Machine Learning to Promote Translational Research: Predicting Patent
  and Clinical Trial Inclusion in Dementia Research
Matilda Beinat Julian Beinat Mohammed Shoaib Jorge Gomez Magenti
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.