Discovering metric temporal constraint networks on temporal databases

Artificial Intelligence in Medicine

Volumn 58, Issue 3, 2013, Pages 139-154

  Álvarez, Miguel R ^a   Félix, Paulo ^a   Cariñena, Purificación ^a  

^a UNIVERSITY OF SANTIAGO DE COMPOSTELA   (Spain)

Author keywords

Constraint satisfaction problems; Sleep apnea hypopnea syndrome; Temporal data mining; Temporal knowledge representation

Indexed keywords

CLUSTERING PROCEDURE; CONSISTENCY CHECKING; ITERATIVE ALGORITHM; SIMPLE TEMPORAL PROBLEMS; SLEEP APNEA-HYPOPNEA SYNDROMES; TEMPORAL CONSTRAINTS; TEMPORAL DATA MINING; TEMPORAL KNOWLEDGE REPRESENTATION;

ALGORITHMS; CONSTRAINT SATISFACTION PROBLEMS; ITERATIVE METHODS; KNOWLEDGE REPRESENTATION; SPEECH RECOGNITION;

DATA MINING;

AROUSAL; ARTICLE; BLOOD OXYGEN TENSION; DATA MINING; HUMAN; KNOWLEDGE; LEARNING ALGORITHM; PATTERN RECOGNITION; POLYSOMNOGRAPHY; PRIORITY JOURNAL; SEQUENCE DATABASE; SIMPLE TEMPORAL PROBLEM; SLEEP DISORDERED BREATHING; TACHYCARDIA; TIME SERIES ANALYSIS;

CONSTRAINT SATISFACTION PROBLEMS; SLEEP APNEA-HYPOPNEA SYNDROME; TEMPORAL DATA MINING; TEMPORAL KNOWLEDGE REPRESENTATION;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CLUSTER ANALYSIS; DATA MINING; DATABASES, FACTUAL; HUMANS; PATTERN RECOGNITION, AUTOMATED; POLYSOMNOGRAPHY; SLEEP APNEA, OBSTRUCTIVE; SOFTWARE DESIGN; TIME FACTORS;

EID: 84880044240     PISSN: 09333657     EISSN: 18732860     Source Type: Journal    
DOI: 10.1016/j.artmed.2013.03.006     Document Type: Article

References (44)

