BACKGROUND
Smoking is a major risk factor and important variable for clinical research, but there are few studies regarding automatic obtainment of smoking classification from unstructured bilingual electronic health records (EHR).
OBJECTIVE
We aim to develop an algorithm to classify smoking status based on unstructured EHRs using natural language processing (NLP).
METHODS
With acronym replacement and Python package Soynlp, we normalize 4,711 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.
RESULTS
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 clinical notes. Given an identical SVM classifier, the extracted keywords improve the F1 score by as much as 1.8% compared to those of the unigram and bigram Bag of Words.
CONCLUSIONS
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 and used for clinical practice and research.
Data mining information from electronic health records produced high yield and accuracy for current smoking status
