#ehealth #innovation #artificialintelligence #datascience

Rémy Choquet, PhD, is a e-health expert for data driven large scale projects. He has an excellent knowledge of local, national and european e-health issues. Besides, he is associated with the biggest e-health research lab in France which collaborates within many national or european H2020 projects.

He has a PhD in Public Health : Epidemiology and Medical data science from the Pierre and Marie Curie University (P6).

He actually is the inovation director at Orange Healthcare and member of the LIMICS lab (U1142) at INSERM. He is an expert in medical data science, semantics, data quality and interoperability in healthcare generated data reuse for public health and research (epidemiology and surveillance systems). He publishes into key domain specific journals and is often appointed as expert for national or european authorities for various e-health topics.

He was co-editor of the clinical research informatics section of the IMIA yearbook of medical informatics (2012-2017). He is french ANR health informatics project external expert reviewer. He participates to several journals or conferences as an external scientific reviewer. He is lecturer for registries and rare diseases. He teaches data science, big data and ontology engineering at the Lyon 2 university.

He’s main interests are : e-health strategy thinking, artificial intelligence, innovation, health information systems, data science, medical informatics, health informatics, clinical research informatics, ontologies, data quality, data integration, semantic interoperability, public health, rare diseases, health IT strategy, management, data privacy.

• A. Weill, R. Choquet, J. Rudant, P. Ricordeau, C. Messiaen, F. Alla, and P. Landais, « Prévalence de la cystinose en France, observance du traitement et coûts directs pour l’assurance maladie : base de données de l’assurance maladie (SNIIRAM) et BNDMR, » in 7e colloque pep adelf afcros, 2015, p. 1–16.
  [Bibtex]

  @inproceedings{Weill2015,
  author = {Weill, A and Choquet, R and Rudant, J and Ricordeau, P and Messiaen, C and Alla, F and Landais, P},
  booktitle = {7e Colloque PEP ADELF AFCROs},
  file = {:Users/remychoquet/Library/Application Support/Mendeley Desktop/Downloaded/Weill et al. - 2015 - Pr{\'{e}}valence de la cystinose en France, observance du traitement et co{\^{u}}ts directs pour l'assurance maladie b.pdf:pdf},
  pages = {1--16},
  title = {{Pr{\'{e}}valence de la cystinose en France, observance du traitement et co{\^{u}}ts directs pour l'assurance maladie : base de donn{\'{e}}es de l'assurance maladie (SNIIRAM) et BNDMR}},
  year = {2015}
  }

• R. Choquet, M. Maaroufi, A. de Carrara, C. Messiaen, E. Luigi, and P. Landais, « A methodology for a minimum data set for rare diseases to support national centers of excellence for healthcare and research., » Journal of the american medical informatics association : jamia, p. 1–7, 2015.
  [Bibtex]

  @article{Choquet2014b,
  abstract = {BACKGROUND: Although rare disease patients make up approximately 6-8{\%} of all patients in Europe, it is often difficult to find the necessary expertise for diagnosis and care and the patient numbers needed for rare disease research. The second French National Plan for Rare Diseases highlighted the necessity for better care coordination and epidemiology for rare diseases. A clinical data standard for normalization and exchange of rare disease patient data was proposed. The original methodology used to build the French national minimum data set (F-MDS-RD) common to the 131 expert rare disease centers is presented. METHODS: To encourage consensus at a national level for homogeneous data collection at the point of care for rare disease patients, we first identified four national expert groups. We reviewed the scientific literature for rare disease common data elements (CDEs) in order to build the first version of the F-MDS-RD. The French rare disease expert centers validated the data elements (DEs). The resulting F-MDS-RD was reviewed and approved by the National Plan Strategic Committee. It was then represented in an HL7 electronic format to maximize interoperability with electronic health records. RESULTS: The F-MDS-RD is composed of 58 DEs in six categories: patient, family history, encounter, condition, medication, and questionnaire. It is HL7 compatible and can use various ontologies for diagnosis or sign encoding. The F-MDS-RD was aligned with other CDE initiatives for rare diseases, thus facilitating potential interconnections between rare disease registries. CONCLUSIONS: The French F-MDS-RD was defined through national consensus. It can foster better care coordination and facilitate determining rare disease patients' eligibility for research studies, trials, or cohorts. Since other countries will need to develop their own standards for rare disease data collection, they might benefit from the methods presented here.},
  author = {Choquet, R{\'{e}}my and Maaroufi, Meriem and de Carrara, Albane and Messiaen, Claude and Luigi, Emmanuel and Landais, Paul},
  doi = {10.1136/amiajnl-2014-002794},
  file = {:Users/remychoquet/Library/Application Support/Mendeley Desktop/Downloaded/Choquet et al. - 2015 - A methodology for a minimum data set for rare diseases to support national centers of excellence for healthcare.pdf:pdf},
  issn = {1527-974X},
  journal = {Journal of the American Medical Informatics Association : JAMIA},
  pages = {1--7},
  pmid = {25038198},
  title = {{A methodology for a minimum data set for rare diseases to support national centers of excellence for healthcare and research.}},
  url = {http://www.ncbi.nlm.nih.gov/pubmed/25038198},
  year = {2015}
  }

