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Background: While machine learning (ML) algorithms may predict cardiovascular outcomes more accurately than statistical models, their result is usually not representable by a transparent formula. Hence, it is often unclear how specific values of predictors lead to the predictions. We aimed to demonstrate with graphical tools how predictor-risk relations in cardiovascular risk prediction models fitted by ML algorithms and by statistical approaches may differ, and how sample size affects the stability of the estimated relations.Methods: We reanalyzed data from a large registry of 1.5 million participants in a national health screening program. Three data analysts developed analytical strategies to predict cardiovascular events within 1 year from health screening. This was done for the full data set and with gradually reduced sample sizes, and each data analyst followed their favorite modeling approach. Predictor-risk relations were visualized by partial dependence and individual conditional expectation plots.Results: When comparing the modeling algorithms, we found some similarities between these visualizations but also occasional divergence. The smaller the sample size, the more the predictor-risk relation depended on the modeling algorithm used, and also sampling variability played an increased role. Predictive performance was similar if the models were derived on the full data set, whereas smaller sample sizes favored simpler models.Conclusion: Predictor-risk relations from ML models may differ from those obtained by statistical models, even with large sample sizes. Hence, predictors may assume different roles in risk prediction models. As long as sample size is sufficient, predictive accuracy is not largely affected by the choice of algorithm. [ABSTRACT FROM AUTHOR]

Background: Seasonal changes and climatic factors like ambient temperature, sunlight duration and rainfall can influence suicidal behavior. Methods: This study analyses the relationship between seasonal changes and climatic variations and suicide attempts in 2131 young patients in Istanbul, Turkey. Results: In our study sample, there was an association between suicide attempts in youths and seasonal changes, as suicide attempts occurred most frequently during summer in females as well as in males. Furthermore, there was a positive correlation between the mean temperature over the past 10 days and temperature at the index day and suicide attempts in females. After seasonality effects were mathematically removed, the mean temperature 10 days before a suicide attempt remained significant in males only, indicating a possible short-term influence of temperature on suicide attempts. Conclusions: This study shows an association between suicide attempts of young people and climatic changes, in particular temperature changes as well as seasonal changes. Therefore, the influence of seasonal changes and climatic factors on young suicide attempters should get more attention in research to understand the biopsychosocial mechanisms playing a role in suicide attempts of young people. As suicide attempts most frequently occur in young people, further research is of considerable clinical importance. [ABSTRACT FROM AUTHOR]

Background: Controlling a myoelectric prosthesis for upper limbs is increasingly challenging for the user as more electrodes and joints become available. Motion classification based on pattern recognition with a multi-electrode array allows multiple joints to be controlled simultaneously. Previous pattern recognition studies are difficult to compare, because individual research groups use their own data sets. To resolve this shortcoming and to facilitate comparisons, open access data sets were analysed using components of BioPatRec and Netlab pattern recognition models. Methods: Performances of the artificial neural networks, linear models, and training program components were compared. Evaluation took place within the BioPatRec environment, a Matlab-based open source platform that provides feature extraction, processing and motion classification algorithms for prosthetic control. The algorithms were applied to myoelectric signals for individual and simultaneous classification of movements, with the aim of finding the best performing algorithm and network model. Evaluation criteria included classification accuracy and training time. Results: Results in both the linear and the artificial neural network models demonstrated that Netlab's implementation using scaled conjugate training algorithm reached significantly higher accuracies than BioPatRec. Conclusions: It is concluded that the best movement classification performance would be achieved through integrating Netlab training algorithms in the BioPatRec environment so that future prosthesis training can be shortened and control made more reliable. Netlab was therefore included into the newest release of BioPatRec (v4.0). [ABSTRACT FROM AUTHOR]