Keyword extraction algorithm for classifying smoking status from unstructured bilingual electronic health records based on natural language processing

Ye Seul Bae, Kyung Hwan Kim, Han Kyul Kim, Sae Won Choi, Taehoon Ko, Hee Hwa Seo, Hae Young Lee, Hyojin Jeon

Research output: Contribution to journalArticlepeer-review

Abstract

Smoking is an important variable for clinical research, but there are few studies regarding automatic obtainment of smoking classification from unstructured bilingual electronic health records (EHR). We aim to develop an algorithm to classify smoking status based on unstructured EHRs using natural language processing (NLP). With acronym replacement and Python package Soynlp, we normalize 4711 bilingual clinical notes. Each EHR notes was classified into 4 categories: current smokers, past smokers, never smokers, and unknown. Subsequently, SPPMI (Shifted Positive Point Mutual Information) is used to vectorize words in the notes. By calculating cosine similarity between these word vectors, keywords denoting the same smoking status are identified. Compared to other keyword extraction methods (word co-occurrence-, PMI-, and NPMI-based methods), our proposed approach improves keyword extraction precision by as much as 20.0%. These extracted keywords are used in classifying 4 smoking statuses from our bilingual EHRs. Given an identical SVM classifier, the F1 score is improved by as much as 1.8% compared to those of the unigram and bigram Bag of Words. Our study shows the potential of SPPMI in classifying smoking status from bilingual, unstructured EHRs. Our current findings show how smoking information can be easily acquired for clinical practice and research.

Original languageEnglish
Article number8812
JournalApplied Sciences (Switzerland)
Volume11
Issue number19
DOIs
StatePublished - 1 Oct 2021

Keywords

  • Document classification
  • Electronic health records
  • Lifestyle modification
  • Natural language processing
  • Smoking

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