Using Machine Learning and Electronic Health Records to Identify Neuropsychiatric Risk Scores for Delirium in ICU and General Hospital Settings.

Objective: Delirium is a common and acute neuropsychiatric syndrome that requires timely intervention to prevent its associated morbidity and mortality. Yet, its diagnosis and symptoms are often overlooked due to its variable clinical presentation and fluctuating nature. Thus, in this study, we address the barriers to delirium diagnosis by utilizing a machine learning-based predictive algorithm for incident delirium that relies on archived electronic health records (EHRs) data.

Medically-oriented design for explainable AI for stress prediction from physiological measurements.

Background: In the last decade, a lot of attention has been given to develop artificial intelligence (AI) solutions for mental health using machine learning. To build trust in AI applications, it is crucial for AI systems to provide for practitioners and patients the reasons behind the AI decisions. This is referred to as Explainable AI.

Strike: a protein-protein interaction classification approach.

Protein-protein interaction has proven to be a valuable biological knowledge and an initial point for understanding how the cell internally works. In this chapter, we introduce a novel approach termed STRIKE which uses String Kernel to predict protein-protein interaction. STRIKE classifies protein pairs into "interacting" and "non-interacting" sets based solely on amino acid sequence information.

Protein-protein interaction based on pairwise similarity.

Background: Protein-protein interaction (PPI) is essential to most biological processes. Abnormal interactions may have implications in a number of neurological syndromes. Given that the association and dissociation of protein molecules is crucial, computational tools capable of effectively identifying PPI are desirable.