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Methods Inf Med 2011; 50(02): 166-179
DOI: 10.3414/ME10-01-0036
Original Article
Schattauer GmbH

Mining Health Care Administrative Data with Temporal Association Rules on Hybrid Events

S. Concaro
1   Dipartimento di Informatica e Sistemistica, Università di Pavia, Pavia, Italy
2   Sistema Informativo Aziendale e Controllo di Gestione, ASL,Pavia, Italy
,
L. Sacchi
1   Dipartimento di Informatica e Sistemistica, Università di Pavia, Pavia, Italy
,
C. Cerra
2   Sistema Informativo Aziendale e Controllo di Gestione, ASL,Pavia, Italy
,
P. Fratino
2   Sistema Informativo Aziendale e Controllo di Gestione, ASL,Pavia, Italy
,
R. Bellazzi
1   Dipartimento di Informatica e Sistemistica, Università di Pavia, Pavia, Italy
› Author Affiliations
Further Information

Publication History

received: 10 May 2010

accepted: 16 September 2010

Publication Date:
18 January 2018 (online)

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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.

Keywords

Temporal data mining - temporal association rules - hybrid events - administrative healthcare data
 

  • References

  • 1 Stefanelli M. The socio-organizational age of artificial intelligence in medicine. Artificial Intelligence in Medicine 2001; 23 (01) 25-47.
    CrossrefPubMedSearch in Google Scholar
  • 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.
    CrossrefPubMedSearch in Google Scholar
  • 3 Post AR, Harrison JH. Temporal data mining. Clinics in Laboratory Medicine 2008; 28 (01) 83-100.
    CrossrefPubMedSearch in Google Scholar
  • 4 Mitsa T. Temporal Data Mining. CRC Press; (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series); 2010. p 395.
    Search in Google Scholar
  • 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.
    Search in Google Scholar
  • 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.
    Search in Google Scholar
  • 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.
    Search in Google Scholar
  • 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.
    Search in Google Scholar
  • 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.
    Search in Google Scholar
  • 10 Agrawal R, Srikant R. Mining Sequential Patterns. In: 11th International Conference on Data Engineering. Taipei.: IEEE Computer Society; 1995. pp 3-14.
    Search in Google Scholar
  • 11 Zaki MJ. SPADE: An Efficient Algorithm for Mining Frequent Sequences. Machine Learning 2001; 42 1–2 31-60.
    CrossrefPubMedSearch in Google Scholar
  • 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.
    Search in Google Scholar
  • 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.
    Search in Google Scholar
  • 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.
    Search in Google Scholar
  • 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
    Search in Google Scholar
  • 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.
    CrossrefPubMedSearch in Google Scholar
  • 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.
    CrossrefPubMedSearch in Google Scholar
  • 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.
    CrossrefPubMedSearch in Google Scholar
  • 19 Moskovitch R, Shahar Y. Medical temporal-knowledge discovery via temporal abstraction. AMIA Annu Symp Proc; 2009 pp 452-456.
    PubMedSearch in Google Scholar
  • 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.
    CrossrefPubMedSearch in Google Scholar
  • 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
    Search in Google Scholar
  • 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.
    CrossrefPubMedSearch in Google Scholar
  • 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.
    Search in Google Scholar
  • 24 Shahar Y. A framework for knowledge-based temporal abstraction. Arificial Intelligence 1997; 90 1–2 79-133.
    PubMedSearch in Google Scholar
  • 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.
    CrossrefPubMedSearch in Google Scholar
  • 26 Combi C, Franceschet M, Peron A. Representing and Reasoning about Temporal Granularities. Journal of Logic and Computation 2004; 14 (01) 51-77.
    CrossrefPubMedSearch in Google Scholar
  • 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.
    Thieme ConnectPubMedSearch in Google Scholar
  • 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.
    Search in Google Scholar
  • 29 Allen JF. Towards a general theory of action and time. Artificial Intelligence 1984; 23 (02) 123-154.
    CrossrefPubMedSearch in Google Scholar
  • 30 Vilain MB. A system for reasoning about time. In: 2nd National Conference in Artificial Intelligence. Pittsburgh: AAAI; 1982. pp 197-201.
    Search in Google Scholar
  • 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.
    Search in Google Scholar
  • 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.
    Search in Google Scholar
  • 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.
    Search in Google Scholar
  • 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.
    Search in Google Scholar
  • 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.
    Search in Google Scholar
  • 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.
    CrossrefPubMedSearch in Google Scholar
  • 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.
    Search in Google Scholar
  • 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.
    CrossrefPubMedSearch in Google Scholar
  • 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.
    CrossrefPubMedSearch in Google Scholar
  • 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.
    PubMedSearch in Google Scholar
  • 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.
    PubMedSearch in Google Scholar
  • 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.
    PubMedSearch in Google Scholar
  • 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.
    Search in Google Scholar
  • 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.
    Search in Google Scholar