Subscribe to RSS
DOI: 10.3414/ME10-01-0036
Mining Health Care Administrative Data with Temporal Association Rules on Hybrid Events
Publication History
received:
10 May 2010
accepted:
16 September 2010
Publication Date:
18 January 2018 (online)
Summary
Objective: The analysis of administrative health care data can be helpful to conveniently assess health care activities. In this context temporal data mining techniques can be suitably exploited to get a deeper insight into the processes underlying health care delivery. Inthis paperwepresent an algorithm for the extraction of temporal association rules (TARs) on sequences of hybrid events and its application on health care administrative databases.
Methods: We propose a method that extends TAR mining by managing hybrid events, namely events characterized by a heterogeneous temporal nature. Hybrid events include both point-like events (e.g. ambulatory visits) and interval-like events (e.g. drug consumption). The definition of user-defined rule templates can be optionally used to constrain the search only to the extraction of a subset of interesting rules. A TAR post-pruning strategy, based on a case-control approach, is also presented.
Results: We analyzed the administrative database of diabetic patients in charge to the regional health care agency (ASL) of Pavia. TAR mining allowed to find patterns specifically related to the diabetic population in comparison with a control group, as well as to check the compliance of the actual clinical careflow with the ASL recommendations.
Conclusion: The experimental results highlighted the main potentials of the algorithm, such as the opportunity to detect interesting temporal relationships between diagnostic or therapeutic patterns, or to check the adherence of past temporal behaviors to specific expected paths (e.g. guidelines) or to discover new knowledge that could be implicitly hidden in the data.
-
References
- 1 Stefanelli M. The socio-organizational age of artificial intelligence in medicine. Artificial Intelligence in Medicine 2001; 23 (01) 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 (04) 750-767.
- 3 Post AR, Harrison JH. Temporal data mining. Clinics in Laboratory Medicine 2008; 28 (01) 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 (02) 1178-1189.
- 17 Bellazzi R, Larizza C, Magni P, Bellazzi R. Temporal data mining for the quality assessment of hemo-dialysis services. Artificial Intelligence in Medicine 2005; 34 (01) 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 (02) 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 (04) 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 (03) 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 (02) 101-113.
- 26 Combi C, Franceschet M, Peron A. Representing and Reasoning about Temporal Granularities. Journal of Logic and Computation 2004; 14 (01) 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 (05) 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 (02) 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 (03) 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 (06) 970-974.
- 39 Siadaty MS, Knaus WA. Locating previously unknown patterns in data-mining results: a dual data-and 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 (01) 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.