• R. Choquet and P. Landais, « The French national registry for rare diseases: an integrated model from care to epidemiology and research, » Orphanet journal of rare diseases, vol. 9, iss. Suppl 1, p. O7, 2014.
  [Bibtex]

  @article{Choquet2014a,
  author = {Choquet, R{\'{e}}my and Landais, Paul},
  doi = {10.1186/1750-1172-9-S1-O7},
  file = {:Users/remychoquet/Library/Application Support/Mendeley Desktop/Downloaded/Choquet, Landais - 2014 - The French national registry for rare diseases an integrated model from care to epidemiology and research.pdf:pdf},
  issn = {1750-1172},
  journal = {Orphanet Journal of Rare Diseases},
  number = {Suppl 1},
  pages = {O7},
  title = {{The French national registry for rare diseases: an integrated model from care to epidemiology and research}},
  url = {http://www.ojrd.com/content/9/S1/O7},
  volume = {9},
  year = {2014}
  }

• D. Taruscio, L. Vittozzi, R. Choquet, K. Heimdal, G. Iskrov, Y. Kodra, P. Landais, M. Posada, R. Stefanov, C. Steinmueller, E. Swinnen, and H. {Van Oyen}, « National Registries of Rare Diseases in Europe: An Overview of the Current Situation and Experiences, » Public health genomics, 2014.
  [Bibtex]

  @article{Taruscio2014,
  author = {Taruscio, D. and Vittozzi, L. and Choquet, R. and Heimdal, K. and Iskrov, G. and Kodra, Y. and Landais, P. and Posada, M. and Stefanov, R. and Steinmueller, C. and Swinnen, E. and {Van Oyen}, H.},
  doi = {10.1159/10.1159/000365897},
  issn = {1662-8063},
  journal = {Public Health Genomics},
  keywords = {EPIRARE,Epidemiology,Public health,Rare diseases,Registries},
  language = {english},
  publisher = {Karger Publishers},
  title = {{National Registries of Rare Diseases in Europe: An Overview of the Current Situation and Experiences}},
  url = {http://www.karger.com/Article/FullText/365897},
  year = {2014}
  }

• F. Dhombres, P. Vandenbussche, A. Rath, M. Hanauer, A. Orly, B. Urbero, R. Choquet, and J. Charlet, « OntoOrpha : an ontology to support the editing and audit of rare diseases knowledge in Orphanet, » in International conference of biomedical ontology (icbo 2011), 2011.
  [Bibtex]

