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Personal Health Data Science

…concentrates on research aimed at shifting healthcare philosophy from reactive to proactive, as an integral part of the Healthcare 2050 vision. Most people are now constantly “online”, consuming and generating ever growing amounts of data, and this trend is expected to continue. Undoubtedly, strong potential exists for creating a positive change in how people manage and influence their health through these data. The long-term goal of this effort is to create a personal health tracker that collects personal health information and provides prediction of our future wellbeing. Examples of such information include longitudinal medical data, genetics, behavioural information, stress levels, dietary habits, environmental and socio-economic factors and other. Apart from computing existing risks, such a tracker could also predict them if the input changes, thus empowering individuals to positively affect their own health, prevent disease and, in consequence, live longer and in better health.

 

Collaboration with healthcare insurers will bring access to historical data about interaction of a vast number of patients with the healthcare system, census, death records, environmental data – which will be used for analytics and to create predictive machine learning models of personal health. Sano will also work with medical institutions to obtain detailed medical data, including EHR imaging and genetics, of carefully selected cohorts around Poland. By strategically combining limited, but representative samples of population, our researchers will develop detailed machine learning models at the national level. This could then be recreated for a different country, or even on a larger scale (like, e.g. the EU).

 

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Dr. Jose Sousa, PhD

Leader of the Sano Science Research Team for Personal Health Data Science

Dr. Jose Sousa, PhD, is the Research Leader of the Personal Health Data Science Group at SANO developing fundamental research on AI machine self-learning applied to non-communicable diseases. Previously he was the Manager of the Advanced Informatics Core Technology Unit in the Faculty of Medicine, Health and Life Sciences (FHMLS) at QUB. He obtained his PhD under Prof. Ricardo Machado (University of Minho, Portugal) and Prof. Jose Mendes (University of Aveiro, Portugal) supervision at University of Minho, Portugal on developing complex network models to study software usage alignment with the project requirements [1.11]. Previously and during his PhD he worked as Information Systems Manager at I3S, a research institution of University of Porto (i3.sup.pt) where he deployed and managed all the IT infrastructure as well as deploying and developing software to support management and research operations.

He is presently collaborator of the DEMON Network as an AI researcher and actively working with International Public Health Centres [1.12] and AI Research Teams such as CLAIRE AI as part of the response to COVID-19 [1.13][1.14] where he is developing self-learning AI on publicly available and self-reporting data. He also has work on genetic alignment modelling [1.15], and on mining of socio-technical systems [1.16].

Selected Publications

  • [1.11] José L.R. Sousa, Ricardo J. Machado, J.F.F. Mendes (2012), Modelling Organizational Information Systems Using “Complex Networks” Concepts, Proceedings of the 8th International Conference on the Quality of Information and Communications Technology (QUATIC), 365-370, IEEE Computer Society Press;
  • [1.12] Sousa, J., Barata, J., Woerden, H. C. van & Kee, F. COVID-19 Symptoms app analysis to foresee healthcare impacts: Evidence from Northern Ireland. Applied Soft Computing, 116, 108324–108324, 2022,  https://doi.org/10.1016/j.asoc.2021.108324.
  • [1.13] José Sousa, João Barata, Tracking the wings of covid-19 by modelling adaptability with open mobility data, Applied Artificial Intelligence, https://doi.org/10.1080/08839514.2020.1840196, Applied Artificial Intelligence Journal, 2020;
  • [1.14] Bontempi, G. et al., “The CLAIRE COVID-19 initiative: approach, experiences and recommendations”, Ethics and Information Technology, 2021, https://doi.org/10.1007/s10676-020-09567-7.
  • [1.15] AC Roddy, A Jurek, J Sousa, A Gilmore, PG O’Reilly, A Stupnikov, D Gonzalez DeCastro, KM Prise, M Salto-Tellez, DG McArt, NUQA: Estimating cancer spatial and temporal heterogeneity and evolution through alignment-free methods, Molecular Biology and Evolution Journal, 2019;
  • [1.16] José Sousa, João Barata, “Mining Sociotechnical Patterns of Enterprise Systems with Complex Networks: A Guiding Framework” chapter in Pańkowska, M. (2021). Autopoiesis and Self-Sustaining Processes for Organizational Success. IGI Global. http://doi:10.4018/978-1-7998-6713-5 (https://www.igi-global.com/book/autopoiesis-self-sustaining-processes-organizational/256641)

