J. Comité-Éditorial, Signalement des événements indésirables : pour quoi faire et à qui ? Journal d'Acréditation des Médecins, 2009.

R. Rosenthal, H. Homann, K. Dwan, P. Clavien, and H. C. Bucher, Reporting of Adverse Events in Surgical Trials: Critical Appraisal of Current Practice, World Journal of Surgery, vol.13, issue.Suppl 1, p.8087, 2015.
DOI : 10.1007/s00268-014-2776-8

J. Cardin and H. Johanet, Intraoperative events and their outcome: Data from 4007 laparoscopic interventions by the French ???Club C??lio???, Journal of Visceral Surgery, vol.148, issue.4, pp.299-310, 2011.
DOI : 10.1016/j.jviscsurg.2011.07.008

F. Chung, G. Mezei, and D. Tong, Pre-existing medical conditions as predictors of adverse events in day-case surgery., British Journal of Anaesthesia, vol.83, issue.2, p.262270, 1999.
DOI : 10.1093/bja/83.2.262

J. L. Faucheron, D. Voirin, F. Reche, and A. Dubreuil, Résultats techniques de la rectopexie au promontoire par voie c×lioscopique pour prolapsus total du rectum : Evaluation prospective chez 100 patients consécutifs, Le Journal de coelio-chirurgie, issue.63, p.4043, 2007.

A. J. Greenstein, A. S. Wahed, A. Adeniji, A. P. Courcoulas, G. Dakin et al., Prevalence of Adverse Intraoperative Events during Obesity Surgery and Their Sequelae, Journal of the American College of Surgeons, vol.215, issue.2, pp.271-277, 2012.
DOI : 10.1016/j.jamcollsurg.2012.03.008

H. M. Kaafarani, M. N. Mavros, J. Hwabejire, P. Fagenholz, D. D. Yeh et al., Derivation and Validation of a Novel Severity Classication for Intraoperative Adverse Events, Journal of the American College of Surgeons, vol.218, issue.6, p.11201128, 2014.

P. Kambakamba, D. Dindo, A. Nocito, P. A. Clavien, B. Seifert et al., Intraoperative adverse events during laparoscopic colorectal resectionbetter laparoscopic treatment but unchanged incidence. Lessons learnt from a Swiss multi-institutional analysis of 3,928 patients, Langenbeck's Archives of Surgery, vol.399, issue.3, p.297305, 2014.
DOI : 10.1007/s00423-013-1156-4

P. Kirchho, S. Dincler, and P. Buchmann, A Multivariate Analysis of Potential Risk Factors for Intra-and Postoperative Complications in 1316 Elective Laparoscopic Colorectal Procedures, Annals of Surgery, vol.248, issue.2, p.259265, 2008.

M. N. Mavros, G. C. Velmahos, A. Larentzakis, D. D. Yeh, P. Fagenholz et al., Opening Pandora's box : understanding the nature, patterns, and 30-day outcomes of intraoperative adverse events, The American Journal of Surgery, vol.208, issue.4, p.626631, 2014.

L. Morgan, E. Robertson, M. Hadi, K. Catchpole, S. Pickering et al., Capturing intraoperative process deviations using a direct observational approach: the glitch method, BMJ Open, vol.3, issue.11, p.3519, 2013.
DOI : 10.1136/bmjopen-2013-003519

M. Nathan, J. M. Karamichalis, H. Liu, P. Del-nido, F. Pigula et al., Intraoperative adverse events can be compensated by technical performance in neonates and infants after cardiac surgery: A??prospective study, The Journal of Thoracic and Cardiovascular Surgery, vol.142, issue.5, pp.14210981107-5, 2011.
DOI : 10.1016/j.jtcvs.2011.07.003

Y. R. Rampersaud, E. R. Moro, M. A. Neary, K. White, S. J. Lewis et al., Intraoperative Adverse Events and Related Postoperative Complications in Spine Surgery: Implications for Enhancing Patient Safety Founded on Evidence-Based Protocols, Spine, vol.31, issue.13, p.3115031510, 2006.
DOI : 10.1097/01.brs.0000220652.39970.c2

. Bissett, Systematic review on ventral rectopexy for rectal prolapse and intussusception, Colorectal Disease, vol.12, issue.6, p.504512, 2010.

D. Dindo, N. Demartines, and P. Clavien, Classication of Surgical Complications : A New Proposal With Evaluation in a Cohort of 6336 Patients and Results of a Survey, Annals of Surgery, vol.240, issue.2, p.205213, 2004.

