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the repository for Open Questions, Challenges and Ressources of the

Personalized Health phYSIcally, sOcially and Mentally for Each in their networkS

Great Challenge[edit | edit source]

Description[edit | edit source]

PHYSIOMES (Personalized Health phYSIcally, sOcially and Mentally for Each in her/his networkS) This great challenge aims at involving each one within his/her best own wellbeing trajectory as well as all the health actors to such objective, according to the Sustainable Development Goal 3 and the WHO resolutions. It gives a special attention to the “extended personalized physiome” by taking into account the interactions of each one in her/his networks. Following the last resolutions of WHO, traditional medicines around the world will be also studied in the perspective of personalized health, including the surveillance and education of the fragile person on her/his place of life. This flagship will use specific version of SIRE, RAPSODY and POEM. It will have specific focus on epidemiology of the main diseases (biologically contagious like bacterial or viral diseases, socially contagious like obesity and diabetes, genetic diseases, cancer, etc.).

Keywords[edit | edit source]

personalised health; patient empowerment; patient centred care network

Board[edit | edit source]

  • Jacques Demongeot (chair | email)
  • Pierre Parrend (co-chair)
  • Pierre Collet (co-chair)
  • Nadine Peyriéras (co-chair)

Bibliography[edit | edit source]

  • The Sustainable Development Goal 3 aims to ensure healthy lives and promote well-being for all at all ages. In its target 3.8, it addresses universal health coverage including financial risk protection, access to quality essential health care services and access to safe, effective, quality and affordable essential medicines and vaccines for all.
  • Strengthening integrated, people-centred health services : Report WHA69.24 of the 65th World Health Assembly, May 2016

Challenges[edit | edit source]

The smart contract for personalized health (e-team)[edit | edit source]

Description[edit | edit source]

Figure 1 The medical application by Humetrix® called iBlueButton is one of the most attractive product developed in the Obamacare. It allows to constitute the personalized medical history of a person and locate it on a smart phone to be communicated to any actor of the e-health system

Figure 2 Along the life applications serve to follow the medical history by storing data and their interpretation by medical and paramedical actors

Figure 3 The life cycle of medical data from the creation of a phenotypic observation (clinical data) to the modelling of the physiological and genetic processes at the origin of the disease (simulation data

Figure 4 The "tsunami" of medical data

Figure 5 Acquisition of activity and physiological personal data using exo- (on walls and furnitures at home) and endo-sensors (on or inside the body) in a e-health patient-centred system

This challenge is the creation of a huge social network of humans 'wishing to share their health personal data with science as a smart contract' for a 'social learning' that return 1. 'personal empowerment of her/his own health through personalized advices' and 2. a revenue complement from the DaD (Dapp Data) market through the scientific anonymous exploitation of these smart data for stakeholder customised studies. The 2nd internet revolution is required for sharing worldwide the health trajectories in the respect of privacy, in a sense close to the EU GDPR (Global Data Protection Regulation). The aim is to connect all the physiological measurement devices as IoT inside the InterPlanetary File System (IPFS) through adequate Dapps as well as the records of all health actors about patients and their systemic networking effects.

This initiative is especially concerned by new IoT like connected clock, balance, activity registering, food consuming, a very low costly echo-graph used by groups of pregnant mothers, etc. The involvement of group of patients belonging to associations of persons suffering the same pathology (like type II diabetes) or to health organisations coming from the non-profit mutualist world (very present in Europe, but also in USA with Medicare and Medicaid, even the politic origin of these organisations differs) will be an important factor of success of the present challenge. For example, the Humetrix® product iBlueButton proposes in the Obamacare framework all the services adapted to the integration in a smart patient file inside the smart phone of all health data needed for cure, care and rehabilitation needs.

