, GET / HTTP/1.1" 115.231.219, GET /robots.txt HTTP/1.1" 207.46.13, vol.145, p.98, 2018.

, ATC Anatomical Therapeutic Chemical Classification System, vol.96, issue.102, p.110

, Presentation Identifier Code): code required for drug marketing authorization decision in France, vol.96, p.110

, DCM Discriminant Chronicle Mining. 9, vol.53, p.110

, GENEPI GENeric substitution of anti-EPIleptic drugs: name of a pharmaco-epidemiology study conducted in the University hospital of Rennes, vol.10, pp.98-101

, SNDS Système National des Données de Santé (National System for Healthcare Data, vol.4, pp.95-99

, Irvine: reference to the dataset repository created in this university

Y. Dauxais, T. Guyet, D. Gross-amblard, and A. Happe, Discriminant chronicles mining, Conference on Artificial Intelligence in Medicine in Europe, pp.234-244, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01568929

T. Guyet, A. Happe, and Y. Dauxais, Declarative sequential pattern mining of care pathways, Conference on Artificial Intelligence in Medicine in Europe, pp.261-266, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01569023

Y. Dauxais, D. Gross-amblard, T. Guyet, and A. Happe, Extraction de chroniques discriminantes, Extraction et Gestion des Connaissances (EGC, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01413473

V. Lemière, Y. Dauxais, P. Boizumault, and A. Lallouet, Mining frequent patterns using cp: a comparative study, CP Doctoral program, 2016.

F. Balusson, M. Botrel, O. Dameron, Y. Dauxais, E. Drezen et al., Peps: a platform for supporting studies in pharmacoepidemiology using medico-administrative databases, International Congress on e-Health Research, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01380939

Y. Dauxais, D. Gross-amblard, T. Guyet, and A. Happe, Chronicles mining in a database of drugs exposures, ECML Doctoral consortium, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01184100

. Bibliography-[achar, A unified view of the apriori-based algorithms for frequent episode discovery, Knowledge and information systems, vol.31, issue.2, pp.223-250, 2012.

. Agrawal, R. Srikant-;-agrawal, and R. Srikant, Mining sequential patterns, Proceedings of the International Conference on Data Engineering, pp.3-14, 1995.

A. , B. , H. Boulicaut, and J. , Mining frequent sequential patterns under regular expressions: a highly adaptative strategy for pushing constraints, Proceedings of the 2003 SIAM International Conference on Data Mining, pp.316-320, 2003.

J. F. Allen-;-allen, Towards a general theory of action and time. Artificial intelligence, vol.23, pp.123-154, 1984.

. Alvarez, Discovering metric temporal constraint networks on temporal databases, Artificial Intelligence in Medicine, vol.58, issue.3, pp.139-154, 2013.

. Andrews, Support vector machines for multiple-instance learning, Advances in neural information processing systems, pp.577-584, 2003.

. Ayres, Sequential pattern mining using a bitmap representation, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pp.429-435, 2002.

[. Batal, A temporal pattern mining approach for classifying electronic health record data, ACM Transactions on Intelligent Systems and Technology, vol.4, issue.4, p.63, 2013.

. Bay, S. D. Bay, and M. J. Pazzani, Detecting group differences: Mining contrast sets, Data Mining and Knowledge Discovery, vol.5, issue.3, pp.213-246, 2001.

G. Beedkar, K. Beedkar, and R. Gemulla, Lash: Large-scale sequence mining with hierarchies, Proceedings of the International Conference on Management of Data, pp.491-503, 2015.

. Blockeel, Multi-instance tree learning, Proceedings of the 22nd international conference on Machine learning, pp.57-64, 2005.

C. Borgelt, Efficient implementations of apriori and eclat, FIMI'03: Proceedings of the IEEE ICDM workshop on frequent itemset mining implementations, 2003.

[. Bornemann, Stife: A framework for feature-based classification of sequences of temporal intervals, International Conference on Discovery Science, pp.85-100, 2016.

[. Breiman, Classification and regression trees, 1984.

[. Bringmann, Pattern-based classification: a unifying perspective, 2011.

[. Buzmakov, The representation of sequential patterns and their projections within formal concept analysis, Workshop Notes for LML (PKDD), 2013.
URL : https://hal.archives-ouvertes.fr/hal-00910266

. Chevaleyre, Y. Zucker-;-chevaleyre, and J. Zucker, Solving multipleinstance and multiple-part learning problems with decision trees and rule sets. application to the mutagenesis problem, Conference of the Canadian Society for Computational Studies of Intelligence, pp.204-214, 2001.
URL : https://hal.archives-ouvertes.fr/hal-01571852

W. W. Cohen-;-cohen, Fast effective rule induction, Proceedings of the International Conference on Machine Learning, pp.115-123, 1995.

[. Cordier, Distributed chronicles for on-line diagnosis of web services, 18th International Workshop on Principles of Diagnosis, pp.37-44, 2007.
URL : https://hal.archives-ouvertes.fr/inria-00463008

[. Cram, A complete chronicle discovery approach: application to activity analysis, Expert Systems, vol.29, issue.4, pp.321-346, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01354577

. De-smedt, Behavioral constraint pattern-based sequence classification, Machine Learning and Knowledge Discovery in Databases-European Conference, ECML PKDD, pp.20-36, 2017.

. Dean, J. Dean, and S. Ghemawat, Mapreduce: simplified data processing on large clusters, Communications of the ACM, vol.51, issue.1, pp.107-113, 2008.

[. Dechter, Temporal constraint networks, Artificial intelligence, vol.49, pp.61-95, 1991.

[. Dietterich, Solving the multiple instance problem with axis-parallel rectangles, Artificial intelligence, vol.89, issue.1, pp.31-71, 1997.

L. Dong, G. Dong, and J. Li, Efficient mining of emerging patterns: Discovering trends and differences, Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, pp.43-52, 1999.

R. ;. Doran, G. Doran, and S. Ray, A theoretical and empirical analysis of support vector machine methods for multiple-instance classification, Machine Learning, vol.97, pp.79-102, 2014.

. Dousson, C. Duong-;-dousson, and T. V. Duong, Discovering chronicles with numerical time constraints from alarm logs for monitoring dynamic systems, Proceedings of International Conference on Artificial Intelligence, pp.620-626, 1999.

[. Duivesteijn, Exceptional model mining, Data Mining and Knowledge Discovery, vol.30, issue.1, pp.47-98, 2016.

[. Fabrègue, Discriminant temporal patterns for linking physico-chemistry and biology in hydro-ecosystem assessment, Ecological Informatics, vol.24, pp.210-221, 2014.

[. Fabrègue, Orderspan: Mining closed partially ordered patterns, International Symposium on Intelligent Data Analysis, pp.186-197, 2013.

F. Foulds, J. Foulds, and E. Frank, A review of multi-instance learning assumptions, The Knowledge Engineering Review, vol.25, issue.01, pp.1-25, 2010.

D. Fradkin-and-mörchen-;-fradkin and F. Mörchen, Mining sequential patterns for classification, Knowledge and Information Systems, vol.45, issue.3, pp.731-749, 2015.

[. Garofalakis, Spirit: Sequential pattern mining with regular expression constraints, Proceedings of VLDB, vol.99, pp.7-10, 1999.

[. Gärtner, Multiinstance kernels, Proceedings of ICML, vol.2, pp.179-186, 2002.

[. Giannotti, Efficient mining of temporally annotated sequences, Proceedings of the 2006 SIAM International Conference on Data Mining, pp.348-359, 2006.
DOI : 10.1137/1.9781611972764.31
URL : https://epubs.siam.org/doi/pdf/10.1137/1.9781611972764.31

[. Guyet, Declarative sequential pattern mining of care pathways, Conference on Artificial Intelligence in Medicine in Europe, pp.261-266, 2017.
DOI : 10.1007/978-3-319-59758-4_29
URL : https://hal.archives-ouvertes.fr/hal-01569023

T. Guyet and R. Quiniou, Extracting temporal patterns from interval-based sequences, Proceedings of International Joint Conference on Artificial Intelligence, pp.1306-1311, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00618444

[. Han, Freespan: frequent pattern-projected sequential pattern mining, Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, pp.355-359, 2000.