P. A. Clavien, J. Barkun, M. L. De-oliveira, J. N. Vauthey, D. Dindo et al., The Clavien-Dindo Classication of Surgical Complications : Five-Year Experience, Annals of Surgery, vol.250, issue.2, p.187196, 2009.

J. Reason, Human Error, 1990.

A. J. Forster, H. J. Mur, J. F. Peterson, T. K. Gandhi, and D. W. Bates, The incidence and severity of adverse events aecting patients after discharge from the hospital, Annals of internal medicine, vol.138, issue.3, p.161167, 2003.

F. Lalys and P. Jannin, Surgical process modelling: a review, International Journal of Computer Assisted Radiology and Surgery, vol.51, issue.1, p.495511, 2013.
DOI : 10.1007/s11548-013-0940-5
URL : https://hal.archives-ouvertes.fr/inserm-00926470

T. Neumuth, N. Durstewitz, M. Fischer, G. Strauss, A. Dietz et al., Structured recording of intraoperative surgical workows, pp.61450-61450, 2006.

T. Neumuth, P. Jannin, J. Schlomberg, J. Meixensberger, P. Wiedemann et al., Analysis of surgical intervention populations using generic surgical process models, International Journal of Computer Assisted Radiology and Surgery, vol.56, issue.10, p.5971, 2010.
DOI : 10.1007/s11548-010-0475-y
URL : https://hal.archives-ouvertes.fr/inserm-00546456

F. Lalys, D. Bouget, L. Riaud, and P. Jannin, Automatic knowledge-based recognition of low-level tasks in ophthalmological procedures, International Journal of Computer Assisted Radiology and Surgery, vol.2, issue.4, p.3949, 2012.
DOI : 10.1007/s11548-012-0685-6
URL : https://hal.archives-ouvertes.fr/inserm-00695646

K. Charrière, G. Quellec, M. Lamard, G. Coatrieux, B. Cochener et al., Automated surgical step recognition in normalized cataract surgery videos, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, p.46474650, 2014.
DOI : 10.1109/EMBC.2014.6944660

S. Agarwal, A. Joshi, T. Finin, Y. Yesha, and T. Ganous, A Pervasive Computing System for the Operating Room of the Future, Mobile Networks and Applications, vol.12, issue.2, p.215228, 2007.
DOI : 10.1007/s11036-007-0010-8

B. Bhatia, T. Oates, Y. Xiao, and P. Hu, Real-time identication of operating room state from video, p.17611766, 2007.

S. A. Ahmadi, T. Sielhorst, R. Stauder, M. Horn, H. Feussner et al., Recovery of surgical workow without explicit models, Medical Image Computing and Computer-Assisted InterventionMICCAI, p.420428, 2006.

S. A. Ahmadi, N. Padoy, K. Rybachuk, H. Feussner, S. M. Heinin et al., Motif discovery in OR sensor data with application to surgical workow analysis and activity detection, 2009.

T. Blum, N. Padoy, H. Feuÿner, and N. Navab, Workow mining for visualization and analysis of surgeries, International Journal of Computer Assisted Radiology and Surgery, vol.3, issue.5, p.379386, 2008.

L. Bouarfa, P. P. Jonker, and J. Dankelman, Discovery of high-level tasks in the operating room, Journal of Biomedical Informatics, vol.44, issue.3, p.455462, 2011.
DOI : 10.1016/j.jbi.2010.01.004

J. A. Ibbotson, C. L. Mackenzie, C. G. Cao, and A. J. Lomax, Gaze patterns in laparoscopic surgery, Studies in health technology and informatics, vol.62, p.154160, 1998.

A. James, D. Vieira, B. Lo, A. Darzi, and G. Yang, Eye-gaze driven surgical workow segmentation, Medical Image Computing and Computer-Assisted Intervention MICCAI 2007, p.110117, 2007.
DOI : 10.1007/978-3-540-75759-7_14

S. Ko, J. Kim, W. Lee, and D. Kwon, Surgery task model for intelligent interaction between surgeon and laparoscopic assistant robot, International Journal of Assitive Robotics and Mechatronics, vol.8, issue.1, p.3846, 2007.

H. C. Lin, I. Shafran, D. Yuh, and G. D. Hager, Towards automatic skill evaluation : Detection and segmentation of robot-assisted surgical motions, Computer Aided Surgery, vol.11, issue.5, p.220230, 2006.

