Headache
• Hearing Aid
• Hydroxyapatite

• FEN
• Fluids, Electrolytes and Nutrition
• Drug Fen Phen
• Unknown
• NSR
• Normal Sinus Rhythm
• Nasoseptal Reconstruction
• FEN
• Fluids, Electrolytes and NutritionDrug
• Fen Phen
• Unknown
• NSR
• Normal Sinus Rhythm
• Nasoseptal Reconstruction

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Experimental Objectives

• Compare performance of ML methods
• Naïve Bayesian classifier
• J48/C4.5 Decision Tree Learner
• Support Vector Machine (SMO)
• Compare four different feature sets
• POS tags from Brill-Hepple Tagger
• Unigrams that occur 5 or more times
• flexible window of size 5 around target
• Bigrams that occur 5 or more times
• flexible window of size 5 around target
• Unigrams Bigrams POS Tags

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Feature Extraction

• Horizon up to 5 content words to left and right
  of target
• Boundaries cross sentences, but not clinical
  notes
• Skip stop words
• Bigrams are pairs of contiguous content words
• Example (CF is target)
• Unigrams If she is found to be a carrier, then
  they will follow with CF carrier testing in her
  husband.
• Bigrams If she is found to be a carrier, then
  they will follow with CF carrier testing in her
  husband.

13
Results (majority 14
Results (50 15
Results (majority 80)
16
Results (flexible window)
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Conclusions

• Overall expansion accuracy at or above 90
  regardless of distribution
• Differences in accuracy are largely due to
  features, not ML algorithms
• Addition of bigrams and POS tags helps
  performance, but unigrams dominant
• Flexible window improves upon fixed window
  feature selection

18
Future Work

• Expand all acronyms in a text, not just select
  few
• expand based on prior expansions
• utilize one sense per discourse constraint
• Integrate supervised methods with knowledge based
  approaches and clustering methods to reduce need
  for annotated examples

19
Acknowledgments

• We would like to thank our annotators Barbara
  Abbott, Debra Albrecht and Pauline Funk.
• This work was supported in part by the NLM
  Training Grant (T15 LM07041-19) and the NIH
  Roadmap Multidisciplinary Clinical Research
  Career Development Award (K12/NICHD)-HD49078.
• Dr. Pedersen has been partially supported by a
  National Science Foundation Faculty Early CAREER
  Development Award (0092784).

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Software Resources

• GATE (General Architecture for Text Engineering)
• http//gate.ac.uk/
• NSPGate
• http//nspgate.sourceforge.net/
• Ngram Statistics Package
• http//ngram.sourceforge.net/
• WSDGate
• http//wsdgate.sourceforge.net/
• WEKA (Waikato Environment for Knowledge Analysis)
  
• http//www.cs.waikato.ac.nz/ml/weka/