The thesis provides a comprehensive overview of key issues affecting practice, policy and patients, in current efforts for colorectal cancer (CRC) disease control. The global burden of CRC is expected to increase by 60% to more than 2.2 million new cases and 1.1 million deaths by 2030. CRC incidence and mortality rates vary up to 10-fold worldwide, which is thought to reflect variation in lifestyles, especially diet. Better primary prevention, and more effective early detection, in screening and surveillance, are needed to reduce the number of patients with CRC in future1. The risk factors for CRC development include genetic, behavioural, environmental and socio-economic factors. Changes to surveillance, which offer non-invasive testing and provide primary prevention interventions represent promising opportunities to improve outcomes and personalise care in those at risk of CRC. By systematic review of the literature, I highlight the gaps in comparative effectiveness analyses of post-polypectomy surveillance. Using micro-simulation methods I assess the role of non-invasive, faecal immunochemical testing in surveillance programmes, to optimise post-polypectomy surveillance programmes, and in an accompanying sub-study, I explore the value of adding an adjunct diet and lifestyle intervention. The acceptability of such revisions is exposed to patient preference evaluation by discrete choice experiment methods. These preferences are accompanied by evidence generated from the prospective evaluation of the health literacy, numeracy, sedentary behaviour levels, body mass index (BMI) and information provision about cancer risk factors, to highlight the potential opportunities for personalisation and optimisation of surveillance. Additional analysis examines the optimisation of a screening programme facing colonoscopy constraints, highlighting the attendant potential to reduce costs and save lives within current capacity.

Information Filtering and Recommender Systems have been used and has been implemented in various ways from various entities since the dawn of the Internet, and state-of-the-art approaches rely on Machine Learning and Deep Learning in order to create accurate and personalized recommendations for users in a given context. These models require big amounts of data with a variety of features such as time, location and user data in order to find correlations and patterns that other classical models such as matrix factorization and collaborative filtering cannot. This thesis researches, implements and compares a variety of models with the primary focus of Machine Learning and Deep Learning for the task of music recommendation and do so successfully by representing the task of recommendation as a multi-class extreme classification task with 100 000 distinct labels. By comparing fourteen different experiments, all implemented models successfully learn features such as time, location, user features and previous listening history in order to create context-aware personalized music predictions, and solves the cold start problem by using user demographic information, where the best model being capable of capturing the intended label in its top 100 list of recommended items for more than 1/3 of the unseen data in an offine evaluation, when evaluating on randomly selected examples from the unseen following week. / Informationsfiltrering och rekommendationssystem har använts och implementeratspå flera olika sätt från olika enheter sedan gryningen avInternet, och moderna tillvägagångssätt beror påMaskininlärrning samtDjupinlärningför att kunna skapa precisa och personliga rekommendationerför användare i en given kontext. Dessa modeller kräver data i storamängder med en varians av kännetecken såsom tid, plats och användardataför att kunna hitta korrelationer samt mönster som klassiska modellersåsom matris faktorisering samt samverkande filtrering inte kan. Dettaexamensarbete forskar, implementerar och jämför en mängd av modellermed fokus påMaskininlärning samt Djupinlärning för musikrekommendationoch gör det med succé genom att representera rekommendationsproblemetsom ett extremt multi-klass klassifikationsproblem med 100000 unika klasser att välja utav. Genom att jämföra fjorton olika experiment,så lär alla modeller sig kännetäcken såsomtid, plats, användarkänneteckenoch lyssningshistorik för att kunna skapa kontextberoendepersonaliserade musikprediktioner, och löser kallstartsproblemet genomanvändning av användares demografiska kännetäcken, där den bästa modellenklarar av att fånga målklassen i sin rekommendationslista medlängd 100 för mer än 1/3 av det osedda datat under en offline evaluering,när slumpmässigt valda exempel från den osedda kommande veckanevalueras.

The aim of this research was to explore consumer perceptions of personalised nutrition and to compare these across three different levels of "medicalization": lifestyle assessment (no blood sampling); phenotypic assessment (blood sampling); genomic assessment (blood and buccal sampling). The protocol was developed from two pilot focus groups conducted in the UK. Two focus groups (one comprising only "older" individuals between 30 and 60 years old, the other of adults 18-65 yrs of age) were run in the UK, Spain, the Netherlands, Poland, Portugal, Ireland, Greece and Germany (N=16). The analysis (guided using grounded theory) suggested that personalised nutrition was perceived in terms of benefit to health and fitness and that convenience was an important driver of uptake. Negative attitudes were associated with internet delivery but not with personalised nutrition per se. Barriers to uptake were linked to broader technological issues associated with data protection, trust in regulator and service providers. Services that required a fee were expected to be of better quality and more secure. An efficacious, transparent and trustworthy regulatory framework for personalised nutrition is required to alleviate consumer concern. In addition, developing trust in service providers is important if such services to be successful.