Background Research investigating treatments and interventions for cognitive decline fail due to difficulties in accurately recognizing behavioral signatures in the presymptomatic stages of the disease. For this validation study, we took our previously constructed digital biomarker‐based prognostic models and focused on generalizability and robustness of the models. Method We validated prognostic models characterizing subjects using digital biomarkers in a longitudinal, multi‐site, 40‐month prospective study collecting data in memory clinics, general practitioner offices, and home environments. Results Our models were able to accurately discriminate between healthy subjects and individuals at risk to progress to dementia within 3 years. The model was also able to differentiate between people with or without amyloid neuropathology and classify fast and slow cognitive decliners with a very good diagnostic performance. Conclusion Digital biomarker prognostic models can be a useful tool to assist large‐scale population screening for the early detection of cognitive impairment and patient monitoring over time.

This study examines the clinical decision support systems in healthcare, in particular about the prevention, diagnosis and treatment of respiratory diseases, such as Asthma and chronic obstructive pulmonary disease. The empirical pulmonology study of a representative sample (n = 132) attempts to identify the major factors that contribute to the diagnosis of these diseases. Machine learning results show that in chronic obstructive pulmonary disease's case, Random Forest classifier outperforms other techniques with 97.7 per cent precision, while the most prominent attributes for diagnosis are smoking, forced expiratory volume 1, age and forced vital capacity. In asthma's case, the best precision, 80.3 per cent, is achieved again with the Random Forest classifier, while the most prominent attribute is MEF2575.

Educational webcasts have enhanced the value of training procedures in institutions and business organizations. In this study, variables from the Unified Theory of Acceptance and Use of Technology, Social Cognitive Theory and Theory of Planed Behavior were chosen as important predictors of e-Learning tools acceptance. This hybrid framework aims to verify the effect of the selected key variables on webcast acceptance. Moreover, the effect of experience on learners' intention to adopt webcasts is explored, as well as the moderating effect of experience on the relationships between the selected key variables and the intention to adopt webcasts. Responses from 248 webcast-based learners were used to examine webcasts' adoption and the differences between learners with low and high levels of webcast usage experience. Results confirmed the effects of the key variables and experience on learners' intention to use webcasts and indicated the moderating effect of learners' experience on the relationships between (1) Perceived Behavioral Control and Behavioral Intention and (2) Social Norm and Behavioral Intention. The overall outcomes are expected to contribute to theoretical development and promote the acceptance of educational webcasts.

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