PMID- 23703825
OWN - NLM
STAT- MEDLINE
DCOM- 20131217
LR  - 20211021
IS  - 1527-974X (Electronic)
IS  - 1067-5027 (Print)
IS  - 1067-5027 (Linking)
VI  - 20
IP  - 5
DP  - 2013 Sep-Oct
TI  - Dictionary construction and identification of possible adverse drug events in 
      Danish clinical narrative text.
PG  - 947-53
LID - 10.1136/amiajnl-2013-001708 [doi]
AB  - OBJECTIVE: Drugs have tremendous potential to cure and relieve disease, but the 
      risk of unintended effects is always present. Healthcare providers increasingly 
      record data in electronic patient records (EPRs), in which we aim to identify 
      possible adverse events (AEs) and, specifically, possible adverse drug events 
      (ADEs). MATERIALS AND METHODS: Based on the undesirable effects section from the 
      summary of product characteristics (SPC) of 7446 drugs, we have built a Danish 
      ADE dictionary. Starting from this dictionary we have developed a pipeline for 
      identifying possible ADEs in unstructured clinical narrative text. We use a named 
      entity recognition (NER) tagger to identify dictionary matches in the text and 
      post-coordination rules to construct ADE compound terms. Finally, we apply 
      post-processing rules and filters to handle, for example, negations and sentences 
      about subjects other than the patient. Moreover, this method allows synonyms to 
      be identified and anatomical location descriptions can be merged to allow 
      appropriate grouping of effects in the same location. RESULTS: The method 
      identified 1 970 731 (35 477 unique) possible ADEs in a large corpus of 6011 
      psychiatric hospital patient records. Validation was performed through manual 
      inspection of possible ADEs, resulting in precision of 89% and recall of 75%. 
      DISCUSSION: The presented dictionary-building method could be used to construct 
      other ADE dictionaries. The complication of compound words in Germanic languages 
      was addressed. Additionally, the synonym and anatomical location collapse improve 
      the method. CONCLUSIONS: The developed dictionary and method can be used to 
      identify possible ADEs in Danish clinical narratives.
FAU - Eriksson, Robert
AU  - Eriksson R
AD  - Department of Disease Systems Biology, Faculty of Health and Medical Sciences, 
      NNF Center for Protein Research, University of Copenhagen, Copenhagen, Denmark.
FAU - Jensen, Peter Bjodstrup
AU  - Jensen PB
FAU - Frankild, Sune
AU  - Frankild S
FAU - Jensen, Lars Juhl
AU  - Jensen LJ
FAU - Brunak, Soren
AU  - Brunak S
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20130523
PL  - England
TA  - J Am Med Inform Assoc
JT  - Journal of the American Medical Informatics Association : JAMIA
JID - 9430800
SB  - IM
MH  - Data Mining/*methods
MH  - Denmark
MH  - *Dictionaries, Medical as Topic
MH  - *Drug-Related Side Effects and Adverse Reactions
MH  - *Electronic Health Records
MH  - Humans
MH  - Narration
PMC - PMC3756275
OTO - NOTNLM
OT  - Adverse Drug Event
OT  - Adverse Drug Reaction Reporting Systems
OT  - Data Mining
OT  - Dictionary
OT  - Electronic Health Records
EDAT- 2013/05/25 06:00
MHDA- 2013/12/18 06:00
PMCR- 2013/05/23
CRDT- 2013/05/25 06:00
PHST- 2013/05/25 06:00 [entrez]
PHST- 2013/05/25 06:00 [pubmed]
PHST- 2013/12/18 06:00 [medline]
PHST- 2013/05/23 00:00 [pmc-release]
AID - amiajnl-2013-001708 [pii]
AID - 10.1136/amiajnl-2013-001708 [doi]
PST - ppublish
SO  - J Am Med Inform Assoc. 2013 Sep-Oct;20(5):947-53. doi: 
      10.1136/amiajnl-2013-001708. Epub 2013 May 23.