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Exploring the evolutionary dynamics of personalized intervention strategies and cost-effective incentive mechanisms by combining an AI-based patient Digital Twin and Game Theory in multiscale environments


   Centre for Digital Innovation

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  Dr Alessandro Di Stefano, Dr T Han, Dr C Angione  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

The increasingly higher medical care costs have reduced the access to a substantial percentage of the population, and this could come with dramatic consequences for the entire humanity. In this context, it is clear the need for precision and predictive approaches are needed to both reduce the cost per capita and design personalised treatments. As a composite ensemble of useful IoT devices, an AI-based patient digital twin represents a sort of avatar of the patient, gathering data through a composite ensemble of useful IoT (Internet of Things) devices. By combining an AI-based patient digital twin with game-theoretic modelling approach in multiscale environments and deep learning methodologies, this PhD project aims to explore the evolutionary dynamics of personalised intervention strategies and design cost-effective incentive mechanisms and personalised decision support systems. It will allow reducing the healthcare costs and increase the explainability in the deep learning analysis of patient data.

Background

 Nowadays, one of the main challenges in medicine is represented by the medical care costs and most of humanity cannot afford even basic medical care due to the growing costs that have been reducing the access to a substantial percentage of the population. To reduce healthcare costs, we need a dramatic paradigm shift in healthcare passing from a reactive to a proactive or predictive approach in medicine. The idea is therefore to leverage the emerging concepts of precision and predictive medicine, exploiting the technology-driven digital transformation of the health service and the multiscale integration of large amount of data at both individual and population levels. Since these data will be taken at different scales, from genomic data to clinical data at systemic levels, designing AI-based, machine learning and, more specifically, deep learning methods, can allow us to achieve this target of integrating multiscale data. In terms of data, one of the main issues is given by the reliability of data related to the patients, indeed reliable systems accurately forecasting physiological conditions of patients is one of the primary targets of precision medicine. The underlying complexity of human physiology, where some systems are independent from others, and the need for integrating and analysing different types of data makes this research field extremely challenging. Considering multi-omics data, this complexity is even more marked if we consider the interplay between communities (i.e., microbial communities) at a meso-scale level. Furthermore, the complexity is not only at a micro-scale level, in terms of multi-omics data, within the human body, but also at a meso-scale level and macro-scale levels, between communities of agents represented by different data gathered from multiple digital twins.

Therefore, to disentangle this complexity, preliminary work has showed the importance of a multiscale analysis and modelling approach that embodies both macro-scale, meso-scale, and micro-scale aspects (in system biology, multiscale means considering from DNA to RNA to protein, from metabolites to cells to tissues, from tissues to organisms and even to interacting populations, and microbial communities). To this aim, a meaningful approach is the integration of reliable multiscale data and models able to combine these data by using deep learning methodologies and build personalised decision support systems and incentive mechanisms able to make the entire healthcare system cost-effective and more explainable.

 Methodology

This research will be built upon methodologies and outcomes from the proposed direct of research’s past and ongoing research works and collaborations within the SCEDT, and with other Universities, such as University of Cambridge, where we are working on “Managing post covid hospital waiting times using digital twin and game theory”, with Professor Pietro Liò, Full Professor at the department of Computer Science and Technology of the University of Cambridge and a member of the Artificial Intelligence group and the Cambridge Centre for AI in Medicine. The following are some key publications in the areas of game theory, complex networks and network science: PloS one, 10(10), e0140646; PLoS computational biology, 15(1), e1006714; IEEE Transactions on Cognitive Communications and Networking, doi: 10.1109/TCCN.2020.3027697; Elsevier, Pervasive and

Mobile Computing, https://doi.org/10.1016/j.pmcj.2020.101230; Scientific reports, 6, 37105; deep learning; Spasov, S. et al., 2021; and explainability

Main aim

To design and explore the evolutionary dynamics of personalized intervention strategies and cost-effective incentive mechanism, by combining an AI-based patient digital twin with Game Theory in multiscale environments and Deep Learning methods to increase the explainability in the deep learning analysis of patient data. The final target is to address the abovementioned challenges and assumptions, and therefore generate new insights in the field of personalized and precision medicine. To achieve this goal, we shall combine research on incentives and multiplex network modelling with the data integration from AI-based patient digital twin, deriving dynamical approaches to analyze the evolutionary dynamics of behaviours of these agents as digital twins in a complex multiscale environment. This modelling approach will enable us to systematically explore how the self-awareness of the patients and different types of multiscale incentives might influence evolutionary behaviors and improve healthcare decisions.