1. Bellazzi R., Ferrazzi F., Sacchi L Predictive data mining in clinical medicine: a focus on selected methods and applications Wiley Interdisc Rew: Data Mining and Knowledge Discovery 2011, 1(5):416-430.
2. Mitra S., Pal S., Mitra P Data mining in soft computing framework: a survey IEEE Transactions on Neural Networks 2002, 13(1):3-14.
3. Álvarez M.R., Félix P., Cariñena P., Otero A A data mining algorithm for inducing temporal constraint networks Computational intelligence for knowledge-based systems design; vol 6178 of Lecture Notes in Computer Science 2010, 300-309 Springer-Verlag, Berlin, Heidelberg, Germany E Hüllermeier, R Kruse, F Hoffmann (Eds.).
4. Álvarez M.R., Félix P., Cariñena P Mining temporal constraint networks by seed knowledge extension Artificial intelligence in medicine; vol 6747 of Lecture Notes in Computer Science 2011, 250-254 Springer-Verlag, Berlin, Heidelberg, Germany M Peleg, N Lavrac, C Combi (Eds.).
5. Agrawal R., Srikant R Fast algorithms for mining association rules in large databases Proceedings of the 20th international conference on very large data bases VLDB '94 1994, 487-499 Morgan Kaufmann Publishers Inc., San Francisco, CA, USA J.B Bocca, M Jarke, C Zaniolo (Eds.).
6. Dousson C., Duong T.V Discovering chronicles with numerical time constraints from alarm logs for monitoring dynamic systems Proceedings of the 16th international joint conference on artificial intelligence - vol 1 IJCAI'99 1999, 620-626 Morgan Kaufmann Publishers Inc., San Francisco, CA, USA T Dean (Ed.).
7. Laird P Identifying and using patterns in sequential data Algorithmic learning theory; vol 744 of Lecture Notes in Computer Science 1993, 1-18 Springer-Verlag, Berlin, Heidelberg, Germany K Jantke, S Kobayashi, E Tomita, T Yokomori (Eds.).
8. Mannila H., Toivonen H., Verkamo A Discovery of frequent episodes in event sequences Data Mining and Knowledge Discovery 1997, 1(3):259-289.
9. Wang J.T.L., Chirn G.W., Marr T.G., Shapiro B., Shasha D., Zhang K Combinatorial pattern discovery for scientific data: some preliminary results Proceedings of the 1994 ACM SIGMOD international conference on management of data SIGMOD'94 1994, 115-125 ACM, New York, NY, USA R.T Snodgrass, M Winslett (Eds.).
10. Agrawal R., Srikant R Mining sequential patterns Proceedings of the eleventh international conference on data engineering ICDE'95 1995, 3-14 IEEE Computer Society, Washington, DC, USA P.S Yu, A.L.P Chen (Eds.).
11. Srikant R., Agrawal R Mining sequential patterns: generalizations and performance improvements Proceedings of the 5th international conference on extending database technology: advances in database technology EDBT '96 1996, 3-17 Springer-Verlag, London, UK P.M.G Apers, M Bouzeghoub, G Gardarin (Eds.).
12. Pei J., Han J., Mortazavi-Asl B., Wang J., Pinto H., Chen Q., et al Mining sequential patterns by pattern-growth: the prefixspan approach IEEE Transactions on Knowledge and Data Engineering 2004, 16(11):1424-1440.
13. Hu Y.H., Huang T.C.K., Yang H.R., Chen Y.L On mining multi-time-interval sequential patterns Data & Knowledge Engineering 2009, 68(10):1112-1127.
14. Wu S.Y., Chen Y.L Discovering hybrid temporal patterns from sequences consisting of point- and interval-based events Data & Knowledge Engineering 2009, 68(11):1309-1330.
15. Chen Y., Wu S., Wang Y Discovering multi-label temporal patterns in sequences databases Information Sciences 2011, 181:398-418.
16. Casas-Garriga G Summarizing sequential data with closed partial orders SIAM international conference on data mining (SDM-05) 2005, 380-391 Society for Industrial and Applied Mathematics, Philadelphia, PA, USA H Kargupta, J Srivastava, C Kamath, A Goodman (Eds.).
17. Pei J., Wang H., Liu J., Wang K., Wang J., Yu P.S Discovering frequent closed partial orders from strings IEEE Transactions on Knowledge and Data Engineering 2006, 18(11):1467-1481.
18. Li Y., Ning P., Wang X., Jajodia S Discovering calendar-based temporal association rules Data & Knowledge Engineering 2003, 44:193-218.
19. Lu H., Feng L., Han J Beyond intratransaction association analysis: mining multidimensional intertransaction association rules ACM Transaction on Information Systems 2000, 18(4):423-454.
