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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 ofdementia research using machine learning, hopes to address the slow translationof fundamental discoveries into practical applications despite dementia'ssignificant societal and economic impact. We used the Dimensions database toextract data from 43,091 UK dementia research publications between the years1990-2023, specifically metadata (authors, publication year etc.), conceptsmentioned in the paper, and the paper abstract. To prepare the data for machinelearning we applied methods such as one hot encoding and/or word embeddings. Wetrained a CatBoost Classifier to predict if a publication will be cited in afuture patent or clinical trial. We trained several model variations. The modelcombining metadata, concept, and abstract embeddings yielded the highestperformance: for patent predictions, an Area Under the Receiver OperatingCharacteristic Curve (AUROC) of 0.84 and 77.17% accuracy; for clinical trialpredictions, an AUROC of 0.81 and 75.11% accuracy. The results demonstrate thatintegrating machine learning within current research methodologies can uncoveroverlooked publications, expediting the identification of promising researchand potentially transforming dementia research by predicting real-world impactand guiding translational strategies.

Machine Learning to Promote Translational Research: Predicting Patent and Clinical Trial Inclusion in Dementia Research