I received my PhD from University of Rennes 1, where I was working in the Dyliss team (DYnamics, Logics and Inference for biological Systems and Sequences), at the INRIA institute (Rennes, France). My research area have focused on making sense of unconventional and complex wide data biological sets, such as signaling pathways, gene interactions, or more recently, Electronical Health Records (EHRs). In Boussard Lab, my research is to establish different novel strategies for the evaluation the quality healthcare delivery, involving machine learning and Natural Language Processing (NLP) methods.

Professional Education

  • Bachelor of Science, Universite De Rennes (2012)
  • Master of Science, Universite De Rennes (2014)
  • Doctor of Philosophy, Universite De Rennes (2017)

Lab Affiliations

All Publications

  • Comparison of Orthogonal NLP Methods for Clinical Phenotyping and Assessment of Bone Scan Utilization among Prostate Cancer Patients. Journal of biomedical informatics Coquet, J., Bozkurt, S., Kan, K. M., Ferrari, M. K., Blayney, D. W., Brooks, J. D., Hernandez-Boussard, T. 2019: 103184


    Clinical care guidelines recommend that newly diagnosed prostate cancer patients at high risk for metastatic spread receive a bone scan prior to treatment and that low risk patients not receive it. The objective was to develop an automated pipeline to interrogate heterogeneous data to evaluate the use of bone scans using a two different Natural Language Processing (NLP) approaches.Our cohort was divided into risk groups based on Electronic Health Records (EHR). Information on bone scan utilization was identified in both structured data and free text from clinical notes. Our pipeline annotated sentences with a combination of a rule-based method using the ConText algorithm (a generalization of NegEx) and a Convolutional Neural Network (CNN) method using word2vec to produce word embeddings.A total of 5,500 patients and 369,764 notes were included in the study. A total of 39% of patients were high-risk and 73% of these received a bone scan; of the 18% low risk patients, 10% received one. The accuracy of CNN model outperformed the rule-based model one (F-measure = 0.918 and 0.897 respectively). We demonstrate a combination of both models could maximize precision or recall, based on the study question.Using structured data, we accurately classified patients' cancer risk group, identified bone scan documentation with two NLP methods, and evaluated guideline adherence. Our pipeline can be used to provide concrete feedback to clinicians and guide treatment decisions.

    View details for PubMedID 31014980

  • KaSa: A Static Analyzer for Kappa Computational Methods in Systems Biology Boutillier, P., Camporesi, F., Coquet, J., Feret, J., Quyên Lý, K., Theret, N., Vignet, P. Springer International Publishing. 2018: 285–291
  • Identifying Functional Families of Trajectories in Biological Pathways by Soft Clustering: Application to TGF-β Signaling Computational Methods in Systems Biology Coquet, J., Theret, N., Legagneux, V., Dameron, O. Springer International Publishing. 2017: 91–107
  • The smell of us – crowdsourcing human body odor evaluation Human Computation 3 Benony, M., Cardon, M., Ferré, A., Coquet, J., et al 2016; 1: 161-179

    View details for DOI 10.15346/hc.v3i1.9