Indirect association rules mining in clinical texts

Abstract

This paper presents a method for structured information extraction from patient status description. The proposed approach is based on indirect association rules mining (IARM) in clinical text. This method is language independent and unsupervised, that makes it suitable for applications in low resource languages. For experiments are used data from Bulgarian Diabetes Register. The Register is automatically generated from pseudonymized 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 data collections with patient status data only. The great variety of possible values (conditions) makes this task challenging. The classical frequent itemsets mining algorithms identify just few frequent pairs only even for small minimal support. The results of the proposed IARM method show that attribute-value pairs of anatomical organs/systems and their condition can be identified automatically. IARM approach allows extraction of indirect relations between item pairs with support below the minimal support.