A supervised named entity recognition for information extraction from medical records
Date Issued
2016
Author(s)
Puflović, Darko
Stanković, Tatjana
Janković, Dragan
Stoimenov, Leonid
Abstract
— Named entity recognition is a widely used task to
extract various kinds of information from unstructured text.
Medical records, produced by hospitals every day contain
huge amount of data about diseases, medications used in
treatment and information about treatment success rate.
There are a large number of systems used in information
retrieval from medical documentation, but they are mostly
used on documents written in English language. This paper
contains the explanation of our approach to solving the
problem of extracting disease and drug names from medical
records written in Serbian language. Our approach uses
statistical language models and can detect up to 80% of
named entities, which is a good result given the very limited
resources for Serbian language, which makes the process of
detection much more difficult.
extract various kinds of information from unstructured text.
Medical records, produced by hospitals every day contain
huge amount of data about diseases, medications used in
treatment and information about treatment success rate.
There are a large number of systems used in information
retrieval from medical documentation, but they are mostly
used on documents written in English language. This paper
contains the explanation of our approach to solving the
problem of extracting disease and drug names from medical
records written in Serbian language. Our approach uses
statistical language models and can detect up to 80% of
named entities, which is a good result given the very limited
resources for Serbian language, which makes the process of
detection much more difficult.
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