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

Journal: Upsala journal of medical sciences

PMID: 32696698

Abstract

BACKGROUND: 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.

Authors

Andrea Caccamisi

Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, Stockholm, Sweden.
Leif Jørgensen

IQVIA Solutions Sweden AB, Solna, Sweden.
Hercules Dalianis

Department of Computer and Systems Sciences, (DSV), Stockholm University, Sweden.
Mats Rosenlund

Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, Stockholm, Sweden.

Keywords

Algorithms Automation Bayes Theorem Data Mining Electronic Health Records False Positive Reactions Humans Machine Learning Medical Informatics Natural Language Processing Observer Variation Pattern Recognition, Automated Reproducibility of Results Research Design ROC Curve Smoking Software Support Vector Machine Sweden Tobacco Use Disorder

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View on PubMed Access via DOI PubMed (32696698)

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

Abstract

Authors

Keywords

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