Research strength and contributions to the school strategy

 This project will contribute to the school growing international reputation in behavioural modelling research, incentives design, and AI-related research, enabling these important research areas to become a Teesside and SCEDT research identity, both at a national and international level. Medical care growing costs and the reduced access to healthcare of a substantial percentage of the population represents a big concern. This PhD project will bring fundamental contributions that help us better understand how to reduce healthcare costs, and build precision and predictive medicine approaches. These will allow us not only to design cost-effective incentive mechanisms and personalised decision support systems, but also to increase the explainability in the deep learning analysis of patient data. Given the research community’s extensive engagement with healthcare costs, AI-based tools and methodologies, and their emphasis in the UK National AI Strategy, this project is timely, topical and of potentially great practical importance.


Funding Notes

The Fees-Paid PhD studentship will cover all tuition fees for the period of a full-time PhD Registration of up to four years. Successful applicants who are eligible will be able to access the UK Doctoral Loan scheme https://www.gov.uk/doctoral-loan to support with living costs. The Fully Funded PhD Studentship covers tuition fees for the period of a full-time PhD Registration of up to four years and provide an annual tax-free stipend of £15,000 for three years, subject to satisfactory progress. Applicants who are employed and their employer is interested in funding a PhD, can apply for a Collaborative Studentship.

References

Di Stefano, A., Scatà, M., La Corte, A., Liò, P., Catania, E., Guardo, E., & Pagano, S. (2015). “Quantifying the role of homophily in human cooperation using multiplex evolutionary game theory”. PloS one, 10(10), e0140646.
Di Stefano, A., Scatà, M., Vijayakumar, S., Angione, C., La Corte, A., & Liò, P. (2019). “Social dynamics modeling of chrono-nutrition”. PLoS computational biology, 15(1), e1006714.
Scatà, M., Di Stefano, A., La Corte, A., & Liò, P. (2020). “Multiplex Social Contagion Dynamics Model to shape and discriminate D2D content dissemination”. IEEE Transactions on Cognitive Communications and Networking, doi: 10.1109/TCCN.2020.3027697. https://ieeexplore.ieee.org/abstract/document/9209110 .
Di Stefano, A., Scatà, M., Attanasio, B., La Corte, A., Liò, P., & Das, S. K. (2020). “A Novel Methodology for designing Policies in Mobile Crowdsensing Systems”. Elsevier, Pervasive and Mobile Computing, Vol. 67, 2020, 101230, ISSN 1574-1192, https://doi.org/10.1016/j.pmcj.2020.101230
Scatà, M., Di Stefano, A., Liò, P., & La Corte, A. (2016). “The impact of heterogeneity and awareness in modeling epidemic spreading on multiplex networks”. Scientific reports, 6, 37105. https://doi.org/10.1038/srep37105.
Spasov, S., Di Stefano, A., Lio, P., & Tang, J. (2021). “GRADE: Graph Dynamic Embedding” (arXiv preprint: arXiv:2007.08060). Submitted to: IEEE Transactions on Neural Networks and Learning Systems.
Cimpeanu, T., Perret, C., & Han, T. A. (2021). Cost-efficient interventions for promoting fairness in the ultimatum game. Knowledge-Based Systems, 107545.
Duong, M. H., & Han, T. A. (2021). Cost efficiency of institutional incentives for promoting cooperation in finite populations. Proceedings of the Royal Society A, 477(2254), 20210568.
Han, T. A. (2021). Institutional Incentives for the Evolution of Committed Cooperation: Ensuring Participation is as Important as Enhancing Compliance - arXiv preprint arXiv:2110.13307.
Barbiero, P., Torné, R. V., & Lió, P. (2021). Graph Representation Forecasting of Patient's Medical Conditions: Toward a Digital Twin. Frontiers in genetics, 12.
Müller, T., & Lió, P. (2020, January). Personalisable Clinical Decision Support System. ERCIM.
Kazhdan, D., Dimanov, B., Jamnik, M., Liò, P., & Weller, A. (2020). Now you see me (CME): concept-based model extraction. arXiv preprint arXiv:2010.13233.
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