C. Mackenzie, J. A. Ibbotson, C. G. Cao, and A. J. Lomax, Hierarchical decomposition of laparoscopic surgery: a human factors approach to investigating the operating room environment, Minimally Invasive Therapy & Allied Technologies, vol.29, issue.3, p.121127, 2001.
DOI : 10.1080/136457001753192222

T. Neumuth, P. Jannin, G. Strauss, J. Meixensberger, and O. Burgert, Validation of Knowledge Acquisition for Surgical Process Models, Journal of the American Medical Informatics Association, vol.16, issue.1, p.7280, 2009.
DOI : 10.1197/jamia.M2748
URL : https://hal.archives-ouvertes.fr/inserm-00344260

S. Nomm, E. Petlenkov, J. Vain, J. Belikov, F. Miyawaki et al., Recognition of the Surgeon's Motions During Endoscopic Operation by Statistics Based Algorithm and Neural Networks Based ANARX Models, IFAC Proceedings Volumes, vol.41, issue.2, 2008.
DOI : 10.3182/20080706-5-KR-1001.02501

N. Padoy, T. Blum, H. Feussner, M. Berger, and N. Navab, On-line Recognition of Surgical Activity for Monitoring in the Operating Room, p.17181724, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00331390

N. Padoy, T. Blum, S. Ahmadi, H. Feussner, M. Berger et al., Statistical modeling and recognition of surgical workow, Medical Image Analysis, vol.16, issue.3, p.632641, 2010.

W. S. Sandberg, B. Daily, M. Egan, J. E. Stahl, J. M. Goldman et al., Deliberate Perioperative Systems Design Improves Operating Room Throughput, Anesthesiology, vol.103, issue.2, p.406418, 2005.
DOI : 10.1097/00000542-200508000-00025

C. Cao, C. L. Mackenzie, and S. Payandeh, Task and motion analyses in endoscopic surgery, p.583590, 1996.

F. Despinoy, D. Bouget, G. Forestier, C. Penet, N. Zemiti et al., Unsupervised Trajectory Segmentation for Surgical Gesture Recognition in Robotic Training, IEEE Transactions on Biomedical Engineering, vol.63, issue.6, p.6312801291, 2016.
DOI : 10.1109/TBME.2015.2493100
URL : https://hal.archives-ouvertes.fr/lirmm-01217023

O. Dergachyova, D. Bouget, A. Huaulmé, X. Morandi, and P. Jannin, Automatic data-driven real-time segmentation and recognition of surgical workow, International Journal of Computer Assisted Radiology and Surgery, 2016.

G. Forestier, F. Lalys, L. Riaud, B. Trelhu, and P. Jannin, Classication of surgical processes using dynamic time warping, Journal of biomedical informatics, vol.45, issue.2, p.255264, 2012.

P. Jannin, M. Raimbault, X. Morandi, and B. Gibaud, Modeling Surgical Procedures for Multimodal Image-Guided Neurosurgery, Medical Image Computing and Computer-Assisted Intervention MICCAI 2001, number 2208 in Lecture Notes in Computer Science, p.565572, 2001.
DOI : 10.1007/3-540-45468-3_68

P. Jannin and X. Morandi, Surgical models for computer-assisted neurosurgery, NeuroImage, vol.37, issue.3, p.783791, 2007.
DOI : 10.1016/j.neuroimage.2007.05.034
URL : https://hal.archives-ouvertes.fr/inserm-00185435

B. Trelhu, F. Lalys, L. Riaud, X. Morandi, and P. Jannin, Analyse de données pour la construction de modèles de procédures neurochirurgicales, p.427432, 2009.

B. Gibaud, C. Penet, and P. Jannin, OntoSPM : a core ontology of surgical procedure models, Proceedings of Surgetica'2014, p.175177, 2014.

C. E. Reiley and G. D. Hager, Task versus Subtask Surgical Skill Evaluation of Robotic Minimally Invasive Surgery, International Conference on Medical Image Computing and Computer-Assisted Intervention, p.435442, 2009.
DOI : 10.1007/978-3-642-04268-3_54

F. Meng, L. W. D-'avolio, A. A. Chen, R. K. Taira, and H. Kangarloo, Generating models of surgical procedures using UMLS concepts and multiple sequence alignment, American Medical Informatics Association, p.520, 2005.

