Nos publications

@article{Huguet2025,

title = {Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow},

author = {Marius Huguet and Canan Pehlivan and François Ballereau and Antoine Dodane-Loyenet and Franck Fontanili and Thierry Garaix and Youri Yordanov and Vincent Augusto and Karim Tazarourte and Abdesslam Redjaline},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105000073700&doi=10.1038%2fs43856-025-00793-y&partnerID=40&md5=0c54a21314363953d989ec26aab4c7b5},

doi = {10.1038/s43856-025-00793-y},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {Communications Medicine},

volume = {5},

number = {1},

note = {Cited by: 0; All Open Access, Gold Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{PERROT2025,

title = {Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France},

author = {Aurore Perrot and Fanny Raguideau and Helene Denis and Martin Prodel and Matthieu Javelot and Marie Pierres and Cyrille Touzeau},

url = {https://www.sciencedirect.com/science/article/pii/S2152265025000801},

doi = {https://doi.org/10.1016/j.clml.2025.03.004},

issn = {2152-2650},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {Clinical Lymphoma Myeloma and Leukemia},

abstract = {Background 

In a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. 

Methods 

The present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. 

Results 

The 5-year OS rate is estimated at 51.9% (95% CI: 51.2–52.6). This rate is 78.3% (95% CI: 77.1–79.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8–44.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. 

Conclusion 

These results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.},

keywords = {biostatistics, pathways},

pubstate = {published},

tppubtype = {article}

}

@article{luu2025lucien,

title = {LUCIEN–Étude non interventionnelle sur les délais et les coûts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining)},

author = {VP Luu and Hélène Denis and L Bensimon and I Durand-Zaleski and L Greillier and P Thomas and M Wislez and A Panes and Martin Prodel and M Apert and others},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {Revue des Maladies Respiratoires Actualités},

volume = {17},

number = {1},

pages = {245–246},

publisher = {Elsevier Masson},

keywords = {pathways, process mining},

pubstate = {published},

tppubtype = {article}

}

@article{Gonzalez2024,

title = {Prediction of pharmaceutical and nonpharmaceutical expenditures associated with Diabetes Mellitus type II based on clinical risk},

author = {Javier-Leonardo -Rodriguez Gonzalez and Carlos Franco and Olga Pinzón-Espitia and Vicent Caballer and Edgar Alfonso-Lizarazo and Vincent Augusto},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195037874&doi=10.1371%2fjournal.pone.0301860&partnerID=40&md5=d0b89241ceabcb96f1f6fc32085e589a},

doi = {10.1371/journal.pone.0301860},

year  = {2024},

date = {2024-01-01},

journal = {PLoS ONE},

volume = {19},

number = {6 June},

note = {Cited by: 0; All Open Access, Gold Open Access, Green Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Uhl2024,

title = {Evaluating the Bias in Hospital Data: Automatic Preprocessing of Patient Pathways Algorithm Development and Validation Study},

author = {Laura Uhl and Vincent Augusto and Benjamin Dalmas and Youenn Alexandre and Paolo Bercelli and Fanny Jardinaud and Saber Aloui},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206501788&doi=10.2196%2f58978&partnerID=40&md5=ba7dc6edb8480a6c38aff3f1810f7ff4},

doi = {10.2196/58978},

year  = {2024},

date = {2024-01-01},

journal = {JMIR Medical Informatics},

volume = {12},

note = {Cited by: 0; All Open Access, Gold Open Access, Green Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{dauphinot2024identification,

title = {Identification of profiles associated with conversions between the Alzheimer’s disease stages, using a machine learning approach},

author = {Virginie Dauphinot and Marie Laurent and Martin Prodel and Alexandre Civet and Alexandre Vainchtock and Claire Moutet and Pierre Krolak-Salmon and Antoine Garnier-Crussard},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {Alzheimer's Research & Therapy},

volume = {16},

number = {1},

pages = {166},

publisher = {BioMed Central London},

keywords = {machine learning},

pubstate = {published},

tppubtype = {article}

}

@article{de2024correction,

title = {Correction: Explaining predictive factors in patient pathways using autoencoders},

author = {Hugo De Oliveira and Martin Prodel and Ludovic Lamarsalle and Vincent Augusto and Xiaolan Xie},

year  = {2024},

date = {2024-01-01},

urldate = {2024-01-01},

journal = {PloS one},

volume = {19},

number = {12},

pages = {e0315113},

keywords = {auto-encoders, pathways},

pubstate = {published},

tppubtype = {article}

}

@article{Lequertier2024225,

title = {Length of Stay Prediction with Standardized Hospital Data from Acute and Emergency Care Using a Deep Neural Network},

