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Projected to impact 1.6 million people in the UK by 2040 and costing {\pounds}25 billion annually, dementia presents a growing challenge to society. This study, a pioneering effort to predict the translational potential of dementia research using machine learning, hopes to address the slow translation of fundamental discoveries into practical applications despite dementia's significant societal and economic impact. We used the Dimensions database to extract data from 43,091 UK dementia research publications between the years 1990-2023, specifically metadata (authors, publication year etc.), concepts mentioned in the paper, and the paper abstract. To prepare the data for machine learning we applied methods such as one hot encoding and/or word embeddings. We trained a CatBoost Classifier to predict if a publication will be cited in a future patent or clinical trial. We trained several model variations. The model combining metadata, concept, and abstract embeddings yielded the highest performance: for patent predictions, an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.84 and 77.17% accuracy; for clinical trial predictions, an AUROC of 0.81 and 75.11% accuracy. The results demonstrate that integrating machine learning within current research methodologies can uncover overlooked publications, expediting the identification of promising research and potentially transforming dementia research by predicting real-world impact and guiding translational strategies.

We present and test a sequential learning algorithm for the short-term prediction of human mobility. This novel approach pairs the Exponential Weights forecaster with a very large ensemble of experts. The experts are individual sequence prediction algorithms constructed from the mobility traces of 10 million roaming mobile phone users in a European country. Average prediction accuracy is significantly higher than that of individual sequence prediction algorithms, namely constant order Markov models derived from the user's own data, that have been shown to achieve high accuracy in previous studies of human mobility prediction. The algorithm uses only time stamped location data, and accuracy depends on the completeness of the expert ensemble, which should contain redundant records of typical mobility patterns. The proposed algorithm is applicable to the prediction of any sufficiently large dataset of sequences., Comment: 15 pages, 8 figures

In the advent of a pervasive presence of location sharing services researchers gained an unprecedented access to the direct records of human activity in space and time. This paper analyses geo-located Twitter messages in order to uncover global patterns of human mobility. Based on a dataset of almost a billion tweets recorded in 2012 we estimate volumes of international travelers in respect to their country of residence. We examine mobility profiles of different nations looking at the characteristics such as mobility rate, radius of gyration, diversity of destinations and a balance of the inflows and outflows. The temporal patterns disclose the universal seasons of increased international mobility and the peculiar national nature of overseen travels. Our analysis of the community structure of the Twitter mobility network, obtained with the iterative network partitioning, reveals spatially cohesive regions that follow the regional division of the world. Finally, we validate our result with the global tourism statistics and mobility models provided by other authors, and argue that Twitter is a viable source to understand and quantify global mobility patterns., Comment: 17 pages, 13 figures