scholarly journals Natural language processing tool for automatic diseases and drugs recognition from electronic health records in polish- pilot study

2021 ◽  
Vol 42 (Supplement_1) ◽  
Author(s):  
C M Maciejewski ◽  
M K Krajsman ◽  
K O Ozieranski ◽  
M B Basza ◽  
M G Gawalko ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Electronic Health Records ◽  
Natural Language ◽  
Language Processing ◽  
Structured Data ◽  
Health Records ◽  
Funding Sources ◽  
Natural Language Processing Tool ◽  
Electronic Health ◽  
Automatic Tool

Abstract Background An estimate of 80% of data gathered in electronic health records is unstructured, textual information that cannot be utilized for research purposes until it is manually coded into a database. Manual coding is a both cost and time- consuming process. Natural language processing (NLP) techniques may be utilized for extraction of structured data from text. However, little is known about the accuracy of data obtained through these methods. Purpose To evaluate the possibility of employing NLP techniques in order to obtain data regarding risk factors needed for CHA2DS2VASc scale calculation and detection of antithrombotic medication prescribed in the population of atrial fibrillation (AF) patients of a cardiology ward. Methods An automatic tool for diseases and drugs recognition based on regular expressions rules was designed through cooperation of physicians and IT specialists. Records of 194 AF patients discharged from a cardiology ward were manually reviewed by a physician- annotator as a comparator for the automatic approach. Results Median CHA2DS2VASc score calculated by the automatic was 3 (IQR 2–4) versus 3 points (IQR 2–4) for the manual method (p=0.66). High agreement between CHA2DS2VASc scores calculated by both methods was present (Kendall's W=0.979; p<0.001). In terms of anticoagulant recognition, the automatic tool misqualified the drug prescribed in 4 cases. Conclusion NLP-based techniques are a promising tools for obtaining structured data for research purposes from electronic health records in polish. Tight cooperation of physicians and IT specialists is crucial for establishing accurate recognition patterns. Funding Acknowledgement Type of funding sources: None.

Development of algorithm for classification smoking status from unstructured bilingual electronic health records based on natural language processing (Preprint)

2021 ◽  
Author(s):  
Ye Seul Bae ◽  
Kyung Hwan Kim ◽  
Han Kyul Kim ◽  
Sae Won Choi ◽  
Taehoon Ko ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Electronic Health Records ◽  
Natural Language ◽  
Language Processing ◽  
Smoking Status ◽  
Svm Classifier ◽  
Keyword Extraction ◽  
Health Records ◽  
Clinical Notes ◽  
Electronic Health

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.

scholarly journals Keyword Extraction Algorithm for Classifying Smoking Status from Unstructured Bilingual Electronic Health Records Based on Natural Language Processing

2021 ◽  
Vol 11 (19) ◽  
pp. 8812
Author(s):  
Ye Seul Bae ◽  
Kyung Hwan Kim ◽  
Han Kyul Kim ◽  
Sae Won Choi ◽  
Taehoon Ko ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Electronic Health Records ◽  
Natural Language ◽  
Language Processing ◽  
Smoking Status ◽  
Extraction Methods ◽  
Svm Classifier ◽  
Keyword Extraction ◽  
Health Records ◽  
Electronic Health

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.

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