author = {Vincent Lequertier and Tao Wang and Julien Fondrevelle and Vincent Augusto and Stéphanie Polazzi and Antoine Duclos},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187543743&doi=10.1097%2fMLR.0000000000001975&partnerID=40&md5=84afd026ed5e2d2eb1d23152e5ec3085},

doi = {10.1097/MLR.0000000000001975},

year  = {2024},

date = {2024-01-01},

journal = {Medical Care},

volume = {62},

number = {4},

pages = {225 – 234},

note = {Cited by: 2},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Peng20244364,

title = {Optimal Process Mining of Traces with Events and Transition Attributes with Application to Care Pathways of Cancer Patients},

author = {Zhihao Peng and Vincent Augusto and Lionel Perrier and Xiaolan Xie},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165398199&doi=10.1109%2fTASE.2023.3295947&partnerID=40&md5=8c10f6733aeb6a0ddac21c29ac9ce5ef},

doi = {10.1109/TASE.2023.3295947},

year  = {2024},

date = {2024-01-01},

journal = {IEEE Transactions on Automation Science and Engineering},

volume = {21},

number = {3},

pages = {4364 – 4381},

note = {Cited by: 2; All Open Access, Hybrid Gold Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{DeOliveira2024e0315113,

title = {Erratum: Correction: Explaining predictive factors in patient pathways using autoencoders (PloS one (2022) 17 11 DOI: 10.1371/journal.pone.0277135)},

author = {Hugo De Oliveira and Martin Prodel and Ludovic Lamarsalle and Vincent Augusto and Xiaolan Xie},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211410722&doi=10.1371%2fjournal.pone.0315113&partnerID=40&md5=6025795bd011eaf99ab0cadcc87a650d},

doi = {10.1371/journal.pone.0315113},

year  = {2024},

date = {2024-01-01},

journal = {PloS one},

volume = {19},

number = {12},

pages = {e0315113},

note = {Cited by: 0; All Open Access, Gold Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{LeLay20243080,

title = {COVID-19 Bed Management Using a Two-Step Process Mining and Discrete-Event Simulation Approach},

author = {Jules Le Lay and Vincent Augusto and Edgar Alfonso-Lizarazo and Malek Masmoudi and Baptiste Gramont and Xiaolan Xie and Bienvenu Bongue and Thomas Celarier},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85160232631&doi=10.1109%2fTASE.2023.3274847&partnerID=40&md5=5a55e8579bdbbf60985bc8f1d9e63da8},

doi = {10.1109/TASE.2023.3274847},

year  = {2024},

date = {2024-01-01},

journal = {IEEE Transactions on Automation Science and Engineering},

volume = {21},

number = {3},

pages = {3080 – 3091},

note = {Cited by: 1; All Open Access, Green Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{laurent2use,

title = {Use of a Clustering Algorithm for Treatment Sequences (TAK®) as a Pre-processing Step to Explanatory and Predictive Models},

author = {Marie Laurent and Fanny Raguideau and Martin Prodel and Nada Assi and Eléonore Herquelot and Benjamin Grenier},

year  = {2023},

date = {2023-08-23},

urldate = {2023-08-23},

journal = {Group},

volume = {2},

number = {1.6},

pages = {1–8},

keywords = {machine learning},

pubstate = {published},

tppubtype = {article}

}

@article{Fleck2023,

title = {Adjuvant therapeutic strategy decision support for an elderly population with localized breast cancer: A monocentric cohort retrospective study},

author = {Julia L. Fleck and Daniëlle Hooijenga and Raksmey Phan and Xiaolan Xie and Vincent Augusto and Pierre-Etienne Heudel},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168746984&doi=10.1371%2fjournal.pone.0290566&partnerID=40&md5=b4c794b47cbb4008a24a4495cb1df548},

doi = {10.1371/journal.pone.0290566},

year  = {2023},

date = {2023-01-01},

journal = {PLoS ONE},

volume = {18},

number = {8 AUGUST},

note = {Cited by: 0; All Open Access, Gold Open Access, Green Open Access},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{laurent2023modelisation,

title = {Modélisation des parcours de soins dans la maladie d'Alzheimer: utilisation d'une méthode de «process mining»},

author = {Marie Laurent and Martin Prodel and Alexandre Civet and Alexandre Vainchtock and Claire Moutet and Virginie Dauphinot and Pierre Krolak-Salmon},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {Revue d'Épidémiologie et de Santé Publique},

volume = {71},

pages = {101444},

publisher = {Elsevier},

keywords = {pathways, process mining},

pubstate = {published},

tppubtype = {article}

}