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- Nunes, A, et al.
(författare)
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Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group
- 2020
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Ingår i: Molecular psychiatry. - : Springer Science and Business Media LLC. - 1476-5578 .- 1359-4184. ; 25:9, s. 2130-2143
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Tidskriftsartikel (refereegranskat)abstract
- Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47–67.00, ROC-AUC = 71.49%, 95% CI = 69.39–73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70–60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen’s Kappa = 0.83, 95% CI = 0.829–0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data.
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- Kircher, Katja, 1973-, et al.
(författare)
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Effects of training on truck drivers interaction with cyclists in a right turn
- 2020
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Ingår i: Cognition, Technology & Work. - : Springer London. - 1435-5558 .- 1435-5566. ; 22:4, s. 745-757
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Tidskriftsartikel (refereegranskat)abstract
- With encounters between trucks and cyclists still being a major safety issue and physical as well as technological improvements far from ubiquitous implementation, training truck drivers in anticipatory driving to improve their interaction with cyclists may be a way forward. After a baseline drive in an urban environment, truck drivers inexperienced with urban driving received a dedicated training on anticipatory driving, followed by another drive along the same route several weeks later. The drivers were also interviewed about their opinion about the training. The drivers behaviour changed from before to after training, resulting in a better speed management in general, and a more intensive monitoring of the cyclists. There were also some improvements with respect to the placement in relation to the cyclist, but this effect was limited mainly because truck drivers performed well already before the training. The observed results correspond well to the opinions and feelings about the training that were reported by the drivers in the interview. Thus, driver training can possibly be one contributor to an increase in safety in urban areas.
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