Mining health care administrative data with temporal association rules on hybrid events

Methods of Information in Medicine

Volumn 50, Issue 2, 2011, Pages 166-179

  Concaro, S ^a,b   Sacchi, L ^a   Cerra, C ^b   Fratino, P ^b   Bellazzi, Riccardo ^a  

^a UNIVERSITY OF PAVIA   (Italy)

^b ASL   (Italy)

Author keywords

Administrative healthcare data; Hybrid events; Temporal association rules; Temporal data mining

Indexed keywords

ALGORITHM; ARTICLE; BIOLOGY; DATA MINING; HEALTH CARE DELIVERY; HUMAN; INSULIN DEPENDENT DIABETES MELLITUS; METHODOLOGY; NON INSULIN DEPENDENT DIABETES MELLITUS;

ALGORITHMS; COMPUTATIONAL BIOLOGY; DATA MINING; DELIVERY OF HEALTH CARE; DIABETES MELLITUS, TYPE 1; DIABETES MELLITUS, TYPE 2; HUMANS;

EID: 80051722310     PISSN: 00261270     EISSN: None     Source Type: Journal    
DOI: 10.3414/ME10-01-0036     Document Type: Article

References (44)

1. Stefanelli M. The socio-organizational age of artificial intelligence in medicine. Artificial Intelligence in Medicine 2001; 23 (1): 25-47.
2. Roddick JF, Spiliopoulou M. A Survey of Temporal Knowledge Discovery Paradigms and Methods. IEEE Transactions on Knowledge and Data Engineering 2002; 14 (4): 750-767.
3. Post AR, Harrison JH. Temporal data mining. Clinics in Laboratory Medicine 2008; 28 (1): 83-100.
4. Mitsa T. Temporal Data Mining. CRC Press (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series); 2010. p 395.
5. Agrawal R, Srikant R. Fast Algorithms for Mining Association Rules in Large Databases. In: 20th International Conference on Very Large Data Bases. Santiago de Chile. Morgan Kaufmann; 1994. pp 487-499.
6. Srikant R, Agrawal R. Mining Sequential Patterns: Generalizations and Performance Improvements. In: 5th International Conference on Extending Database Technology: Advances in Database Technology. Avignon. Springer-Verlag; 1996. pp 3-17.
7. Pei J, Han J, Asl BM, Pinto H, Chen Q, Dayal U, Hsu M. PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth. In: 17th International Conference on Data Engineering. Heidelberg. IEEE Computer Society; 2001. pp 215-224.
8. Ng RT, Lakshmanan LVS, Han J, Pang A. Exploratory mining and pruning optimizations of constrained associations rules. In: ACM-SIGMOD International Conference on Management of Data. Seattle: ACM; 1998. pp 13-24.
9. Bayardo RJ, Agrawal R, Gunopulos D. Constraint-Based Rule Mining in Large, Dense Databases. In: 15th International Conference on Data Engineering. Sydney. IEEE Computer Society; 1999. pp 188-197.
10. Agrawal R, Srikant R. Mining Sequential Patterns. In: 11th International Conference on Data Engineering. Taipei. IEEE Computer Society; 1995. pp 3-14.
11. Zaki MJ. SPADE: An Efficient Algorithm for Mining Frequent Sequences. Machine Learning 2001; 42 (1-2): 31-60.
12. Ayres J, Flannick J, Gehrke J, Yiu T. Sequential PAttern mining using a bitmap representation. In: 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Edmonton: ACM; 2002. pp 429-435.
13. Kam PS, Fu AWC. Discovering Temporal Patterns for Interval-Based Events. In: 2nd International Conference on Data Warehousing and Knowledge Discovery. London: Springer-Verlag; 2000. pp 317-326.
14. Höppner F, Klawonn F. Finding Informative Rules in Interval Sequences. In: 4th International Conference on Advances in Intelligent Data Analysis. Cascais: Springer-Verlag; 2001. pp 125-134.
15. Patel D, Hsu W, Lee ML. Mining relationships among interval-based events for classification. In: Proceedings of the 2008 ACM SIGMOD international conference on Management of data. Series Mining relationships among interval-based events for classification. Vancouver, Canada: ACM; 2008.
16. Zhang L, Chen G, Brijs T, Zhang X. Discovering during-temporal patterns (DTPs) in large temporal databases. Expert Systems with Applications: An International Journal 2008; 34 (2): 1178-1189.
17. 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.
18. 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.
19. Moskovitch R, Shahar Y. Medical temporal-knowledge discovery via temporal abstraction. AMIA Annu Symp Proc; 2009. pp 452-456.
20. Jin HW, Chen J, He H, Williams GJ, Kelman C, O'Keefe CM. Mining unexpected temporal associations: applications in detecting adverse drug reactions. IEEE Trans Inf Technol Biomed 2008; 12 (4): 488-500.