20. Mörchen F., Ultsch A Efficient mining of understandable patterns from multivariate interval time series Data Mining and Knowledge Discovery 2007, 15:181-215.
21. Garofalakis M.N., Rastogi R., Shim K Spirit: sequential pattern mining with regular expression constraints Proceedings of the 25th international conference on very large data bases VLDB'99 1999, 223-234 Morgan Kaufmann Publishers Inc., San Francisco, CA, USA M.P Atkinson, M.E Orlowska, P Valduriey, S.B Zdonik, M.L Brodie (Eds.).
22. Bellazzi R., Larizza C., Magni P., Bellazzi R Temporal data mining for the quality assessment of hemodialysis services Artificial Intelligence in Medicine 2005, 34(1):25-39.
23. Allen J Maintaining knowledge about temporal intervals Communications of the ACM 1983, 26(11):832-843.
24. Sacchi L., Larizza C., Combi C., Bellazzi R Data mining with temporal abstractions: learning rules from time series Data Mining and Knowledge Discovery 2007, 15(2):217-247.
25. Sacchi L., Larizza C., Magni P., Bellazzi R Precedence temporal networks to represent temporal relationships in gene expression data Journal of Biomedical Informatics 2007, 40(6):761-774.
26. Concaro S., Sacchi L., Cerra C., Fratino P., Bellazzi R Mining healthcare data with temporal association rules: improvements and assessment for a practical use 10th conference on artificial intelligence in medicine, AIME 2009; vol 5651 of Lecture Notes in Artificial Intelligence 2009, 16-25 Springer-Verlag, Berlin, Heidelberg, Germany C Combi, Y Shahar, A Abu-Hanna (Eds.).
27. Dechter R., Meiri I., Pearl J Temporal constraint networks Artificial Intelligence 1991, 49:61-95.
28. Wainer J., de Melo A A temporal extension to the parsimonious covering theory Artificial Intelligence in Medicine 1997, 10(3):235-255.
29. Gamper J., Nejdl W Abstract temporal diagnosis in medical domains Artificial Intelligence in Medicine 1997, 10(3):209-234.
30. Palma J., Juárez J., Campos M., Marín R Fuzzy theory approach for temporal model-based diagnosis: an application to medical domains Artificial Intelligence in Medicine 2006, 38:197-218.
31. Duftschmid G., Miksch S., Gall W Verification of temporal scheduling constraints in clinical practice guidelines Artificial Intelligence in Medicine 2002, 25(2):93-121.
32. Anselma L., Terenziani P., Montani S., Bottrighi A Towards a comprehensive treatment of repetitions, periodicity and temporal constraints in clinical guidelines Artificial Intelligence in Medicine 2006, 38(2):171-195.
33. Campos M., Juárez J., Palma J., Marín R Using temporal constraints for temporal abstraction Journal of Intelligent Information Systems 2010, 34(1):57-92.
34. Barro S., Marín R., Mira J., Patón A A model and a language for the fuzzy representation and handling of time Fuzzy Sets and Systems 1994, 61:153-175.
35. Deshpande A., Brandt C., Nadkarni P Temporal query of attribute-value patient data: utilizing the constraints of clinical studies International Journal of Medical Informatics 2003, 70(1):49-67.
36. Combi C., Gozzi M., Oliboni B., Juárez J., Marín R Temporal similarity measures for querying clinical workflows Artificial Intelligence in Medicine 2009, 46:37-54.
37. Bettini C., Wang X., Jajodia S A general framework for time granularity and its application to temporal reasoning Annals of Mathematics and Artificial Intelligence 1998, 10(1-2):29-58.
38. Han J., Kamber M., Pei J Data mining: concepts and techniques 2005, Morgan-Kaufmann, San Francisco, CA, USA.
39. Höppner F., Klawonn F., Kruse R., Runkler T Fuzzy clustering analysis: methods for classification, data analysis and image recognition 1999, Wiley, Chichester, UK.
40. Jain A.K Data clustering: 50 years beyond k-means Pattern Recognition Letters 2010, 31(8):651-666.
41. Flemons W Obstructive sleep apnea New England Journal of Medicine 2002, 347(7):498-504.
42. American Academy of Sleep Medicine Task Force Sleep-related breathing disorders in adults: recommendations for syndrome definition and measurement techniques in clinical research Sleep 1999, 22:667-689.
43. Otero A., Félix P., Álvarez M Algorithms for the analysis of polysomnographic recordings with customizable criteria Expert Systems with Applications 2011, 38:10133-10146.
44. Yager R., Filev D Approximate clustering via the mountain method IEEE Transactions on Systems, Man and Cybernetics 1994, 24(4):1279-1284.