Kamil Woźniak

PhD Student

He holds a Master's Degree in Physics and is currently pursuing one in Computer Science. At Sano, he is part of the Personal Health Data Science team working on self-learning AI. His interests include the application of AI in various scientific fields, the limitations of current approaches to AI, and its philosophical aspects.

Anna Dróżdż

PostDoc in Personal Health Data Science

Anna obtained her M.Sc. in Laboratory Medicine form Jagiellonian University and Engineer’s Degree in Materials Science from AGH University of Science and Technology. In 2021 Anna defended her thesis in Physics (with the specialization in Biophysics) and was awarded a Ph.D. degree by the Jagiellonian University. She worked briefly as an Research Assistant in the Department of Medical Physics on JU. Before joining Sano, her research focused on drug delivery systems, long-distance cell-to-cell communication and diabetes. She was a visiting graduate researcher at Academic Medical Center of the University of Amsterdam and Joseph Stefan Institute in Ljubljana . At Sano Anna will work on connecting the worlds of data science and biology. In her free time she enjoys travelling, mountaineering and reading.

Current projects

Scientific Directions:

The group has a research vision of “Citizen-Before-Patient” (CB4P). To develop it, it’s focus on the mission of “Empowering personal health decision making within actionable insights of a Computational Intelligence Architecture of Choice”.

While most individuals are now constantly “online”, consuming and providing enormous amounts of information, healthcare deliverables remain relatively unchanged compared to thirty years ago.

  • With a long-term goal to co-create a personal health architecture of choice that will collect available information about individuals and provide model based evidence on personal health.
  • By integrating medical records, genetics, microbiome genetics, behavioural information (exercise level, sleep quality, physical activity, etc.), stress levels, dietary habits, food quality, environmental factors (e.g. pollution, allergen levels) and other socioeconomic factors build a multiverse machine self-learning process.
  • Building upon Poland’s unique position as a single-payer healthcare system with centralized access to data concerning a vast number of patients.
  • Co-developing with medical institutions access to detailed medical data (EHR, imaging and multi-omics).
  • We aim to deliver AI machine self-learning models for improving the health of entire populations.

These are being translated in the development of different project having as background populational epidemiology and individual paths supported by the development of self-semantic machine learning.

Funder: Diabetes UK

Sano team: Anna Drozdz, José Sousa

Goal: Prospect diseases transaction by modelling risk factors data by building machine self-semantic learning

Collaboration:

  • Queen’s University Belfast

Data:

  • NICOLA IS THE "NORTHERN IRELAND COHORT FOR THE LONGITUDINAL STUDY OF AGEING" - The study has recruited 8,500 people from across Northern Ireland to provide a true representation of the Northern Ireland population.  Our aim is to monitor these individuals and examine how their health, lifestyle, financial circumstances, and overall wellbeing changes over the next 10 years.

Preliminary results:

  • A self-semantic machine learning approach capable to model the prevalent risks in the transaction from healthy to diabetes.
  • Publication in preparation

Publications

Sousa, José; JoãoBarata,; van Woerdend, Hugo C; Kee, Frank

COVID-19 Symptoms App Analysis to Foresee Healthcare Impacts: Evidence from Northern Ireland Journal Article

In: Applied Soft Computing, 2021.

Abstract | BibTeX | Links:

Bacciu, Davide; Girardi, Emanuela; Maratea, Marco; Sousa, Jose

AI & COVID-19 Journal Article

In: Intelligenza Artificiale, 2021.

Abstract | BibTeX | Links: