Pharmacotherapy in Anorexia Nervosa

There is currently no evidence base for prescribing antidepressants or antipsychotics in young people with eating disorders. There is a need for greater understanding of psychotropic prescribing in individuals with anorexia nervosa (AN) to provide guidance for their use in clinical practice. The aim of this PhD was to explore and describe the drug utilisation and effectiveness of psychopharmacotherapy in individuals with AN. First, a systematic review was conducted to review the current literature. Next, pharmacotherapy in individuals with AN was explored by three means; 1) through self-reported questionnaires by child and adolescent eating disorder (CAED) psychiatrists on their prescribing practices in AN, 2) using The Health Improvement Network (THIN) database by reporting on AN patterns and describing prescribing patterns in AN in primary care, and 3) in multisite specialised child and young people eating disorder services (CYP EDS) within secondary healthcare settings by describing AN population and examining the effects of psychotropic treatment on weight change. Findings from my review fail to provide strong evidence for any increase in weight associated with the use of psychotropic drugs in adolescents with AN and show some evidence of harmful effects associated with their use. Studies in this thesis have shown there is a high use of psychotropics in AN treatment, with around half of individuals in the primary and secondary care study having a record for psychotropic prescriptions, of which olanzapine and fluoxetine are the most common. No serious adverse events were found in any of the studies. After six months of pharmacotherapy, the mean BMI of those individuals on antipsychotics was greater than the mean BMI of those on antidepressants or no medication, despite having a lower starting BMI upon diagnosis. This thesis found that although a lack of strong existing evidence, psychotropic medications are often prescribed for the treatment of young people with AN

Enabling the Analysis of Personality Aspects in Recommender Systems

Existing Recommender Systems mainly focus on exploiting users’ feedback, e.g., ratings, and reviews on common items to detect similar users. Thus, they might fail when there are no common items of interest among users. We call this problem the Data Sparsity With no Feedback on Common Items (DSW-n-FCI). Personality-based recommender systems have shown a great success to identify similar users based on their personality types. However, there are only a few personality-based recommender systems in the literature which either discover personality explicitly through filling a questionnaire that is a tedious task, or neglect the impact of users’ personal interests and level of knowledge, as a key factor to increase recommendations’ acceptance. Differently, we identifying users’ personality type implicitly with no burden on users and incorporate it along with users’ personal interests and their level of knowledge. Experimental results on a real-world dataset demonstrate the effectiveness of our model, especially in DSW-n-FCI situations