2020
DOI: 10.3390/app10082824
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A Natural Language Processing Approach to Automated Highlighting of New Information in Clinical Notes

Abstract: Electronic medical records (EMRs) have been used extensively in most medical institutions for more than a decade in Taiwan. However, information overload associated with rapid accumulation of large amounts of clinical narratives has threatened the effective use of EMRs. This situation is further worsened by the use of “copying and pasting”, leading to lots of redundant information in clinical notes. This study aimed to apply natural language processing techniques to address this problem. New information in lon… Show more

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Cited by 4 publications
(2 citation statements)
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“…One prominent example in this area is analyzing and extracting data from the clinical notes entered by doctors in free form. One recent study utilized the Bigram Language Model for annotating clinical notes to distinguish new information from redundant and improve the performance of medical personnel [12]. Another example of applying NLP in healthcare is extracting adverse drug reactions and interactions from medical texts.…”
Section: Related Workmentioning
confidence: 99%
“…One prominent example in this area is analyzing and extracting data from the clinical notes entered by doctors in free form. One recent study utilized the Bigram Language Model for annotating clinical notes to distinguish new information from redundant and improve the performance of medical personnel [12]. Another example of applying NLP in healthcare is extracting adverse drug reactions and interactions from medical texts.…”
Section: Related Workmentioning
confidence: 99%
“…To convert unstructured data into structured data that is readable, processable, and computable data, researchers use various methods [ 5 , 6 , 7 ]. These methods include a traditional approach via manual reviewing of notes and converting them into the structured data.…”
Section: Introductionmentioning
confidence: 99%