D. Kati¢, A. Wekerle, F. Gärtner, H. Kenngott, B. P. Müller-stich et al., Ontology-based prediction of surgical events in laparoscopic surgery, Medical Imaging 2013: Image-Guided Procedures, Robotic Interventions, and Modeling, p.86711, 2013.
DOI : 10.1117/12.2007895

P. F. Hu, Y. Xiao, D. Ho, C. F. Mackenzie, H. Hu et al., Advanced Visualization Platform for Surgical Operating Room Coordination: Distributed Video Board System, Surgical Innovation, vol.103, issue.6, p.129135, 2006.
DOI : 10.1177/1553350606291484

C. Mello-thoms and D. Gur, Remote vs. head-mounted eye-tracking: a comparison using radiologists reading mammograms, Medical Imaging 2007: Image Perception, Observer Performance, and Technology Assessment, p.65150, 2007.
DOI : 10.1117/12.706335

L. Rabiner and B. Juang, An introduction to hidden Markov models, IEEE ASSP Magazine, vol.3, issue.1, p.416, 1986.
DOI : 10.1109/MASSP.1986.1165342

H. Sakoe and S. Chiba, Dynamic programming algorithm optimization for spoken word recognition, IEEE transactions on acoustics, speech, and signal processing, vol.26, issue.1, p.4349, 1978.
DOI : 10.1109/tassp.1978.1163055
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.114.3782

G. Forestier, F. Petitjean, L. Riaud, and P. Jannin, Optimal Sub-Sequence Matching for the Automatic Prediction of Surgical Tasks, In Articial Intelligence in Medicine, vol.9105, p.123132, 2015.
DOI : 10.1007/978-3-319-19551-3_15
URL : https://hal.archives-ouvertes.fr/hal-01262692

L. Bouarfa and J. Dankelman, Workow mining and outlier detection from clinical activity logs, Journal of Biomedical Informatics, vol.45, issue.6, p.11851190, 2012.
DOI : 10.1016/j.jbi.2012.08.003
URL : http://doi.org/10.1016/j.jbi.2012.08.003

R. Wolf, Quantication de la qualité d'un geste chirurgical à partir de connaissances a priori, 2013.

F. Narayanan, M. Pasteau, and A. Marchal, Krupa, and others. Synthesis and Simulation of Surgical Process Models, Studies in health technology and informatics, vol.220, p.6370, 2016.

A. Hughes-hallett, P. Pratt, J. Dilley, J. Vale, A. Darzi et al., Augmented reality: 3D image-guided surgery, Cancer Imaging, vol.15, issue.Suppl 1, p.12, 2015.
DOI : 10.1016/j.urology.2014.02.051
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4601542

J. Loygue, B. Nordlinger, O. Cunci, M. Malafosse, C. Huguet et al., Rectopexy to the promontory for the treatment of rectal prolapse. Diseases of the colon & rectum, p.356359, 1984.

D. Lechaux, Traitement des prolapsus du rectum par abord laparoscopique. EMC -Techniques chirurgicales -Appareil digestif, p.17, 2007.
DOI : 10.1016/s0246-0424(07)44063-8

C. Rosse and J. L. Mejino, A reference ontology for biomedical informatics: the Foundational Model of Anatomy, Journal of Biomedical Informatics, vol.36, issue.6, p.478500, 2003.
DOI : 10.1016/j.jbi.2003.11.007

Y. He, S. Sarntivijai, Y. Lin, Z. Xiang, A. Guo et al., OAE: The Ontology of Adverse Events, Journal of Biomedical Semantics, vol.5, issue.1, p.2929, 2014.
DOI : 10.1186/2041-1480-5-29

J. Souvignet, G. Declerck, H. Asfari, M. C. Jaulent, and C. Bousquet, OntoADR a semantic resource describing adverse drug reactions to support searching, coding, and information retrieval, Journal of Biomedical Informatics, vol.63, p.100107, 2016.
DOI : 10.1016/j.jbi.2016.06.010
URL : https://hal.archives-ouvertes.fr/hal-01358317

M. C. Jaulent and J. Alecu, Evaluation of an ontological resource for pharmacovigilance, Studies in Health Technology and Informatics, vol.150, p.522526, 2009.

D. B. Lenat and R. V. Guha, Building Large Knowledge-Based Systems ; Representation and Inference in the Cyc Project, 1989.