[. Han, Prefixspan: Mining sequential patterns efficiently by prefix-projected pattern growth, Proceedings of the 17th international conference on data engineering, pp.215-224, 2001.

[. Han, Mining frequent patterns without candidate generation, ACM sigmod record, vol.29, pp.1-12, 2000.
DOI : 10.1145/335191.335372

[. Herrera, An overview on subgroup discovery: foundations and applications, Knowledge and information systems, vol.29, issue.3, pp.495-525, 2011.
DOI : 10.1007/s10115-010-0356-2

T. K. Ho, The random subspace method for constructing decision forests, Proceedings of the Third International Conference on Document Analysis and Recognition, vol.1, pp.832-844, 1995.

. Huang, On mining clinical pathway patterns from medical behaviors, Artificial Intelligence in Medicine, vol.56, issue.1, pp.35-50, 2012.
DOI : 10.1016/j.artmed.2012.06.002

[. Knobbe, From local patterns to global models: the LeGo approach to data mining, LeGo, vol.8, pp.1-16, 2008.

[. Lakshmanan, Investigating clinical care pathways correlated with outcomes, Business process management, pp.323-338, 2013.
DOI : 10.1007/978-3-642-40176-3_27

[. Lattner, Experimental comparison of symbolic learning programs for the classification of gene network topology models, Center for Computing Technologies-TZI, vol.2, p.1, 2003.

[. Li, Pfp: parallel fpgrowth for query recommendation, Proceedings of the conference on Recommender systems, pp.107-114, 2008.

Z. C. Lipton, The doctor just won't accept that! arXiv preprint, 2016.

[. Lou, Intelligible models for classification and regression, Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.150-158, 2012.
DOI : 10.1145/2339530.2339556
URL : http://www.cs.cornell.edu/~yinlou/papers/lou-kdd12.pdf

[. Luo, C. Luo, C. Chung, and S. M. , A scalable algorithm for mining maximal frequent sequences using a sample, Knowledge and Information Systems, vol.15, issue.2, pp.149-179, 2008.
DOI : 10.1007/s10115-006-0056-0

L. Ma, B. L. Ma, Y. Liu, and B. , Integrating classification and association rule mining, Proceedings of the fourth international conference on knowledge discovery and data mining, 1998.

N. R. Mabroukeh and C. I. Ezeife, A taxonomy of sequential pattern mining algorithms, ACM Journal of Computing Survey, vol.43, issue.1, pp.1-41, 2010.

[. Mannila, Discovery of frequent episodes in event sequences, Data mining and knowledge discovery, vol.1, issue.3, pp.259-289, 1997.

[. Mäntyjärvi, Sensor signal data set for exploring context recognition of mobile devices, Proc. of 2nd Int. Conf. on Pervasive Computing (PERVASIVE 2004), pp.18-23, 2004.

[. Masseglia, The psp approach for mining sequential patterns, Principles of Data Mining and Knowledge Discovery, pp.176-184, 1998.

[. Masseglia, Recherche des motifs séquentiels, Revue Ingénierie des Systemes d'Information (ISI), vol.9, issue.3-4, pp.183-210, 2004.

R. Mooney, C. H. Mooney, and J. F. Roddick, Sequential pattern mining-approaches and algorithms, ACM Journal of Computing Survey, vol.45, issue.2, pp.1-39, 2013.

B. Morin and H. Debar, Correlation of intrusion symptoms: an application of chronicles, International Workshop on Recent Advances in Intrusion Detection, pp.94-112, 2003.

R. Moskovitch and Y. Shahar, Fast time intervals mining using the transitivity of temporal relations, Knowledge and Information Systems, vol.42, issue.1, pp.21-48, 2015.

. Moulis, French health insurance databases: What interest for medical research? La Revue de Médecine Interne, vol.36, pp.411-417, 2015.

. Novak, Supervised descriptive rule discovery: A unifying survey of contrast set, emerging pattern and subgroup mining, Journal of Machine Learning Research, vol.10, pp.377-403, 2009.

[. Orlando, A new algorithm for gap constrained sequence mining, Proceedings of the 2004 ACM symposium on Applied computing, pp.540-547, 2004.