21. Li J, Fu AW-c, He H, Chen J, Jin H, McAullay D, Williams G, Sparks R, Kelman C. Mining risk patterns in medical data. In: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining. Series Mining risk patterns in medical data. Chicago, Illinois, USA: ACM; 2005.
22. Norén GN, Hopstadius J, Bate A, Star K, Edwards IR. Temporal pattern discovery in longitudinal electronic patient records. Data Mining and Knowledge Discovery 2010; 20 (3): 361-387.
23. Böhlen MH, Busatto R, Jensen CS. Point-Versus Interval-Based Temporal Data Models. In: 14th International Conference on Data Engineering. Orlando. IEEE Computer Society; 1998. pp 192-200.
24. Shahar Y. A framework for knowledge-based temporal abstraction. Arificial Intelligence 1997; 90 (1-2): 79-133.
25. Adlassnig KP, Combi C, Das AK, Keravnou ET, Pozzi G. Temporal representation and reasoning in medicine: Research directions and challenges. Artificial Intelligence in Medicine 2006; 38 (2): 101-113.
26. Combi C, Franceschet M, Peron A. Representing and Reasoning about Temporal Granularities. Journal of Logic and Computation 2004; 14 (1): 51-77.
27. Combi C, Pinciroli F, Pozzi G. Managing different time granularities of clinical information by an interval-based temporal data model. Methods Inf Med 1995; 34 (5): 458-474.
28. Bettini C, Wang XS, Jajodia S. Testing complex temporal relationships involving multiple granularities and its application to data mining (extended abstract). In: 15th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems. Montreal: ACM; 1996. pp 68-78.
29. Allen JF. Towards a general theory of action and time. Artificial Intelligence 1984; 23 (2): 123-154.
30. Vilain MB. A system for reasoning about time. In: 2nd National Conference in Artificial Intelligence. Pittsburgh: AAAI; 1982. pp 197-201.
31. Han J, Fu Y. Discovery of Multiple-Level Association Rules from Large Databases. In: 21th International Conference on Very Large Data Bases. Zurich: Morgan Kaufmann; 1995. pp 420-431.
32. Raj R, O'Connor MJ, Das AK. An ontology-driven method for hierarchical mining of temporal patterns: application to HIV drug resistance research. In: AMIA Annual Symposium. Chicago: AMIA; 2007. pp 614-619.
33. Bayardo RJ, Agrawal R. Mining the most interesting rules. In: 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Diego: ACM; 1999. pp 145-154.
34. Zaki MJ. Generating non-redundant association rules. In: 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Boston: ACM; 2000. pp 34-43.
35. Tan PN, Kumar V, Srivastava J. Selecting the right interestingness measure for association patterns. In: 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Edmonton: ACM; 2002. pp 32-41.
36. Ohsaki M, Abe H, Tsumoto S, Yokoi H, Yamaguchi T. Evaluation of rule interestingness measures in medical knowledge discovery in databases. Artificial Intelligence in Medicine 2007; 41 (3): 177-196.
37. Klemettinen M, Mannila H, Ronkainen P, Toivonen H, Verkamo AI. Finding interesting rules from large sets of discovered association rules. In: 3rd International Conference on Information and Knowledge Management. Gaithersburg: ACM; 1994. 401-407.
38. Silberschatz A, Tuzhilin A. What Makes Patterns Interesting in Knowledge Discovery Systems. IEEE Transactions on Knowledge and Data Engineering 1996; 8 (6): 970-974.
39. Siadaty MS, Knaus WA. Locating previously unknown patterns in data-mining results: a dual dataand knowledge-mining method. BMC Medical Informatics and Decision Making 2006; 6: 13.
40. Svatek V, Riha A, Peleska J, Rauch J. Analysis of guideline compliance-a data mining approach. Studies in Health Technology and Informatics 2004; 101: 157-161.
41. Razavi AR, Gill H, Ahlfeldt H, Shahsavar N. A data mining approach to analyze non-compliance with a guideline for the treatment of breast cancer. Studies in Health Technology and Informatics 2007; 129 (Pt 1): 591-595.
42. Concaro S, Sacchi L, Cerra C, Bellazzi R. Mining administrative and clinical diabetes data with temporal association rules. Stud Health Technol Inform 2009; 150: 574-578.
43. Concaro S, Sacchi L, Cerra C, Fratino P, Bellazzi R. Mining Healthcare Data with Temporal Association Rules: Improvements and Assessment for a Practical Use. In: 12th Conference on Artificial Intelligence in Medicine. Verona: Springer-Verlag; 2009. pp 16-25.
44. Concaro S, Sacchi L, Cerra C, Stefanelli M, Fratino P, Bellazzi R. Temporal Data Mining for the Assessment of the Costs Related to Diabetes Mellitus Pharmacological Treatment. In: AMIA Annual Symposium. San Francisco: AMIA; 2009. pp 119-123.