=Paper=
{{Paper
|id=Vol-1178/CLEF2012wn-CLEFeHealth-IseniusEt2012
|storemode=property
|title=Initial Results in the Development of SCAN A Swedish Clinical Abbreviation Normalizer
|pdfUrl=https://ceur-ws.org/Vol-1178/CLEF2012wn-CLEFeHealth-IseniusEt2012.pdf
|volume=Vol-1178
}}
==Initial Results in the Development of SCAN A Swedish Clinical Abbreviation Normalizer==
https://ceur-ws.org/Vol-1178/CLEF2012wn-CLEFeHealth-IseniusEt2012.pdf
Initial Results in the Development of SCAN
A Swedish Clinical Abbreviation Normalizer
Niklas Isenius1, Sumithra Velupillai1, and Maria Kvist1, 2
1
Dept. of Computer & Systems Sciences, Stockholm University, Sweden
2
Dept. of Clinical Immunology & Transfusion Medicine, Karolinska University Hospital,
Sweden
[nikl-ise, sumithra]@dsv.su.se, maria.kvist@karolinska.se
Abstract. Abbreviations are common in clinical documentation, as this type of
text is written under time-pressure and serves mostly for internal communica-
tion. This study attempts to apply and extend existing rule-based algorithms that
have been developed for English and Swedish abbreviation detection, in order
to create an abbreviation detection algorithm for Swedish clinical texts that can
identify and suggest definitions for abbreviations and acronyms. This can be
used as a pre-processing step for further information extraction and text mining
models, as well as for readability solutions.
Through a literature review, a number of heuristics were defined for automat-
ic abbreviation detection. These were used in the construction of the Swedish
Clinical Abbreviation Normalizer (SCAN). The heuristics were: a) freely avail-
able external resources: a dictionary of general Swedish, a dictionary of medical
terms and a dictionary of known Swedish medical abbreviations, b) maximum
word lengths (from three to eight characters), and c) heuristics for handling
common patterns such as hyphenation. For each token in the text, the algorithm
checks whether it is a known word in one of the lexicons, and whether it fulfills
the criteria for word length and the created heuristics. The final algorithm was
evaluated on a set of 300 Swedish clinical notes from an emergency department
at the Karolinska University Hospital, Stockholm. These notes were annotated
for abbreviations, a total of 2,050 tokens. This set was annotated by a physician
accustomed to reading and writing medical records.
The algorithm was tested in different variants, where the word lists were
modified, heuristics adapted to characteristics found in the texts, and different
combinations of word lengths. The best performing version of the algorithm
achieved an F-Measure score of 79%, with 76% recall and 81% precision,
which is a considerable improvement over the baseline where each token was
only matched against the word lists (51% F-measure, 87% recall, 36% preci-
sion). Not surprisingly, precision results are higher when the maximum word
length is set to the lowest (three), and recall results higher when it is set to the
highest (eight).
Algorithms for rule-based systems, mainly developed for English, can be
successfully adapted for abbreviation detection in Swedish medical records.
System performance relies heavily on the quality of the external resources, as
well as on the created heuristics. In order to improve results, part-of-speech in-
Cross-Language Evaluation of Methods, Applications, and Resources for eHealth Document Analysis -
CLEFeHealth 2012 Workshop, edited by Hanna Suominen
© CLEF 2012
formation and/or local context is needed for disambiguation. In the case of
Swedish, compounding also needs to be handled.
Keywords: Automatic Abbreviation Detection; Medical Records; Clinical Text
Mining
1 Introduction
Abbreviations are common in clinical documentation, as this type of text is written
under time-pressure and serves mostly for internal communication. For text mining
and information extraction techniques, as well as for readability for e.g. patients and
caregivers from other specialties, it would be beneficial to automatically identify and
expand abbreviations to their full-form counterparts. However, most research on ab-
breviation detection and expansion has been performed on English texts. This study
attempts to apply and extend existing rule-based algorithms that have been developed
for English and Swedisha abbreviation detection,1-5 in order to create an abbreviation
detection algorithm for Swedish clinical texts that can identify and suggest definitions
for abbreviations and acronyms. This can be used as a pre-processing step for further
information extraction and text mining models, as well as for readability solutions.
