Natural language processing and machine learning to enable automatic extraction and classification of patients' smoking status from electronic medical records.

Upsala Journal of Medical Sciences
Andrea CaccamisiMats Rosenlund

Abstract

The electronic medical record (EMR) offers unique possibilities for clinical research, but some important patient attributes are not readily available due to its unstructured properties. We applied text mining using machine learning to enable automatic classification of unstructured information on smoking status from Swedish EMR data. Data on patients' smoking status from EMRs were used to develop 32 different predictive models that were trained using Weka, changing sentence frequency, classifier type, tokenization, and attribute selection in a database of 85,000 classified sentences. The models were evaluated using F-score and accuracy based on out-of-sample test data including 8500 sentences. The error weight matrix was used to select the best model, assigning a weight to each type of misclassification and applying it to the model confusion matrices. The best performing model was then compared to a rule-based method. The best performing model was based on the Support Vector Machine (SVM) Sequential Minimal Optimization (SMO) classifier using a combination of unigrams and bigrams as tokens. Sentence frequency and attributes selection did not improve model performance. SMO achieved 98.14% accuracy and 0.981 F-score versus 79.32...Continue Reading

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Citations

Aug 29, 2021·Tobacco Control·Rui FuMichael Oliver Chaiton
Sep 4, 2021·Yearbook of Medical Informatics·Natalia GrabarUNKNOWN Section Editors of the IMIA Yearbook Section on Clinical Natural Language Processing
Oct 28, 2021·Clinical and Translational Science·Elmer V BernstamMichael J Becich

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Software Mentioned

Waikato Environment for Knowledge Analysis ( Weka )
SVM SMO
Excel
weka
InfoGainAttributeEval
Notepad

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