Mining comorbidity patterns using retrospective analysis of big collection of outpatient records

Abstract

Background: Studying comorbidities of disorders is important for detection and prevention. For discovering frequent patterns of diseases we can use retrospective analysis of population data, by filtering events with common properties and similar significance. Most frequent pattern mining methods do not consider contextual information about extracted patterns. Further data mining developments might enable more efficient applications in specific tasks like comorbidities identification. Methods:We propose a cascade data mining approach for frequent pattern mining enriched with context information, including a new algorithm MIxCO for maximal frequent patterns mining. Text mining tools extract entities from free text and deliver additional context attributes beyond the structured information about the patients. Results:The proposed approach was tested using pseudonymised reimbursement requests (outpatient records) submitted to the Bulgarian National Health Insurance Fund in 2010-2016 for more than 5 million citizens yearly. Experiments were run on 3 data collections. Some known comorbidities of Schizophrenia, Hyperprolactinemia and Diabetes Mellitus Type 2 are confirmed; novel hypotheses about stable comorbidities are generated. The evaluation shows that MIxCO is efficient for big dense datasets. Conclusion:Explicating maximal frequent itemsets enables to build hypotheses concerning the relationships between the exogeneous and endogeneous factors triggering the formation of these sets. MixCO will help to identify risk groups of patients with a predisposition to develop socially-significant disorders like diabetes. This will turn static archives like the Diabetes Register in Bulgaria to a powerful alerting and predictive framework.

Guest editorial: special issue on “Artificial Intelligence in Health and Medicine”

“…The article, titled "Mining Comorbidity Patterns Using Retrospective Analysis of Big Collection of Outpatient Records" by Boytcheva et al [11] presents an innovative drug extractor algorithm for mining sets of events, which identifies strong co-occurrence patterns. They design a numerical value extractor where experiments run on a relatively large corpus of Outpatient Records (ORs).…”

Exploring Interval Graphs of Rare Diseases in Retrospective Analysis of Outpatient Records

“…Hyperprolactinaemia is classified as rare disorder. The collections are extracted by using a Business Intelligence tool (BITool) [4] from the repository of ORs for approx. 5 million patients for a 3-years period.…”

“…Most FPM and FSM methods do not consider contextual information about extracted patterns. Further development of such DM methods is needed [4].…”

“…ℱ = { | ⊆ sup ( ) ≥ }. A frequent item- 4 Chronic diseases, WHO, http://www.who.int/topics/chronic_diseases/en/ set ∈ ℱ is called maximal if it has no frequent supersets. Let ℳ denote the set of all maximal frequent itemsets, i.e.…”

“…We apply a tabular method MixCO [4] for MFI mining using a vertical database, depth-first traversal as well as set intersection and diffsets. The context information is represented as attributevalue tuples for each patient.…”