2 Materials and Methods
Through a literature review, a number of heuristics were defined for automatic abbre-
viationb detection and were used for the construction of the Swedish Clinical Abbre-
viation Normalizer (SCAN). These heuristics were:
1. freely available external resources:
─ a dictionary of general Swedish,c
─ a dictionary of medical terms,d and
─ a dictionary of known Swedish medical abbreviations,6
2. maximum word lengths (from three to eight characters), and
3. heuristics for handling common patterns such as hyphenation.
For each token in the text, the algorithm checks whether it is a known word in one of
the lexicons, and whether it fulfils the criteria for word length and the created heuris-
tics. The final algorithm was evaluated on a set of 300 Swedish clinical notes from an
emergency department at the Karolinska University Hospital, Stockholm. e These
notes were annotated for abbreviations, resulting in a total of 2,050. This set was an-
notated by a physician accustomed to reading and writing medical records.
a
This study is applied on Swedish biomedical texts, not on clinical documentation.
b
In the study presented here, acronyms are included in the definition of abbreviations.
c
http://runeberg.org/words/ and http://g3.spraakdata.gu.se/saob/
d
http://www.fass.se
e
Ethical approval is granted by the Regional Ethical Review Board in Stockholm
(Etikprövningsnämnden I Stockholm), permission number 2009/1742-31/5
3 Results
The algorithm was tested in different variants, where the word lists were modified,
heuristics adapted to characteristics found in the texts, and different combinations of
word lengths. The best performing version of the algorithm achieved an F-Measure
score of 79%, with 76% recall and 81% precision, which is a considerable improve-
ment over the baseline where each token was only matched against the word lists
(51% F-measure, 87% recall, 36% precision), see Table 1. Not surprisingly, precision
results are higher when the maximum word length is set to the lowest (three), and
recall results higher when it is set to the highest (eight).
Table 1. SCAN results, precision, recall and F-measure. Baseline = each token is checked
against the word lists. ImprovedListLength4 = token length 4 and modified word lists.
Version Recall % Precision % F-measure %
Baseline 87 36 51
ImprovedListLength3 68 82 75
ImprovedListLength4 76 81 79
ImprovedListLength8 83 62 71
4 Discussion
In this study, a rule-based abbreviation system tailored for Swedish medical records is
presented. The system relies on lexicons and heuristics and the overall results are
encouraging. Through an error analysis, different types of common errors have been
identified, such as ambiguous words (e.g. hö which is a valid word (hay) but also a
common abbreviation for höger (right)) and abbreviations within compounds, e.g.
lungrtg (x-ray of lungs, x-ray abbreviated rtg). Compared to similar research for Eng-
lish (e.g. Xu et al.1), results are lower. Relying on word lengths and external lexicons
limits coverage. In order to improve results, part-of-speech information and/or local
context is needed for disambiguation, for instance, which would probably improve
precision results. In the case of Swedish, compounding also needs to be handled.
5 Conclusion
Algorithms for rule-based systems, mainly developed for English, can be successfully
adapted for abbreviation detection in Swedish medical records. System performance
relies heavily on the quality of the external resources, as well as on the created heuris-
tics. However, promising results are obtained without extensive tailoring of previous
algorithms developed for other languages. In the case of Swedish, language-specific
properties need to be addressed. Future work involves expanding the abbreviations to
their definitions, where emphasis should be put on improving precision rather than
recall, as precision would be more important for this task (an erroneous expansion
would create unfortunate misunderstandings). In the current system definitions are
provided if they exist in the list of known medical abbreviations but no evaluation has
yet been performed on this part. Detecting abbreviations automatically in medical
records has great importance for information access from this type of text. In one
study, extraction of disorders were hampered by abbreviations, as 14% of disorders in
clinical text were written as abbreviations and was not found when matched to
SNOMED CT7. If these could be correctly expanded to their full-length counterpart,
automatic disorder extraction would of course also improve. With rule-based algo-
rithms, very little language specific tailoring is needed, at least in the case of English
versus Swedish. However, for better performance, some issues need to be handled,
e.g. compound splitting and disambiguation.
Acknowledgements
The authors wish to thank the anonymous and known reviewers for their comments
and feedback.
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