  @inproceedings{75,
  abstract = {Orphanet is the reference information portal on rare diseases and orphan drugs for all audience (for both healthcare professionals and general public). Orphanet is led by a large European consortium of around 40 countries, coordinated by the French INSERM team which is responsible for the infrastructure of Orphanet, the management tools, the quality control, the rare diseases inventory, the classifications and the edition of the encyclopedia*.
  After ten years of evolution, current Orphanet tools are limited in efficiently supporting the editing, update and data sharing processes of a constantly growing rare diseases knowledge (6000 rare diseases with annotations and more than one hundred overlapping classifications).},
  author = {Dhombres, Ferdinand and Vandenbussche, Pierre-Yves and Rath, Anna and Hanauer, Marc and Orly, Annie and Urbero, Bruno and Choquet, Remy and Charlet, Jean},
  booktitle = {International Conference of Biomedical Ontology (ICBO 2011)},
  title = {{OntoOrpha : an ontology to support the editing and audit of rare diseases knowledge in Orphanet}},
  year = {2011}
  }

• D. Schober, M. Boeker, J. Bullenkamp, C. Huszka, K. Depraetere, D. Teodoro, N. Nadah, R. Choquet, C. Daniel, and S. Schulz, « The DebugIT core ontology: semantic integration of antibiotics resistance patterns., » Studies in health technology and informatics, vol. 160, p. 1060–1064, 2010.
  [Bibtex]

  @article{66,
  abstract = {Antibiotics resistance development poses a significant problem in today{\{}$\backslash$textquoteright{\}}s hospital care. Massive amounts of clinical data are being collected and stored in proprietary and unconnected systems in heterogeneous format. The DebugIT EU project promises to make this data geographically and semantically interoperable for case-based knowledge analysis approaches aiming at the discovery of patterns that help to align antibiotics treatment schemes. The semantic glue for this endeavor is DCO, an application ontology that enables data miners to query distributed clinical information systems in a semantically rich and content driven manner. DCO will hence serve as the core component of the interoperability platform for the DebugIT project. Here we present DCO and an approach thet uses the semantic web query language SPARQL to bind and ontologically query hospital database content using DCO and information model mediators. We provide a query example that indicates that ontological querying over heterogeneous information models is feasible via SPARQL construct- and resource mapping queries.},
  author = {Schober, Daniel and Boeker, Martin and Bullenkamp, Jessica and Huszka, Csaba and Depraetere, Kristof and Teodoro, Douglas and Nadah, Nadia and Choquet, Remy and Daniel, Christel and Schulz, Stefan},
  issn = {0926-9630},
  journal = {Studies in health technology and informatics},
  keywords = {Databases,Drug Resistance,Factual,Information Storage and Retrieval,Internet,Microbial,Semantics,Software},
  pages = {1060--1064},
  title = {{The DebugIT core ontology: semantic integration of antibiotics resistance patterns.}},
  volume = {160},
  year = {2010}
  }

• R. Choquet, S. Qouiyd, D. Ouagne, E. Pasche, C. Daniel, O. Boussaïd, and M. Jaulent, « The Information Quality Triangle: a methodology to assess clinical information quality., » Studies in health technology and informatics, vol. 160, p. 699–703, 2010.
  [Bibtex]

  @article{68,
  abstract = {Building qualitative clinical decision support or monitoring based on information stored in clinical information (or EHR) systems cannot be done without assessing and controlling information quality. Numerous works have introduced methods and measures to qualify and enhance data, information models and terminologies quality. This paper introduces an approach based on an Information Quality Triangle that aims at providing a generic framework to help in characterizing quality measures and methods in the context of the integration of EHR data in a clinical datawarehouse. We have successfully experimented the proposed approach at the HEGP hospital in France, as part of the DebugIT EU FP7 project.},
  author = {Choquet, R{\'{e}}my and Qouiyd, Samiha and Ouagne, David and Pasche, Emilie and Daniel, Christel and Boussa{\"{i}}d, Omar and Jaulent, Marie-Christine},
  issn = {0926-9630},
  journal = {Studies in health technology and informatics},
  keywords = {Clinical,Decision Support Systems,Delivery of Health Care,Electronic Health Records,France,Health Care,Models,Organizational,Quality Assurance},
  pages = {699--703},
  title = {{The Information Quality Triangle: a methodology to assess clinical information quality.}},
  volume = {160},
  year = {2010}
  }