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2010

Section classification in clinical notes using supervised hidden markov model

13 years 1 months ago
Section classification in clinical notes using supervised hidden markov model
As more and more information is available in the Electronic Health Record in the form of free-text narrative, there is a need for automated tools, which can process and understand such texts. One first step towards the automated processing of clinical texts is to determine the document-level structure of a patient note, i.e., identifying the different sections and mapping them to known section types automatically. This paper considers section mapping as a sequence-labeling problem to 15 possible known section types. Our method relies on a Hidden Markov Model (HMM) trained on a corpus of 9,679 clinical notes from NewYork-Presbyterian Hospital. We compare our method to a state-of-the-art baseline, which ignores the sequential aspect of the sections and considers each section independently of the others in a note. Experiments show that our method outperforms the baseline significantly, yielding 93% accuracy in identifying sections individually and 70% accuracy in identifying all the sect...
Ying Li, Sharon Lipsky Gorman, Noemie Elhadad
Added 18 May 2011
Updated 18 May 2011
Type Journal
Year 2010
Where IHI
Authors Ying Li, Sharon Lipsky Gorman, Noemie Elhadad
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