Members[edit | edit source]

  • Jacques Demongeot (chair)
  • Pierre Parrend (co-chair)

Bibliography[edit | edit source]

Personal extended physiome for personalized health (e-team)[edit | edit source]

Description[edit | edit source]

Figure 6 The challenge needs a collaboration between different scientific and medical communities, i.e., the researchers in biomedicine, the clinicians and the computer scientists

Figure 7 The patient centred e-health needs the participation af all actors of the health systems, from the patient and his/her family at home until the professionals of the medical and paramedical worlds

Figure 8 The data are coming from different origines, for example from different types of exo- or endo-sensors exploring the different physical fields (thermic, electro-magnetic, gravitational, acoustic, etc.) and hence, they need fusion procedures in order to trigger alerts through specific scores

Figure 9 The genetic, metabolic and social networks related to the patient at home are constructed from the raw data coming from sensors and information given by the actors of the patient centred health system

The 'personalized physiome' is proposing a specific version of RAPSODY for the best possible prediction in probability on remaining lifelong health trajectory using the past previous trajectories of older people. It will propose an “Integrative Knowledge Map on biological systems along phylogenesis” using the research in biology and medicine with increasing level of refinements in order to be adapted to all ages. It will make strong links with the international research programme about the “Physiome”, defined as the multilevel dynamics from the initial egg to the death.

For example, any advance in understanding the cell functioning must be formalized by finding a high level of formal description of biochemical, mechanical, signalling processes used in cell biology, from the genome expression to cell motion and tissue morphogenesis. This close coupling between data, knowledge and models will allow us to come up with missing elements ensuring the coherence and the pertinence of the explanation of observed pathological phenomena in order to increase the efficiency of further medical actions. A bridge between the cell and molecular level, on the one hand, and higher levels of organization, on the other hand, has to be built. The elucidation of these interlevel processes relies on structural and functional information which includes membrane features, channel kinetics, transporter characteristics, metabolic network topology as well as tissue properties (architecture, elasticity, fluid dynamic, etc.). Such an ambitious perspective emphasizes that the future, and major challenge, in medicine is to move from diagnosis to disease prediction and prevention. It requires new means to detect very early the onset of a given pathology.

This new way to conceive medicine needs the participation of all actors of the health system, in a common approach called the 5P medicine, involving at different levels:

- Pluri-expertise with experts of various biological, medical and paramedical fields (from fundamentalists to clinicians), - Prediction using the omics to anticipate the occurrence of the pathologies at adult and ageing stages - Prevention for avoiding the pathologies predicted - Participation with an increase of the patient empowerment - Personalization using specific data acquired by exo- or endo-sensors on the patient and restituted to him for their first assessment.

This 5P medicine (fig. 9) is implemented in 3 dimensions, Physic, Mental and Social and can be exerted at home as well as in hospital in ambulatory mode. The e-health allows indeed the construction of a patient centred information system merging (as in the US system Humetrix®) all the data acquired on him by exo- and endo-sensors, but also all information emerging from these raw data (from raw data to high level diagnostic and pronostic interpretation coming from the medical and paramedical worlds).

Members[edit | edit source]

  • Jacques Demongeot (chair)
  • Nadine Peyriéras (co-chair)
  • Pierre Parrend (co-chair)
  • Slimane B. MILED (co-chair)
  • Yana KOROBKO (co-chair)
  • Carla TARAMASCO (co-chair)
  • Jeffrey JOHNSON (chair)
  • Jorge LOUCA (co-chair)
  • Fatima OULEBSIR-BOUMGHAR (chair)

Bibliography[edit | edit source]

  • J. DEMONGEOT, J. BEZY-WENDLING, J. MATTES, P. HAIGRON, N. GLADE & J.L. COATRIEUX: Multiscale Modeling and Imaging: The Challenges of Biocomplexity. Proceedings of the IEEE Society, 91, 1723-1737 (2003).
  • J. DEMONGEOT, A. HAMIE, O. HANSEN, C. FRANCO, B. SUTTON & E.P. COHEN: Dynalets: a new method of modelling and compressing biological signals. Applications to physiological and molecular signals. Comptes Rendus Biologies, 337, 609-624 (2014).
  • J. DEMONGEOT, O. HANSEN & A. HAMIE: Dynalets : a new tool for biological signal processing. In: Medicon’13, IFBME Proceedings 41, Springer Verlag, New York, 1250-1253 (2014).

Patient education for patient empoverment (e-team)[edit | edit source]

Description[edit | edit source]

Figure 10 A personalized coaching permits to give the preventive and therapeutic education adapted to the precise needs of the patient athe right time and place

Figure 11 Personalized serious games can help for example the patient to choose her/his food depending on the efforts he has to produce during a given day

For each physical, mental or social problem, the previous challenge is providing a probabilistic predictive model including the ranking

Each individuated health trajectory with its physical, mental or social problems belongs to exchangeable cohorts corresponding the each of these problems. For each cohort, the 'antisymmetric MAJ Dapp' will provide for each individuated trajectory with its 'ELO' ranking (like in Chess) some advices that are a little more difficult than his/her 'ELO' ranking. Thus the empowerment is as quick as possible toward the resilience of each individuated trajectory.

Each day, the patients which are not convinced by the advices for a same problem, he can ask her tutor or, when the problem is more difficult, she can co-organize a e-meeting with others and their tutors for solving this problem. The personalized solutions are again graded and the grades entails a modification of the 'advice Dapp'. All these interactions remain personal data for the patient and the tutor.

The Health Knowledge Map is proposing knowledge about the health problem version as well as about the advices towards a better wellbeing, in the sense of the « extended personalized physiome » (physical, mental and social dimensions).

Members[edit | edit source]

  • Pierre Collet (chair)
  • Jacques Demongeot (co-chair)
  • Pierre Parrend (co-chair)

Bibliography[edit | edit source]

J. DEMONGEOT, A. ELENA, C. TARAMASCO & N. VUILLERME Serious games and personalization of the therapeutic education. In: ICOST’15, Lecture Notes in Comp. Sci., 9102, 270-281 (2015).

Obesity & diabet (e-team)[edit | edit source]

Description[edit | edit source]

Figure 12 The obesity is a pandemics all around the world concerning all developed as well as emergent countries

Figure 13 A fine analysis of the social networks concerned by obesity allows to find critical individuals (nodes of the social network) who will be optimal targets of the preventive education (for example the obese individuals having a lot of friends)

This challenge aims to give a preventive and therapeutic education, with biofeedback and personalization, to the chronic patient suffering obesity and its main complication, type II diabetes, inside his social network. The techniques proposed are:

  • serious games in their pedagogical dimension of elaboration of therapeutic education scenarios and their local dimension of information capture using specific sensors and biofeedback processes allowing the customization of the game, and
  • tools of visualization of social networks to which the patient belongs, in order to bring him to an awareness of belonging to a community sharing same pathology and therapy.

Three scenarios will be developed, dealing with

  • the dietary of a type II diabetic patient,
  • the detection and monitoring of the diabetic retinitis and
  • the detection and monitoring of diabetic foot ulcers.

Members[edit | edit source]

  • Jacques Demongeot (chair)
  • Carla Taramasco (co-chair)

Bibliography[edit | edit source]

  • J. DEMONGEOT, A. ELENA, C. TARAMASCO & N. VUILLERME: Serious game as new health telematics tool for patient therapy education: the example of obesity and type 2 diabetes. Lecture Notes in Computer Science, 7910, 187-194 (2013).
  • J. DEMONGEOT & C. TARAMASCO: Evolution of social networks: the example of obesity. Biogerontology, 15, 611-626 (2014).
  • J. DEMONGEOT, O. HANSEN & C. TARAMASCO: Complex systems and contagious social diseases: example of obesity. Virulence, 7, 129-140 (2015).
  • J. DEMONGEOT, A. ELENA, M. JELASSI, S. BEN MILED, N. BELLAMINE BEN SAOUD & C. TARAMASCO: Smart Homes and Sensors for Surveillance and Preventive Education at Home: Example of Obesity. Information, 7, 50 (2016).
  • J. DEMONGEOT, M. JELASSI & C. TARAMASCO: From Susceptibility to Frailty in social networks: the case of obesity. Math. Pop. Studies 24, 219-245 (2017).
  • J. DEMONGEOT, M. JELASSI, H. HAZGUI, S. BEN MILED, N. BELLAMINE BEN SAOUD & C. TARAMASCO: Biological Networks Entropies: Examples in Neural Memory Networks, Genetic Regulation Networks and Social Epidemic Networks. Entropy 20, 36 (2018).

