Recent decades have seen increasing recognition of the importance of patient involvement during patient-professional interactions and promotion of preventative and long term approaches to healthcare for those with long-term conditions. The concepts of 'shared decision making' and 'personalised care planning' have both been advocated by patient groups, policy-makers, professional bodies and academia as best practice. During shared decision making, patients and healthcare professionals work in equal partnership to decide the best course of action. Shared decision making is a central tenet of personalised care planning, as it aims to foster partnerships between patients and healthcare professionals when making decisions, but personalised care planning also describes an overall approach to healthcare that is forward-planning and preventative, rather than episodic and reactive. Despite the breadth of support for such approaches, in reality they are not routinely adopted. Multiple Sclerosis (MS) is a heterogeneous neurodegenerative long term condition, which is unpredictable with limited treatments available. Such uncertainty and complexity position MS as an interesting long term condition to explore decisional and information needs. This doctoral research comprises of three methods stages. Firstly, two systematic reviews assessing the effectiveness of personalised care planning for people with long-term conditions and people with MS were carried out. Secondly, 22 in-depth semi-structured qualitative interviews were carried out with people with MS across the UK to explore experiences of decision making and interactions with healthcare professionals. Purposive sampling was carried out and data saturation determined sample size. A modified grounded theory approach was used and thematic analysis of interview data was carried out. Lastly, a series of structured qualitative interviews were carried out with 6 consultant neurologists. This stage was iterative in that problematic areas identified during analysis of interview data from stage 2 were presented to neurologists in infographic form to further examine issues raised. Framework analysis was carried out on neurologist interview data to examine their interpretations and potential solutions. Although there appears to be some evidence demonstrating that personalised care planning is effective for people with long term conditions, such favourable effects were not demonstrated in the context of MS. Based on the findings from the systematic reviews it is unclear whether personalised care planning is effective for people with MS and there is a clear gap in the literature examining this. Findings from the interview stages suggest there are key areas which are lacking in terms of information and decisional support. Such areas included the type and amount of information around the time of diagnosis, support when choosing disease modifying drugs and discussions about approaches outside mainstream medicine. Findings from neurologist interview data corroborated those from MS interview data, but through examination of issues raised it also highlighted some of the complexities and challenges of involving patients and enacting shared decision making in reality. This research identified key areas that require improvement for people with MS in terms of provision of the information and decisional support. Although in theory personalised care planning and shared decision making are positioned as best practice, in reality it is unclear whether they are effective or appropriate for people with MS. The way in which such approaches are enacted are complex and require careful consideration. Potential barriers and pitfalls identified within this study suggest a lack of clarity in how to respond to challenges and further investigation into how patient involvement is enacted is needed.

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Dans le contexte de recherche et développement d’un nouveau traitement thérapeutique, le chercheur veut surveiller ses thématiques de recherche pour actualiser ses connaissances. Il a besoin d’accéder à l’information qui lui est utile directement sur son ordinateur. La prise en compte de la complexité d’un système biologique, révèle la très grande difficulté à traduire de façon linguistique toute une réflexion hypothétique. Nous proposons dans ce travail, un procédé détaché du système de langue. Pour ce faire, nous présentons une méthodologie basée sur une indexation qualitative en utilisant un filtrage personnalisé. L’index n’est plus d’ordre linguistique mais de type « liaisons de connaissances ». Cette méthode d’indexation qualitative appliquée à « l’information retrieval » contraste avec l’indexation documentaire et l’utilisation d’un thésaurus tel que le MeSH lorsqu’il s’agit d’exprimer une requête complexe. Le choix du sujet d’expérimentation sur la base de données Medline via PubMed constitue une démonstration de la complexité d’expression d’une problématique de recherche. Le thème principal est un traitement possible de la maladie d’Alzheimer. Cette expérience permet de mettre en avant des documents contenus dans Medline qui ne répondent pas ou peu à une indexation en mots-clés. Les résultats obtenus suggèrent qu’une « indexation en connaissances » améliore significativement la recherche d’information dans Medline par rapport à une simple recherche sur Google pratiquée habituellement par le chercheur. Assimilable à une veille scientifique, cette méthodologie ouvre une nouvelle collaboration entre professionnels de l’information et chercheurs / In the context of research and development for a new therapeutic treatment, the researcher seeks to monitor relevant research topics in order to update field-specific knowledge. Direct computer access to relevant information is required. The complexity of biological systems increases the great difficulty of translating some hypothetical reflections in a linguistic manner or by semiotics. In this study, we propose a detached process of the system of language. To do this, we will present a methodology based on a qualitative indexing using personalized filtering. The index is no longer of a linguistic nature but a sort of “connection of knowledge”. This method of qualitative indexing applied to information retrieval is in contrast with documentation indexing systems and the use of thesauruses such as MeSH when it pertains to formulating a complex request. The choice of the experimentation subject using Medline database via PubMed proves the complexity of research problem formulation. The main theme is a possible treatment of Alzheimer's disease. This experiment makes it possible to highlight the documents contained in Medline which provide few or no answers by indexing keywords. The results obtained suggest that an indexing knowledge significantly improves search results for information via Medline in comparison to “Google” searches habitually carried out by the researcher. Comparable to scientific awareness, this methodology opens new collaboration possibilities between information professionals and research

Pharmacogenetics (PGx) and personalised medicine are new disciplines that, gathering the existing knowledge about the genetic and phenotypic factors that underpin drug response, aim to deliver more targeted therapies that avoid the existing problems of adverse drug reactions or lack of drug efficacy. PGx and personalised medicine imply a shift in the way drugs are prescribed, as they require introducing diagnostic tools and implementing pre-screening mechanisms that assess patients' susceptibility to new or existing drugs. The direct benefit is an improvement in drug safety and/or efficacy. However, neither pharmacogenetics nor personalised medicine, are widely used in clinical practice. Both technologies face a number of controversies that hamper their widespread use in clinical practice. This thesis investigates the scientific; technological; social; economic; regulatory and ethical implications of PGx and personalised medicine, to understand the enablers and barriers that drive the process of technology diffusion in three conditions: autoimmune diseases, acute lymphoblastic leukaemia and non-small cell lung cancer.The thesis uses concepts of the sociology of science and a qualitative approach, to explore the arguments for and against the use of the technology by different actors (pharmaceutical and biotechnology companies, researchers, clinicians, regulators and patient organisations). The core of this analysis lies in the understanding of how, diagnostic testing (TPMT testing in the case of autoimmune diseases, acute lymphoblastic leukaemia, and EGFR testing in the case of non-small-cell lung cancer) may affect the existing drug development and service delivery mechanisms, with a particular focus on the user-producer interactions and feedback mechanisms that underpin diffusion of medical innovations and technological change in medicine.The thesis concludes by identifying gaps in knowledge and common issues among TPMT and EGFR testing, which might be used, in the future, to inform policy on how to improve PGx service delivery through a public Health System such as the NHS.

One major objective in radiation oncology is the personalisation of cancer treatment. The implementation of this concept requires the identification of biomarkers, which precisely predict therapy outcome. Besides molecular characterisation of tumours, a new approach known as radiomics aims to characterise tumours using imaging data. In the context of the presented thesis, radiomics was established at OncoRay to improve the performance of imaging-based risk models. Two software-based frameworks were developed for image feature computation and risk model construction. A novel data-driven approach for the correction of intensity non-uniformity in magnetic resonance imaging data was evolved to improve image quality prior to feature computation. Further, different feature selection methods and machine learning algorithms for time-to-event survival data were evaluated to identify suitable algorithms for radiomics risk modelling. An improved model performance could be demonstrated using computed tomography data, which were acquired during the course of treatment. Subsequently tumour sub-volumes were analysed and it was shown that the tumour rim contains the most relevant prognostic information compared to the corresponding core. The incorporation of such spatial diversity information is a promising way to improve the performance of risk models.:1. Introduction 2. Theoretical background 2.1. Basic physical principles of image modalities 2.1.1. Computed tomography 2.1.2. Magnetic resonance imaging 2.2. Basic principles of survival analyses 2.2.1. Semi-parametric survival models 2.2.2. Full-parametric survival models 2.3. Radiomics risk modelling 2.3.1. Feature computation framework 2.3.2. Risk modelling framework 2.4. Performance assessments 2.5. Feature selection methods and machine learning algorithms 2.5.1. Feature selection methods 2.5.2. Machine learning algorithms 3. A physical correction model for automatic correction of intensity non-uniformity in magnetic resonance imaging 3.1. Intensity non-uniformity correction methods 3.2. Physical correction model 3.2.1. Correction strategy and model definition 3.2.2. Model parameter constraints 3.3. Experiments 3.3.1. Phantom and simulated brain data set 3.3.2. Clinical brain data set 3.3.3. Abdominal data set 3.4. Summary and discussion 4. Comparison of feature selection methods and machine learning algorithms for radiomics time-to-event survival models 4.1. Motivation 4.2. Patient cohort and experimental design 4.2.1. Characteristics of patient cohort 4.2.2. Experimental design 4.3. Results of feature selection methods and machine learning algorithms evaluation 4.4. Summary and discussion 5. Characterisation of tumour phenotype using computed tomography imaging during treatment 5.1. Motivation 5.2. Patient cohort and experimental design 5.2.1. Characteristics of patient cohort 5.2.2. Experimental design 5.3. Results of computed tomography imaging during treatment 5.4. Summary and discussion 6. Tumour phenotype characterisation using tumour sub-volumes 6.1. Motivation 6.2. Patient cohort and experimental design 6.2.1. Characteristics of patient cohorts 6.2.2. Experimental design 6.3. Results of tumour sub-volumes evaluation 6.4. Summary and discussion 7. Summary and further perspectives 8. Zusammenfassung

I och med teknologins framfart har digital marknadsföring blivit ett av de vanligaste sätten för marknadsförare att nå sin publik. Med denna digitala utveckling ökar också möjligheterna för marknadsföringen. Något som för 25 år sedan bara var en dröm är idag en mycket aktuell verklighet -nämligen digital one-to-one marknadsföring. Denna marknadsföringsform går ut på att enorma mängder data samlas in om individer som sedan används för att skapa personaliserade annonser anpassade just för dessa individers behov. Teknologin gör detta möjligt och denna marknadsföringsform befinner sig ofta på sociala medier. En konsumentgrupp som spenderar mycket tid på sociala medier är tonåringar. Denna studie ämnar därför undersöka hur tonåringar upplever att deras köpbeteende påverkas av digital one-to-one marknadsföring.  Kvalitativa intervjuer har utförts till empiriinsamlingen med respondenter i åldrarna 15-17 år. Alla respondenter känner sig påverkade av digital one-to-one marknadsföring. De känner oro för vad deras information används till och tycker att riskerna med att ge ifrån sig information överväger nyttan av att få anpassade annonser. Denna integritetsoro skulle möjligtvis kunna dämpas om information försågs med var företaget hittat den personliga informationen. Hos tonåringar kan denna marknadsföring skapa behov som de inte hade tidigare och bidra med fler alternativ för att tillfredsställa detta behov. One-to-one marknadsföring har alltså en direkt påverkan på tonåringars köpbeteende. / With the steady and rapid growth of technology, digital marketing has become one of the most common ways for advertisers to reach out to a broader audience. Instagram and Snapchat are the most commonly used social networking services among today’s adolescents that highly utilise the concept of personalised digital advertising through third parties. One-to-one marketing is a form of personalised marketing that aims to collect information about individual consumers in order to provide each individual with advertisements fitted to their interests. This study takes a closer look on how teenagers perceive the personal effect of one-to-one marketing.  The empirical research was obtained from qualitative interviews carried out among teenagers aged 15 to 17 years old. Upon conclusion of the study, it was found that all respondents felt influenced by digital one-to-one marketing and are worried about the use of their information. Ultimately, respondents think the risks outweigh the benefits and want to know where the company gets their information from. In adolescents, one-to-one marketing can create needs that did not exist before and can be met through the provision of more options. Hence, one-to-one marketing has a direct impact on the purchasing behaviour of adolescents.

A standard Neurofeedback approach to mitigate the working memory decline in some fragile groups (elderly, subjects affected by stroke or Alzheimer's disease) can be suboptimal for some patients. The goal of this research is to investigate which visual stimulus (among colour, geometrical shape, direction, and symbol) is the most suited for each of the six healthy participants and which brain areas are the most discriminative, during the maintenance of a presented stimulus in a retro-cue-based working memory experiment. In order to identify the most discriminative stimulus, the single-trial classification accuracies of some Support Vector Machines, trained on the theta, alpha and beta electroencephalography power bands, have been compared; while, in order to identify the most involved brain regions, three machine learning feature reduction techniques have been explored: the first based on a massive univariate analysis, the second based on multivariate filtering and wrapping, and the last one based on Frequency-based Common Spatial Pattern. The results have shown that the univariate approach, more than the others, managed to clearly identify for each participant at least one preferential type of stimulus and a brain region of discriminative electrodes during the maintenance of the stimulus. These promising results can be interpreted as a further step to optimize the Neurofeedback working memory enhancement through a personalised approach.

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Background: Chatbots have developed from simple algorithmic tools to advanced systems that automate and personalise B2B customer interactions, therefore improving CRM through the efficiency of service and customer satisfaction. Purpose: The purpose of this thesis is to explore the potential of chatbots as a solution to address the challenges faced by B2B customer service in today's business environment. Method: This chapter explains the utilisation of semi-structured interviews in relation to discovering challenges and later on the possible chatbot solutions for B2B customer service. It includes details on the selection of participants, data collection, and methods for data analysis. Findings: The study explores challenges and strategies in B2B customer service, where high inquiry volumes, after-hours contact, and the need for more staff are prominent issues. Factors for quality service include time, staff training, and efficient communication channels. Trust and consistency are upheld through internal communication tools and CRM technology. The participants commented on the need for quick responses and discussed the future use of technologies like chatbots. Conclusion: This thesis focuses on how chatbots can enhance B2B customer service through efficient handling of high-volume inquiries, ensuring GDPR compliance, and balancing automated responses with personalised engagement. These capabilities further service responsiveness and personalization, leading to increasing customer satisfaction and loyalty in B2B environments.

When using socially assistive robots (SAR), it is important that their personality is personalised so that it suits their user. This work investigated how the customisation of the personality of a SAR health coach is perceived when done by the users themselves or their friends via ChatGPT. Therefore, the research question in this study is: How is personalised dialogue for a social robot perceived when generated via ChatGPT, by users and their friends? This study uses a mixed method approach, where participants got to test their own and their friend’s personalised version. The qualitative data was analysed using a thematic analysis. Sixteen participants were recruited.The result from this study showed that it does not matter who is customising the SAR, nor does one make a more persuasive version than the other, and when customising the personality, participants explained what they or their friend preferred. However, it is important to remember that the individual’s preference matters.