M. Uschold and M. King, Towards a methodology for building ontologies. Citeseer, 1995.

M. Grüninger and M. S. Fox, Methodology for the Design and Evaluation of Ontologies, 1995.

A. Bernaras, I. Laresgoiti, and J. Corera, Building and Reusing Ontologies for Electrical Network Applications, ECAI, p.298302, 1996.

B. Swartout, R. Patil, K. Knight, and T. Russ, Toward distributed use of largescale ontologies, Proc. of the Tenth Workshop on Knowledge Acquisition for Knowledge-Based Systems, p.138148, 1996.

M. Fernández-lópez, A. Gómez-pérez, and N. Juristo, Methontology : from ontological art towards ontological engineering, 1997.

S. Staab, R. Studer, H. Schnurr, and Y. Sure, Knowledge processes and ontologies, IEEE Intelligent Systems, vol.16, issue.1, p.2634, 2001.
DOI : 10.1109/5254.912382
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.22.2860

F. Neuhaus, A. Vizedom, K. Baclawski, M. Bennett, M. Dean et al., Obrst, and others. Towards ontology evaluation across the life cycle, Applied Ontology, vol.8, issue.3, p.179194, 2013.

Ó. Corcho, M. Fernández-lópez, A. Gómez-pérez, and Ó. Vicente, WebODE: An Integrated Workbench for Ontology Representation, Reasoning, and Exchange, As
DOI : 10.1007/3-540-45810-7_16
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.96.3003

V. R. Gómez-pérez and . Benjamins, Knowledge Engineering and Knowledge Management : Ontologies and the Semantic Web, number 2473 in Lecture Notes in Computer Science, pp.138153-138163, 2002.

R. E. Clark, D. F. Feldon, J. J. Van-merriënboer, K. A. Yates, and S. Early, Cognitive task analysis. Handbook of research on educational communications and technology, p.577593, 2008.

R. E. Clark, Design document for a guided experiential learning course, Final report on contract DAAD, 1999.

G. A. Klein, R. Calderwood, and D. Macgregor, Critical decision method for eliciting knowledge, IEEE Transactions on Systems, Man, and Cybernetics, vol.19, issue.3, pp.462-472, 1989.
DOI : 10.1109/21.31053

A. D. Hoore and F. Penninckx, Laparoscopic ventral recto(colpo)pexy for rectal prolapse : surgical technique and outcome for 109 patients, Surgical Endoscopy, vol.20, issue.12, p.19191923, 2006.

N. Wijels, C. Cunningham, A. Dixon, G. Greenslade, and I. Lindsey, Laparoscopic ventral rectopexy for external rectal prolapse is safe and eective in the elderly. Does this make perineal procedures obsolete ?, Colorectal Disease, vol.13, issue.5, p.561566, 2011.

F. Cadeddu, P. Sileri, M. Grande, E. De-luca, L. Franceschilli et al., Focus on abdominal rectopexy for full-thickness rectal prolapse: meta-analysis of literature, Techniques in Coloproctology, vol.48, issue.1, p.3753, 2012.
DOI : 10.1007/s10151-011-0798-x

G. Portier, S. Kirzin, P. Cabarrot, M. Queralto, and F. Lazorthes, The eect of abdominal ventral rectopexy on faecal incontinence and constipation in patients with internal intra-anal rectal intussusception, Colorectal Disease, issue.8, p.13914917, 2011.

F. Fda, Adverse Events Reporting System (FAERS) -FDA Adverse Event Reporting System (FAERS) Statistics, 2015.

S. N. Jadhav and K. Bhandari, Anomaly Detection Using Hidden Markov Model, International Journal of Computational Engineering Research, p.28, 2013.

X. Tan and H. Xi, Hidden semi-Markov model for anomaly detection, Applied Mathematics and Computation, vol.205, issue.2, p.562567, 2008.
DOI : 10.1016/j.amc.2008.05.028

G. Forestier, F. Petitjean, L. Riaud, and P. Jannin, Non-linear temporal scaling of surgical processes, Artificial Intelligence in Medicine, vol.62, issue.3, p.143152, 2014.
DOI : 10.1016/j.artmed.2014.10.007
URL : https://hal.archives-ouvertes.fr/hal-00862978

M. Shokoohi-yekta, J. Wang, and E. Keogh, On the Non-Trivial Generalization of Dynamic Time Warping to the Multi-Dimensional Case, Data Mining. Proceeding of the 2015 International Conference on, p.3948, 2015.
DOI : 10.1137/1.9781611974010.33

