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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.


All Publications

  • Phenotyping severity of patient-centered outcomes using clinical notes: A prostate cancer use case LEARNING HEALTH SYSTEMS Bozkurt, S., Paul, R., Coquet, J., Sun, R., Banerjee, I., Brooks, J. D., Hernandez-Boussard, T. 2020

    View details for DOI 10.1002/lrh2.10237

    View details for Web of Science ID 000548944700001

  • Association between patient-initiated emails and overall 2-year survival in cancer patients undergoing chemotherapy: Evidence from the real-world setting. Cancer medicine Coquet, J. n., Blayney, D. W., Brooks, J. D., Hernandez-Boussard, T. n. 2020


    Prior studies suggest email communication between patients and providers may improve patient engagement and health outcomes. The purpose of this study was to determine whether patient-initiated emails are associated with overall survival benefits among cancer patients undergoing chemotherapy.We identified patient-initiated emails through the patient portal in electronic health records (EHR) among 9900 cancer patients receiving chemotherapy between 2013 and 2018. Email users were defined as patients who sent at least one email 12 months before to 2 months after chemotherapy started. A propensity score-matched cohort analysis was carried out to reduce bias due to confounding (age, primary cancer type, gender, insurance payor, ethnicity, race, stage, income, Charlson score, county of residence). The cohort included 3223 email users and 3223 non-email users. The primary outcome was overall 2-year survival stratified by email use. Secondary outcomes included number of face-to-face visits, prescriptions, and telephone calls. The healthcare teams' response to emails and other forms of communication was also investigated. Finally, a quality measure related to chemotherapy-related inpatient and emergency department visits was evaluated.Overall 2-year survival was higher in patients who were email users, with an adjusted hazard ratio of 0.80 (95 CI 0.72-0.90; p < 0.001). Email users had higher rates of healthcare utilization, including face-to-face visits (63 vs. 50; p < 0.001), drug prescriptions (28 vs. 21; p < 0.001), and phone calls (18 vs. 16; p < 0.001). Clinical quality outcome measure of inpatient use was better among email users (p = 0.015).Patient-initiated emails are associated with a survival benefit among cancer patients receiving chemotherapy and may be a proxy for patient engagement. As value-based payment models emphasize incorporating the patients' voice into their care, email communications could serve as a novel source of patient-generated data.

    View details for DOI 10.1002/cam4.3483

    View details for PubMedID 32986931

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

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