The "Deep Care" concept was developed with the aim of creating synergies between "Artificial Intelligence" (AI) and "Care"—in the sense of "taking care of" and "caring for"—which defines an integrative vision of health that goes far beyond medicine.
The overall objective of the chair is to fulfill the promise of the World Health Organization (WHO) in the Ottawa Charter, which defines health as “[the capacity of] an individual or group [to] identify and realize aspirations, satisfy needs, and adapt to or cope with the environment.” Based on the principles of a predictive, personalized, preventive, and participatory (4P) vision of medicine [Hood L. et al. Science 2004; Zerhouni E. et al. NEJM 2005], the Deep Care Chair posits that AI can play a “deep” role in the integrative vision of healthcare.
The Deep Care Chair focuses its efforts on Grenoble’s strong added value in AI for Health, with the primary objective of exploring the hypothesis that AI can empower health and healthcare systems through the development of AI-based tools and methods. The Deep Care Chair is structured around the following four tasks:
The expected outcomes of the Deep Care initiative include the development of models and tools that will enable everyone to create a participatory health project, based on the genuine empowerment of complex health systems (e.g., more effective collaboration between patients and healthcare teams, more relevant healthcare organizations).
The strength of the Deep Care Chair lies in the vast potential for multidisciplinary and interdisciplinary synergies offered by Grenoble’s academic and industrial environment in the fields of AI and health, as well as in the historical experience and expertise of the TIMC-IMAG laboratory. In addition, 47 researchers and industrial companies make up the Deep Care Chair’s human task force.
Since its creation in June 2019, the Deep Care Chair has already carried out significant research activities, including notable initiatives in multi- and interdisciplinary fields (PREDIMED, one of only four health data lake projects in France authorized by the CNIL; a systematic search for partnerships with clinical units at the Grenoble Alpes University Hospital to align research objectives with real-world needs and innovative models), several research projects have secured funding or established significant partnerships between academic institutions and industrial companies (8 have already been signed, and two additional ones are in the process of being signed), three startups have already been launched, and two patents have been filed.
The Deep Care Chair has already organized or helped organize eight events, including the first Winter School on AI and Health, which brought significant visibility to the chair (438 students registered, including over 100 international students from 31 countries, in addition to French students).
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C. Cancé, C. Lenne, S. Artemova et al. “Hypergraph-Based Data Model for Complex Health Data Exploration and Its Implementation in the PREDIMED Clinical Data Warehouse,” to be published in the MEDINFO2021 proceedings
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K. Charrière, PE Madiot, S. Artemova et al. ODIASP: Clinically Contextualized Image Analysis Using the PREDIMED Clinical Data Warehouse, Toward a Better Diagnosis of Sarcopenia, to be published in the MEDINFO2021 proceedings
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S. Artemova et al., “COVID-19 Geographical Maps and the PREDIMED Clinical Data Warehouse,” to be published in the MEDINGO 2021 proceedings
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J. L. Bosson et al. “A program centered on smart electrically assisted bicycle outings for rehabilitation after breast cancer: a pilot study,” to be published in *Medical Engineering & Physics*, (2022)
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J. L. Bosson and U. von Schenck, “Using Data to Improve the Quality and Safety of Care by Preventing Adverse Events,” symposium “Learning Information Systems for Trusted Decision Support in Healthcare,” Rennes, Nov. 30, 2021
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M. Cuggia and A. Moreau-Gaudry, “Learning Information Systems: A Challenge for Our Healthcare Institutions,” symposium “Learning Information Systems for Reliable Decision Support,” Rennes, November 30, 2021
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Struber, L., Ledouit, S., Daniel, O., Barraud, P-A., & Nougier, V. (2021). Reliability of human running analysis using low-cost inertial and magnetic sensor arrays. Sensors. 21, 13, 15299–15307
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M. Calka, P. Perrier, J. Ohayon, C. Grivot-Boichond, M. Rochette, Y. Payan, “Machine-Learning-Based Model Order Reduction of a Biomechanical Model of the Human Tongue,” *Computer Methods and Programs in Biomedicine*, https://doi.org/10.1016/j.cmpb.2020.105786, (2021)
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Petit P, Bosson-Rieutort D, Maugard C, Gondard E, Ozenfant D, Joubert N, François O, Bonneterre V. The TRACTOR Project: TRACking and MoniToring Occupational Risks in Agriculture Using French Insurance Health Data (MSA). Ann Work Expo Health. September 25, 2021:wxab083.
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F. Ghayem, B. Rivet, R. Cabral Farias, and C. Jutten, “Gradient-based algorithm with spatial regularization for optimal sensor placement,” in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Barcelona, Spain, May 2020, pp. 5655–5659.
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S. Noorzadeh, B. Rivet, and C. Jutten, “3D Interface for P300-Speller BCI,” IEEE Transactions on Human-Machine Systems, 2020,
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C. Dopierala et al., "A New Gastric Impedancemeter for Detecting the Development of Visceral Edema: A Proof-of-Concept Study on Experimental Endotoxemic Shock," 2020. 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada, 2020, pp. 4433–4436.
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F. Ghayem, B. Rivet, R. Cabral Farias, and C. Jutten, “Optimal sensor placement for signal extraction,” in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Brighton, UK, May 2019, pp. 4978–4982.
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Pagonis D, Januel JM, Schieborr U, Mogenet A. Co-presentation (TIMC/Elsevier). Development of a deep learning model to assess patient safety. Symposium on AI, Big Data, and Decision Support in Healthcare: From Theory to Evidence—Scientific and Practical. December 3, 2019, Institut Curie, Paris.
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Achard P, Maugard C, Cancé C, Spinosi J, Ozenfant D, Maître A, Bosson-Rieutort D, Bonneterre V. Combining medical and administrative data with agricultural practices data to retrospectively estimate pesticide use by agricultural workers. J Expo Sci Environ Epidemiol. July 2020;30(4):743-755. doi: 10.1038/s41370-019-0166-x. Epub 2019 Sep 4. PMID: 31484997.
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Cindy Dopierala, Pierre-Yves Guméry, Mohamed-Ridha Frikha, Jean-Jacques Thiébault, Philippe Cinquin, François Boucher. Digital Implantable Gastric Stethoscope for the Detection of Early Signs of Acute Cardiac Decompensation in Patients with Chronic Heart Failure. Medinfo 2019, electronic proceedings.
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C. Dopierala et al., "A novel bimodal stethoscope for gastric collection of heart sounds: preliminary results," 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany, 2019, pp. 4926–4929, doi: 10.1109/EMBC.2019.8857315.
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Christophe Cancé, Pierre-Ephrem Madiot, Christian Lenne, Svetlana Artemova, Brigitte Cohard, Marjolaine Bodin, Alban Caporossi, Jean-François Blatier, Jérôme Fauconnier, Frédérique Olive, Daniel Pagonis, Dominique Le Magny, Jean-Luc Bosson, Katia Charriere, Ivan Paturel, Bruno Lavaire, Gabriel Schummer, Joseph Eterno, Jean-Noël Ravey, Ivan Bricault, Gilbert Ferretti, Sébastien Chanoine, Pierrick Bedouch, Emmanuel Barbier, Julien Thevenon, Pascal Mossuz, Alexandre Moreau-Gaudry, "Cohort Creation and Visualization Using a Graph Model in the PREDIMED Health Data Warehouse." Medinfo 2019, Studies in Health Technology and Informatics.