F. Petitjean, A. Ketterlin, and P. Gançarski, A global averaging method for dynamic time warping, with applications to clustering, Pattern Recognition, vol.44, issue.3, p.678693, 2011.
DOI : 10.1016/j.patcog.2010.09.013

L. E. Baum and T. Petrie, Statistical Inference for Probabilistic Functions of Finite State Markov Chains, The Annals of Mathematical Statistics, vol.37, issue.6, p.15541563, 1966.
DOI : 10.1214/aoms/1177699147

L. E. Baum and J. A. Eagon, An inequality with applications to statistical estimation for probabilistic functions of Markov processes and to a model for ecology, Bulletin of the American Mathematical Society, vol.73, issue.3, p.73360363, 1967.
DOI : 10.1090/S0002-9904-1967-11751-8

L. E. Baum and G. Sell, Growth transformations for functions on manifolds, Pacific Journal of Mathematics, vol.27, issue.2, p.211227, 1968.
DOI : 10.2140/pjm.1968.27.211

L. E. Baum, T. Petrie, G. Soules, and N. Weiss, A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains, The Annals of Mathematical Statistics, vol.41, issue.1, p.164171, 1970.
DOI : 10.1214/aoms/1177697196

L. R. Rabiner, A tutorial on hidden Markov models and selected applications in speech recognition, Proceedings of the IEEE, vol.77, issue.2, p.257286, 1989.

T. V. Duong, H. H. Bui, D. Q. Phung, and S. Venkatesh, Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), p.838845, 2005.
DOI : 10.1109/CVPR.2005.61

T. V. Duong, D. Q. Phung, H. H. Bui, and S. Venkatesh, Human Behavior Recognition with Generic Exponential Family Duration Modeling in the Hidden

N. T. Nguyen, D. Q. Phung, S. Venkatesh, and H. Bui, Learning and Detecting Activities from Movement Trajectories Using the Hierarchical Hidden Markov Models, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)
DOI : 10.1109/CVPR.2005.203

G. D. Forney, The viterbi algorithm, Proceedings of the IEEE, p.268278, 1973.
DOI : 10.1109/PROC.1973.9030

S. Yu and H. Kobayashi, An ecient forward-backward algorithm for an explicitduration hidden Markov model, IEEE Signal Processing Letters, vol.10, issue.1, p.1114, 2003.

O. J. Dunn, Multiple Comparisons among Means, Journal of the American Statistical Association, vol.25, issue.293, p.52, 1961.
DOI : 10.1080/01621459.1961.10482090
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.309.1277

S. Voros, A. Moreau-gaudry, B. Tamadazte, G. Custillon, R. Heus et al., Devices and systems targeted towards augmented robotic radical prostatectomy, IRBM, vol.34, issue.2, p.139146, 2013.
DOI : 10.1016/j.irbm.2013.01.014
URL : https://hal.archives-ouvertes.fr/hal-00910308

A. Agustinos and S. Voros, 2D/3D Real-Time Tracking of Surgical Instruments Based on Endoscopic Image Processing
DOI : 10.1007/978-3-319-29965-5_9
URL : https://hal.archives-ouvertes.fr/hal-01424280

. Mariottini, Computer-Assisted and Robotic Endoscopy, number 9515 in Lecture Notes in Computer Science, p.90100, 2015.

J. Meier, A. Dietz, A. Boehm, and T. Neumuth, Predicting treatment process steps from events, Journal of Biomedical Informatics, vol.53, p.308319, 2015.
DOI : 10.1016/j.jbi.2014.12.003
URL : http://doi.org/10.1016/j.jbi.2014.12.003

G. Forestier, F. Lalys, L. Riaud, D. Louis-collins, J. Meixensberger et al., Multi-site study of surgical practice in neurosurgery based on surgical process models, Journal of Biomedical Informatics, vol.46, issue.5, p.46822829, 2013.
DOI : 10.1016/j.jbi.2013.06.006
URL : https://hal.archives-ouvertes.fr/inserm-00853846

T. Neumuth, R. Wiedemann, C. Foja, P. Meier, J. Schlomberg et al., Identication of surgeon\individual treatment proles to support the provision of an optimum treatment service for cataract patients, Journal of Ocular Biology, Diseases, and Informatics, vol.3, issue.2, p.7383, 2010.

D. Y. Chiang, P. O. Brown, and M. B. Eisen, Visualizing associations between genome sequences and gene expression data using genome-mean expression proles, Bioinformatics, issue.1, pp.17-49, 2001.
DOI : 10.1093/bioinformatics/17.suppl_1.s49
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.18.6871

I. Eidhammer, I. Jonassen, and W. R. Taylor, Structure Comparison and Structure Patterns, Journal of Computational Biology, vol.7, issue.5, p.685716, 2000.
DOI : 10.1089/106652701446152
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.34.5869

H. Mannila, H. Toivonen, and A. I. Verkamo, Discovery of Frequent Episodes in Event Sequences, Data Mining and Knowledge Discovery, vol.1, issue.3, p.259289, 1997.

C. I. Ezeife and Y. Lu, Mining Web Log Sequential Patterns with Position Coded Pre-Order Linked WAP-Tree, Data Mining and Knowledge Discovery, vol.10, issue.1, p.538, 2005.
DOI : 10.1007/s10618-005-0248-3

Z. Huang, X. Lu, H. Duan, and W. Fan, Summarizing clinical pathways from event logs, Journal of Biomedical Informatics, vol.46, issue.1, p.111127, 2013.
DOI : 10.1016/j.jbi.2012.10.001
URL : http://doi.org/10.1016/j.jbi.2012.10.001

Z. Huang, W. Dong, L. Ji, C. Gan, X. Lu et al., Discovery of clinical pathway patterns from event logs using probabilistic topic models, Journal of Biomedical Informatics, vol.47, p.3957, 2014.
DOI : 10.1016/j.jbi.2013.09.003

. Knaus, Data mining and clinical data repositories : Insights from a 667,000 patient data set, Computers in Biology and Medicine, vol.36, issue.12, p.13511377, 2006.

B. Brejová, T. Vinar, and M. Li, Pattern Discovery
DOI : 10.1007/978-1-59259-335-4_29

J. Van-helden, B. André, and J. Collado-vides, Extracting regulatory sites from the upstream region of yeast genes by computational analysis of oligonucleotide frequencies1, Journal of Molecular Biology, vol.281, issue.5, p.827842, 1998.

R. Agrawal and R. Srikant, Fast Algorithms For Mining Association Rules In Datamining, p.487499, 1994.

I. Rigoutsos and A. Floratos, Motif discovery without alignment or enumeration (extended abstract), Proceedings of the second annual international conference on Computational molecular biology , RECOMB '98, p.221227, 1998.
DOI : 10.1145/279069.279118

. Wootton, Detecting subtle sequence signals : a Gibbs sampling strategy for multiple alignment, p.208214, 1993.

A. K. Jain, Data clustering: 50 years beyond K-means, Pattern Recognition Letters, vol.31, issue.8, p.31651666, 2010.
DOI : 10.1016/j.patrec.2009.09.011
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.151.4286

R. R. Sokal and C. D. Michener, A statistical method for evaluating systematic relationships, University of Kansas Scientic Bulletin, vol.28, p.14091438, 1958.

T. Neumuth, G. Strauÿ, J. Meixensberger, H. U. Lemke, and O. Burgert, Acquisition of Process Descriptions from Surgical Interventions, Database and Expert Systems Applications, number 4080 in Lecture Notes in Computer Science, pp.602611-602621, 1007.
DOI : 10.1007/11827405_59

B. Li, Z. Zhang, S. Xie, and R. Chen, Fluorescence spectral characteristics of human blood and its endogenous uorophores, Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu, vol.26, issue.7, p.13101313, 2006.

R. Wolf, J. Duchateau, P. Cinquin, and S. Voros, 3D Tracking of Laparoscopic Instruments Using Statistical and Geometric Modeling, International Conference on Medical Image Computing and Computer-Assisted Intervention, p.203210
DOI : 10.1109/34.888718
URL : https://hal.archives-ouvertes.fr/hal-00726854

C. Piciarelli and G. L. Foresti, On-line trajectory clustering for anomalous events detection, Pattern Recognition Letters, vol.27, issue.15, p.18351842, 2006.
DOI : 10.1016/j.patrec.2006.02.004

K. Charriere, Real-time analysis and characterization of surgical videos. Application to cataract surgery. Theses, Télécom Bretagne, 2015.
URL : https://hal.archives-ouvertes.fr/tel-01282321