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Search: WFRF:(Rosenlund L.)

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  • Liese, J. G., et al. (author)
  • Incidence and clinical presentation of acute otitis media in children aged < 6 years in European medical practices
  • 2014
  • In: Epidemiology and Infection. - 0950-2688 .- 1469-4409. ; 142:8, s. 1778-1788
  • Journal article (peer-reviewed)abstract
    • We conducted an epidemiological, observational cohort study to determine the incidence and complications of acute otitis media (AOM) in children aged <6 years. Data on physician-diagnosed AOM were collected from retrospective review of medical charts for the year preceding enrolment and then prospectively in the year following enrolment. The study included 5776 children in Germany, Italy, Spain, Sweden, and the UK. AOM incidence was 256/1000 person-years [95% confidence interval (CI) 243-270] in the prospective study period. Incidence was lowest in Italy (195, 95% CI 171-222) and highest in Spain (328, 95% CI 296-363). Complications were documented in < 1% of episodes. Spontaneous tympanic membrane perforation was documented in 7% of episodes. Both retrospective and prospective study results were similar and show the high incidence during childhood in these five European countries. Differences by country may reflect true differences and differences in social structure and diagnostic procedures.
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  • Caccamisi, Andrea, et al. (author)
  • PRM92 - Automatic Extraction and Classification of Patients’ Smoking Status from Free Text Using Natural Language Processing
  • 2016
  • In: Value in Health. - : Elsevier BV. - 1098-3015 .- 1524-4733. ; 19:7
  • Journal article (peer-reviewed)abstract
    • ObjectivesTo develop a machine learning algorithm for automatic classification of smoking status (smoker, ex-smoker, non-smoker and unknown status) in EMRs, and validate the predictive accuracy compared to a rule-based method. Smoking is a leading cause of death worldwide and may introduce confounding in research based on real world data (RWD). Information on smoking is often documented in free text fields in Electronic Medical Records (EMRs), but structured RWD on smoking is sparse.Methods32 predictive models were trained with the Weka machine learning suite, tweaking sentence frequency, classifier type, tokenization and attribute selection using a database of 85,000 classified sentences. The models were evaluated using F-Score and Accuracy based on out-of-sample test data including 8,500 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 models confusion matrices.ResultsThe best performing model was based on the Support Vector Machine (SVM) Sequential Minimal Optimization (SMO) classifier using a polynomial kernel with parameter C equal to 6 and a combination of unigrams and bigrams as tokens. Sentence frequency and attributes selection did not improve model performance. SMO achieved 98.25% accuracy and 0.982 F-Score versus 79.32% and 0.756, respectively, for the rule-based model.ConclusionsA model using machine learning algorithms to automatically classify patients smoking status was successfully developed. This algorithm would enable automatic assessment of smoking status directly from EMRs, obviating the need to extract complete case notes and manual classification.
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