Shayan Mostafaei

The general purpose of the project is designing explainable machine learning (XML) algorithms for data analysis and hereby guide decisions in medication selection. The term "explainable" in XML refers to the ability of these algorithms to provide transparent and interpretable explanations to doctors for their predictions or decisions. XML based medication repurposing for dementia (XMLD) refers to the development and application of XML algorithms to identify potential drugs among existing drugs or medications that can be repurposed for the treatment or management of dementia. The goal is to develop XML algorithms to discover new uses for approved drugs or therapies that were originally developed for different medical conditions in patients with dementia. Therefore, for a patient and/or a class of patients, identification of potential drugs among many existing drugs is a variable selection problem, where XML can help. Variable selection uses sparsity. Sparsity is ubiquitous in nature, and in this case, sparsity arises because a set of few drugs from many available drugs is more appropriate and efficient for a patient and/or a class of patients. We will use sparsity-inducing XML algorithms to select the important drugs. We will explore classical sparsity-inducing algorithms, as used in sparse representations (like LASSO) and new generation of deep learning-based algorithms. The XML algorithms are used to analyze and identify patterns, relationships, and potential associations between drug characteristics, disease severity, and patient outcomes. There are many advantages of medication repurposing for dementia using XML, such as cost and time saving, safety profile, broad range of medication candidates, and improved treatment efficiency. Overall, it addresses a pressing healthcare problem with potentially widespread impact. While our focus is dementia in this project, the accumulated technological knowledge can be used for medication repurposing of many other health problems and diseases in clinics. The proposed XMLD project will establish a strong cooperation between medical doctors and ML researchers in the clinical environment.