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- Liese, J. G., et al.
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Incidence and clinical presentation of acute otitis media in children aged < 6 years in European medical practices
- 2014
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In: Epidemiology and Infection. - 0950-2688 .- 1469-4409. ; 142:8, s. 1778-1788
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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)
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PRM92 - Automatic Extraction and Classification of Patients’ Smoking Status from Free Text Using Natural Language Processing
- 2016
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In: Value in Health. - : Elsevier BV. - 1098-3015 .- 1524-4733. ; 19:7
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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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- Ekman, S., et al.
(author)
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Treatment (Tx) patterns and overall survival (OS) in patients (pts) with NSCLC in Sweden : A SCAN-LEAF study analysis from the I-O Optimise initiative
- 2019
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In: Annals of Oncology. - : Elsevier BV. - 0923-7534. ; 30:Suppl 2, s. 17-17
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Conference paper (peer-reviewed)abstract
- Background: As part of I-O Optimise, a multinational research platform providing real-world insights into the management of lung cancers, the SCAN-LEAF study aims to describe the epidemiology, clinical care and outcomes for pts with NSCLC in Scandinavia. We report initial Tx and OS for pts with NSCLC prior to the availability of immunotherapies in Sweden. Methods: The analysis includes all adult pts diagnosed with NSCLC at Uppsala and Karolinska (Stockholm) University Hospitals from 2012 to 2015 (follow-up to Dec 2016). Electronic medical record data were extracted using Pygargus CXP software and linked with national registries. Bespoke rule-based algorithms were applied to describe Tx patterns; Kaplan–Meier methods were used to estimate OS. Results: 2779 pts were diagnosed with incident NSCLC (median age, 70 yrs [range: 22–96; 14.2% ≥80]; male, 48.5%; histology: non-squamous (NSQ), 70.9%, squamous (SQ), 17.7%, other, 11.4%; stage distribution: I, 19.3%; II, 7.7%; IIIA, 12.3%; IIIB, 7.2%; IV, 51.2%). Initial Tx (≤6 months from diagnosis) by stage and yr of diagnosis is shown in the table. Median OS (months) for NSQ and SQ pts: not reached and 52.8 in stage I, 43.2 and 23.6 in stage II, 26.7 and 20.4 in stage IIIA, 12.5 and 12.9 in stage IIIB, and 7.6 and 6.1 in stage IV, respectively. Among stage IIIB–IV pts, 60.7% (NSQ) and 53.5% (SQ) had ≥1 line of systemic anti-cancer therapy (SACT); median OS was 12.2 (NSQ) and 10.4 (SQ) months in pts on SACT, and 3.1 (NSQ) and 3.7 (SQ) months in pts not on SACT. Ongoing analyses will assess factors associated with SACT receipt in stage IIIB–IV pts. Conclusions: Swedish pts with NSCLC had a high burden of disease, with most diagnosed at stage IV and a median OS of 1 yr in late-stage pts receiving SACT. There is also scope for improved prognosis in pts diagnosed at early stages, particularly in SQ pts. Future analyses will assess the potential impact of recent improvements in diagnostics and therapeutics on Tx patterns and OS in Swedish NSCLC pts.
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- Moruz, Luminita, 1982-, et al.
(author)
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Mass Fingerprinting of Complex Mixtures : Protein Inference from High-Resolution Peptide Masses and Predicted Retention Times
- 2013
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In: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 12:12, s. 5730-5741
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Journal article (peer-reviewed)abstract
- In typical shotgun experiments, the mass spectrometer records the masses of a large set of ionized analytes but fragments only a fraction of them. In the subsequent analyses, normally only the fragmented ions are used to compile a set of peptide identifications, while the unfragmented ones are disregarded. In this work, we show how the unfragmented ions, here denoted MS1-features, can be used to increase the confidence of the proteins identified in shotgun experiments. Specifically, we propose the usage of in silico mass tags, where the observed MS1-features are matched against de novo predicted masses and retention times for all peptides derived from a sequence database. We present a statistical model to assign protein-level probabilities based on the MS1-features and combine this data with the fragmentation spectra. Our approach was evaluated for two triplicate data sets from yeast and human, respectively, leading to up to 7% more protein identifications at a fixed protein-level false discovery rate of 1%. The additional protein identifications were validated both in the context of the mass spectrometry data and by examining their estimated transcript levels generated using RNA-Seq. The proposed method is reproducible, straightforward to apply, and can even be used to reanalyze and increase the yield of existing data sets.
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- Sorensen, JB, et al.
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Initial treatment and survival in Danish patients diagnosed with non-small-cell lung cancer (2005-2015): SCAN-LEAF study
- 2021
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In: Future oncology (London, England). - : Future Medicine Ltd. - 1744-8301 .- 1479-6694. ; 18:2, s. 205-214
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Journal article (peer-reviewed)abstract
- Aim: To describe initial treatment patterns and survival of patients diagnosed with non-small-cell lung cancer (NSCLC) in Denmark, before immune checkpoint inhibitor and later-generation tyrosine kinase inhibitor use. Patients & methods: Adults diagnosed with incident NSCLC (2005–2015; follow-up: 2016). Initial treatments and overall survival (OS) are reported. Results: 31,939 NSCLC patients (51.6% stage IV) were included. Increasing use of curative radiotherapy/chemoradiation for stage I, II/IIIA and IIIB NSCLC coincided with improved 2-year OS. Systemic anticancer therapy use increased for patients with stage IV non-squamous NSCLC (53.0–60.6%) but not squamous NSCLC (44.9–47.3%). 1-year OS improved in patients with stage IV non-squamous NSCLC (23–31%) but not squamous NSCLC (22–25%). Conclusion: Trends indicated improved OS as treatments evolved between 2005 and 2015, but the effect was limited to 1-year OS in stage IV disease.
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