You are here:

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).

 

Read more

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 Drożdż

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.

Varun Ravi Varma

SCIENTIFIC PROGRAMMER

Hi, I am Varun. Post a bachelor’s in Mechanical Engineering from India, I spent a little over two years working as a Data Science and AI consultant for a Microsoft Partner. I then pursued my Masters in Artificial Intelligence from Rijksuniversiteit Groningen, Netherlands, focusing on Reinforcement Learning and Explainable AI using Automata. I have experience with Data Science, Unsupervised Learning Methodologies and Automaton Theory. My current interests include exploring the use of Multi-Agent Systems and Continuous Learning methods in the space of Personal Health.

Maria Gołębiowska

PhD Student

Currently pursuing her PhD at Sano as part of the Personal Health Data Science team. Received her MD degree from the Jagiellonian University Medical College, Bachelor’s degrees in Informatics and Econometrics as well as International Economic Relations from Cracow University of Economics and a Bachelor’s degree in Transatlantic International Relations from Grand Valley State University. Interested in regenerative, preventive and lifestyle medicine.

Luca Gherardini

PhD Student

Luca got a Master of Science in Computer Science from the University of Modena and Reggio Emilia, in Italy. He feeds a deep curiosity for many scientific fields, for which he decided to join Sano in a project on proteins diffusion in Alzheimer and Parkinson diseases. Luca is always focusing on self-development, meditation, and training sessions.

Alfredo Ibias

PostDoc in Personal Health Data Science

Alfredo obtained his M.Sc. in Formal Methods for Computer Science from Universidad Complutense de Madrid (UCM), Spain, where he also obtained both bachelors on Mathematics and Computer Science. In 2022 he defended his thesis in Computer Science and was awarded a Ph.D. degree by the UCM. Before joining Sano, Alfredo was working in applications of Information Theory and Artificial Intelligence to Software Testing, and he did a stay at University College of London under Facebook funding. At Sano, Alfredo will work developing machine self-learning algorithms to generate knowledge graphs from self-reported data. In his spare time he enjoys playing videogames, doing sport and listening to music.

Paulina Komorek

Junior Postdoctoral Researcher in Personal Health Data Science

Paulina obtained her M.Sc. in Biophysics (Medical Physics) from Faculty of Physics, Astronomy and Applied Computer Science at Jagiellonian University. After that Paulina started interdisciplinary PhD studies “Interdisciplinarity for Innovation Medicine” in the area of chemistry and physics at Jerzy Haber Institute of Catalysis and Surface Chemistry Polish Academy of Sciences and at Henryk Niewodniczański Institute of Nuclear Physics Polish Academy of Sciences. Her doctoral project concerns conformational changes in proteins that are of great importance in the development of neurodegenerative diseases, and she carried out part of the project at University of Oldenburg and at Yale Medical School. Paulina defended doctoral degree in September 2022. She also worked as Data Scientist in a start-up dealing with diagnosis the condition of human heart. At Sano Paulina will work on applying data science in personalized medicine. In her free time, she loves travelling and expanding knowledge on the possibilities of supporting medical care by new technologies.

Anna Furgała-Wojas

PostDoc in Clinical Data Science

Anna graduated from Jagiellonian University Medical College in 2016 with a Master of Science in Pharmacy and is currently (November 2022) defended doctoral degree in Department of Pharmacology at the Faculty of Pharmacy, Jagiellonian University. Her doctoral project involved the use of computer tools in behavioral research, thus enabling the improvement of the experimenter's work, the elimination of errors, the unification of expertise as well as the reduction of the number of animals used for behavioral research. She is interested in the use of computer methods in medicine and pharmacy, expanding knowledge, streamlining the process of conducting research at the preclinical stage as well as setting new, engaging goals on her scientific path. Lover of travel, good movies, books and DIY.

Karol Capała

PostDoc in Personal Health Data Science

Karol is an alumnus of physics at the Jagiellonian University. During his bachelor's degree, he was involved in epidemiological modeling, and later devoted time to the study of stochastic processes. He defended, with distinction, his PhD thesis on underdamped Lévy flights. His interests include random walks (the mathematical kind), statistical physics, and population models.

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: