Dynamic Models Supporting Personalised Chronic Disease Management through Healthcare Sensors with Interactive Process Mining

[EN] Rich streams of continuous data are available through Smart Sensors representing a unique opportunity to develop and analyse risk models in healthcare and extract knowledge from data. There is a niche for developing new algorithms, and visualisation and decision support tools to assist health professionals in chronic disease management incorporating data generated through smart sensors in a more precise and personalised manner. However, current understanding of risk models relies on static snapshots of health variables or measures, rather than ongoing and dynamic feedback loops of behaviour, considering changes and different states of patients and diseases. The rationale of this work is to introduce a new method for discovering dynamic risk models for chronic diseases, based on patients¿ dynamic behaviour provided by health sensors, using Process Mining techniques. Results show the viability of this method, three dynamic models have been discovered for the chronic diseases hypertension, obesity, and diabetes, based on the dynamic behaviour of metabolic risk factors associated. This information would support health professionals to translate a one-fits-all current approach to treatments and care, to a personalised medicine strategy, that fits treatments built on patients¿ unique behaviour thanks to dynamic risk modelling taking advantage of the amount data generated by smart sensors.This research was partially funded by the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement no. 727560.Valero Ramon, Z.; Fernández Llatas, C.; Valdivieso, B.; Traver Salcedo, V. (2020). Dynamic Models Supporting Personalised Chronic Disease Management through Healthcare Sensors with Interactive Process Mining. Sensors. 20(18):1-25. https://doi.org/10.3390/s20185330S1252018Chen, M., Mao, S., & Liu, Y. (2014). Big Data: A Survey. 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Digital phenotyping through multimodal, unobtrusive sensing

The growing adoption of multimodal wearable and mobile devices, such as smartphones and wrist-worn watches has generated an increase in the collection of physiological and behavioural data at scale. This digital phenotyping data enables researchers to make inferences regarding users’ physical and mental health at scale, for the first time. However, translating this data into actionable insights requires computational approaches that turn unlabelled, multimodal time-series sensor data into validated measures that can be interpreted at scale. This thesis describes the derivation of novel computational methods that leverage digital phenotyping data from wearable devices in large-scale populations to infer physical behaviours. These methods combine insights from signal processing, data mining and machine learning alongside domain knowledge in physical activity and sleep epidemiology. First, the inference of sleeping windows in free-living conditions through a heart rate sensing approach is explored. This algorithm is particularly valuable in the absence of ground truth or sleep diaries given its simplicity, adaptability and capacity for personalization. I then explore multistage sleep classification through combined movement and cardiac wearable sensing and machine learning. Further, I demonstrate that postural changes detected through wrist accelerometers can inform habitual behaviours and are valuable complements to traditional, intensity-based physical activity metrics. I then leverage the concomitant responses of heart rate to physical activity that can be captured through multimodal wearable sensors through a self-supervised training task. The resulting embeddings from this task are shown to be useful for the downstream classification of demographic factors, BMI, energy expenditure and cardiorespiratory fitness. Finally, I describe a deep learning model for the adaptive inference of cardiorespiratory fitness (VO2max) using wearable data in free living conditions. I demonstrate the robustness of the model in a large UK population and show the models’ adaptability by evaluating its performance in a subset of the population with repeated measures ~6 years after the original recordings. Together, this work increases the potential of multimodal wearable and mobile sensors for physical activity and behavioural inferences in population studies. In particular, this thesis showcases the potential of using wearable devices to make valuable physical activity, sleep and fitness inferences in large cohort studies. Given the nature of the data collected and the fact that most of this data is currently generated by commercial providers and not research institutes, laying the foundations for responsible data governance and ethical use of these technologies will be critical to building trust and enabling the development of the field of digital phenotyping.I was funded by GlaxoSmithKline and the Engineering and Physical Sciences Research Council. I was also supported by the Alan Turing Institute through their Enrichment Scheme

User acceptance and adoption of smart homes: A decade long systematic literature review

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Addressing data accuracy and information integrity in mHealth using ML

The aim of the study was finding a way in which Machine Learning can be applied in mHealth Solutions to detect inaccurate data that can potentially harm patients. The result was an algorithm that classified accurate and inaccurate data

Automatic Generation of Personalized Recommendations in eCoaching

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SPARC 2018 Internationalisation and collaboration : Salford postgraduate annual research conference book of abstracts

Welcome to the Book of Abstracts for the 2018 SPARC conference. This year we not only celebrate the work of our PGRs but also the launch of our Doctoral School, which makes this year’s conference extra special. Once again we have received a tremendous contribution from our postgraduate research community; with over 100 presenters, the conference truly showcases a vibrant PGR community at Salford. These abstracts provide a taster of the research strengths of their works, and provide delegates with a reference point for networking and initiating critical debate. With such wide-ranging topics being showcased, we encourage you to take up this great opportunity to engage with researchers working in different subject areas from your own. To meet global challenges, high impact research inevitably requires interdisciplinary collaboration. This is recognised by all major research funders. Therefore engaging with the work of others and forging collaborations across subject areas is an essential skill for the next generation of researchers

An innovative approach for health care delivery to obese patients: from health needs identification to service integration

In Europe, more than half of the population is overweight or obese, and effort to design, validate, and implement innovative approaches is required to address social and health unmet needs of obese patients in terms of health promotion, disease prevention, and integration of services. The challenge is improving the collaboration between the different health and care stakeholders involved in the lives of obese patients, changing the socio-cultural attitude towards food intake and other behaviours leading to a negative impact on their health-related quality of life. The digital transformation of health and care can support changes in healthcare systems, healthy policy, and approaches to patient care and better implementation of the different health promotion and disease prevention strategies between all the stakeholders and support obese patients. Based on the previously experience adopted by Blueprint Partners with the Blueprint persona and user scenario in the context of models of care and prevention, health policies and analysis of risk factors affecting health and quality of life of obese subjects, the study aimed to simulate an integrated care pathway, through a multidisciplinary approach, developing and applying solutions and good clinical practices addressing the social and health unmet needs of obese patients. A pilot study assessed the quality of life (QoL), adherence to the Mediterranean diet, efficacy and interoperability of a digital health platform, Paginemediche. it. A qualitative approach has been adopted to identify and specify key digital solutions and high-impact user scenarios in Active and Healthy Ageing (AHA). To achieve a successful result, an iterative and collaborative approach has been followed to develop a user-centred perspective to the identification of solutions addressing health needs with different complexity along the entire life-course. Four initial key topic areas were chosen and used to identify different digital solutions that may meet the needs of the population segments defined by both age and the complexity of their health status. All data, derived from the industry representatives in the European Innovation Partnership on Active and Healthy Ageing (EIP on AHA), were collected via a survey to how digital solutions best met the needs of the various population segments represented by personas. Subsequently, innovative solutions were designed based on how a user from a target group interacts with technologies, developing "personas" belonging to specific "population segments" with different conditions and needs. Then, a high-impact user scenario, based on the correlation of personas' needs, good clinical practices and digital solutions available targeting needs which playing a role in the health and care delivery for the persona, has been developed. In the end, to evaluate how digital solutions and technologies can support obese patients during their weight loss or management of their related comorbidities in current service provision, ten obese patients were enrolled to evaluate a Digital Health platform, pagininemediche.it, developed. Matilde, the Blueprint persona developed, highlighted some of the main needs (social support, development of a health-friendly environment and educational program on healthy nutrition and physical activity) that may be addressed by integrating innovative solutions in the care of obese patients. Based on her profile, a high-impact user scenario diagram correlates health and social needs with digital solutions and can help key actors in the creation of a well-integrated care approach. Moreover, the evaluation of the digital platform, paginemediche.it, demonstrated how digital solutions can motivate and support obese patients in changing habits towards a healthy lifestyle, although no further statistical significance has been identified in the quality of life assessment because of the limited number of the patients, and short period of observation. Overweight or obese patients tend to be marginalized and the subject of a real social stigma. Digital solutions may be useful to overcome psychological factors that prevent obese patients from starting their journey for a lifestyle change. The suggested approach, which considers health needs, IT skills, socioeconomic context, interoperability, and integration gaps that may influence the adoption of innovative solutions tailored to improve health outcomes is person-centred, and identify what is important for obese patients. The implementation of a persona and user scenario approach may also be useful for the early involvement of end-users in solutions' design and adaptation, increasing adherence, and the effectiveness of digital solutions. Persona profiles, the user scenario, and the related digital solution also consider the potential benefits that can derive for both patients and health system in term of reduced emergency room admissions, waiting lists, and health related expenditures

Radial Basis Function Neural Network in Identifying The Types of Mangoes

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