HOPE: Health Optimisation through 4P Ecosystems (e-team)[edit | edit source]

Description[edit | edit source]

HOPE is a platform for managing complex patient cohorts. It supports integration, analysis and exploitation of medical cohort data. HOPE supports massive as well as very reduced cohorts: The Jiva cohort entails 500.000 Indian patients treated with traditional therapeutics, whereas some cohorts of the Genida suite entails a single-digit quantity of patients. Genida patients are mostly affected by de novo genetic diseases, i.e. neither the mother nor the father are affected. This is for instance the case for gene triplication where a given erroneous gene is present in 3 copies.

The scientific challenges of HOPE are:

  • Accompanying medical cohorts to support integration, analysis and exploitation of data by biologists and medical practitionners
  • Developping suitable software tools to support saving, backup, analysis and exploitation of these data while staying compliant to legal constraints such as "Computer and Liberty" laws, as well as with technical constraints bound with data backup and security. These constraints are strongly dependent on the country where the tools are deployed. Some strongly regulated geographical area like European Union enforce rigourous ethical and legal processes; emergent countries do not always robust technical infrastructure available and are more open to innovative solutions solving these issues.

Ongoing project in the context of HOPE are:

  • The development of an integration system for managing cohorts and exploiting patient data in a 4P environent (contact: Julie Thompson)
  • The development of access control models specific to medical applications using genetic information (contact: Pierre Parrend)
  • Structuration of raw data into scientifically analysable medical cohorts in the context of the RADAR tool (Contact : Anne Jeannin)
  • Optim’R BICS, statistical analysis of medical data (Contact : Erik-André Sauleau)

The main cohorts handled in the context of HOPE are

  • Genida (meta-cohort with 32 different affections)
  • Jiva
  • Profamily network

The development of the tools of the HOPE platform are performed according to 3 axes:

  • A suite of robust platforms for managing patient data
  • A suite of software and tools for enforcing the security of medical applications
  • A suite of software and tools for for analysis massive health data to support 4P care cascade

Members[edit | edit source]

involved teams:

  • Pierre Collet, Olivier Poch, Julie Thompson, Pierre Parrend, Anne Jeannin (team CSTB)
  • Florent Colin, Timothée Mazzucotelli (Jean-Louis Mandel e-team, projet GENIDA)
  • Érik-André Sauleau (team IMAGeS)

Bibliography[edit | edit source]

  • P. Parrend, T. Mazzucotelli, F. Colin , P. Collet, J-L. Mandel, Cerberus, an Access Control Scheme for Enforcing Least Privilege in Patient Cohort Study Platforms, Journal of Medical Systems, Springer Verlag (Germany) ( IF : 2.456, SNIP : 1.292, SJR : 0.501 ), Volume 42, n° 1, janvier 2018, doi:10.1007/s10916-017-0844-y
  • P. Parrend, A. Kress, E-A. Sauleau, A. Jeannin, F. Colin , J-L. Mandel , P. Collet, Global data and local action for wellbeing science: when 4P-medicine meets health self-assessment, Conference on Complex Systems (CCS2018), Digital Epidemiology and Surveillance Satellite, Thessaloniki, Greece, Complex System Society (Eds.), septembre 2018
  • A. Kress, E-A. Sauleau, A. Jeannin, F. Colin , J-L. Mandel , P. Collet, P. Parrend, HOPE: Emergent solutions for analysis and management of patient cohort data, Conference on Complex Systems (CCS2018), Thessaloniki, Greece, Complex System Society (Eds.), janvier 2018
  • J-L. Mandel , F. Colin , T. Mazzucotelli, P. Parrend, Genida, une base de données participative et internationale pour une meilleure connaissance des nombreuses formes génétiques de déficience intellectuelle et troubles autistiques, Du malade passif au patient expert, - (Eds.), Chapitre. -, pages 87-93, Edition de santé, 2018
  • F. Colin , T. Mazzucotelli, P. Parrend, A. Deruyver, J-L. Mandel, GenIDA: a social network and database to inform on natural history of monogenic forms of intellectual disability and autism, European Human Genetics Conference, Bristol, United Kingdom, European Society of Human Genetics (Eds.), juin 2015

Data and/or tools to be shared[edit | edit source]

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