[. Otaki, Pattern structures for understanding episode patterns, CLA, pp.47-58, 2014.

G. Pagallo and D. Haussler, Boolean feature discovery in empirical learning, Machine learning, vol.5, issue.1, pp.71-99, 1990.

[. Papapetrou, Discovering frequent arrangements of temporal intervals, Fifth IEEE International Conference on, p.8, 2005.

[. Pasquier, Discovering frequent closed itemsets for association rules, International Conference on Database Theory, pp.398-416, 1999.
URL : https://hal.archives-ouvertes.fr/hal-00467747

[. Pedregosa, Scikit-learn: Machine learning in Python, Journal of Machine Learning Research, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

P. , H. ;. Pei, J. Han, and J. , Constrained frequent pattern mining: a patterngrowth view, ACM SIGKDD Explorations Newsletter, vol.4, issue.1, pp.31-39, 2002.

[. Pei, Mining sequential patterns with constraints in large databases, Proceedings of the international conference on Information and knowledge management, pp.18-25, 2002.

[. Polard, Brand name to generic substitution of antiepileptic drugs does not lead to seizure-related hospitalization: a population-based case-crossover study, Pharmacoepidemiology and drug safety, vol.24, issue.11, pp.1161-1169, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01198646

[. Quiniou, Application of ILP to cardiac arrhythmia characterization for chronicle recognition, Proceedings of International Conference on Inductive Logic Programming, pp.220-227, 2001.

J. R. Quinlan, Induction of decision trees, Machine learning, vol.1, issue.1, pp.81-106, 1986.

J. R. Quinlan, Simplifying decision trees. International journal of manmachine studies, vol.27, pp.221-234, 1987.

J. R. Quinlan and . Salle, C4. 5: Programming for machine learning. Morgan Kauffmann, Conference on Artificial Intelligence in Medicine in Europe, vol.38, pp.365-369, 1993.

T. Santisteban, J. Santisteban, and J. Tejada-cárcamo, Unilateral jaccard similarity coefficient, GSB@ SIGIR, pp.23-27, 2015.

R. Srikant and R. Agrawal, Mining sequential patterns: Generalizations and performance improvements, Advances in Database Technology-EDBT'96, pp.1-17, 1996.

[. Starner, Real-time american sign language recognition using desk and wearable computer based video, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.20, issue.12, pp.1371-1375, 1998.

. Subias, Learning chronicles signing multiple scenario instances, IFAC Proceedings Volumes, vol.47, pp.10397-10402, 2014.
URL : https://hal.archives-ouvertes.fr/hal-02087826

[. Uno, LCM ver. 2: Efficient mining algorithms for frequent/closed/maximal itemsets, FIMI, vol.126, 2004.

[. Vásquez, Enhanced chronicle learning for process supervision, IFAC-PapersOnLine, vol.50, issue.1, pp.5035-5040, 2017.

S. M. Weiss and N. Indurkhya, Reduced complexity rule induction, IJCAI, pp.678-684, 1991.

. Xing, A brief survey on sequence classification, ACM Sigkdd Explorations Newsletter, vol.12, issue.1, pp.40-48, 2010.

Y. , K. Yang, Z. Kitsuregawa, and M. , Lapin-spam: An improved algorithm for mining sequential pattern, Data Engineering Workshops, 2005. 21st International Conference on, pp.1222-1222, 2005.

Y. , Lapin: Effective sequential pattern mining algorithms by last position induction, 2005.

[. Zaharia, Spark: Cluster computing with working sets, Proceedings of the 2Nd USENIX Conference on Hot Topics in Cloud Computing, HotCloud'10, pp.10-10, 2010.

M. J. Zaki, Sequence mining in categorical domains: incorporating constraints, Proceedings of the ninth international conference on Information and knowledge management, pp.422-429, 2000.

M. J. Zaki, Spade: An efficient algorithm for mining frequent sequences, Machine learning, vol.42, issue.1, pp.31-60, 2001.

[. Zhang, A gsp-based efficient algorithm for mining frequent sequences, Proc. of IC-AI, pp.497-503, 2001.