• Machine Learning in drug discovery has greatly benefited the pharmaceutical industry. • Application of machine algorithms must entail a robust design in real clinical tasks. • Trending machine learning algorithms in drug design: NB, SVM, RF and ANN.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Major Depressive Disorder (MDD) or depression is among the most prevalent psychiatric disorders, affecting more than 300 million people worldwide. Early detection is critical for rapid intervention, which can potentially reduce the escalation of the disorder. Objective: This study used data from social media networks to explore various methods of early detection of MDDs based on analysis with artificial intelligence. Methods: Our best performing method is based on a dual approach that uses a machine learning model to detect depressed subjects and another model to identify non-depressed individuals. Results: The results of a thorough evaluation of the method following a time-aware approach that rewards early detections demonstrate that the dual model can improve current state-of-the-art detection models by more than 10%. Conclusions: Given the results, we consider that this work can help in the development of new solutions to deal with the early detection of depression on social networks.

Recent data suggest that concentrations of lipoprotein(a) [Lp(a)] may be inversely associated with the risk of diabetes. This study analyzed the relationships between Lp(a) and both diabetes and insulin resistance in an adult cohort from the island of Gran Canaria, Spain.