ArticleUpsala journal of medical sciences2020
Natural language processing and machine learning to enable automatic extraction and classification of patients' smoking status from electronic medical records.
Article in Upsala journal of medical sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The validity of electronic health data for measuring smoking status: a systematic review and meta-analysis.BMC medical informatics and decision making · 2024Pooled it
- Improving Clinical Documentation with Artificial Intelligence: A Systematic Review.Perspectives in health information management · 2024Pooled it
- Machine learning used to study risk factors for chronic diseases: A scoping review.Canadian journal of public health = Revue canadienne de sante publique · 2026Article
- Natural Language Processing for Substance Use Disorder Information Extraction: A Systematic Literature Review.Current addiction reports · 2026Review
- Improving social determinants of health documentation in French electronic health records using large language models.Scientific reports · 2025Article
- From manual clinical criteria to machine learning algorithms: Comparing outcome endpoints derived from diverse electronic health record data modalities.PLOS digital health · 2025Article
- An Implemented Real-World-Data Pipeline for Standardization of Electronic Health Records in Precision Oncology.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Identification of patients' smoking status using an explainable AI approach: a Danish electronic health records case study.BMC medical research methodology · 2024Article
- Article
- Machine learning application for predicting smoking cessation among US adults: An analysis of waves 1-3 of the PATH study.PloS one · 2023Article
- Tasks as needs: reframing the paradigm of clinical natural language processing research for real-world decision support.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Data mining to retrieve smoking status from electronic health records in general practiceEuropean heart journal. Digital health · 2022Article
- Artificial intelligence in clinical and translational science: Successes, challenges and opportunities.Clinical and translational science · 2022Review
- Year 2020 (with COVID): Observation of Scientific Literature on Clinical Natural Language Processing.Yearbook of medical informatics · 2021Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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.
methodsData 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.
resultsThe 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% and 0.756 for the rule-based model.
conclusionA model using machine-learning algorithms to automatically classify patients' smoking status was successfully developed. Such algorithms may enable automatic assessment of smoking status and other unstructured data directly from EMRs without manual classification of complete case notes.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.