Mining Disease Courses across Organizations: A Methodology Based on Process Mining of Diagnosis Events Datasets.

Abstract

This work proposes the use of Process Mining methodologies on healthcare datasets containing diagnosis information as a means to identify the course of a disease across organizations. Datasets containing diagnosis information for administrative purposes are a good candidate due to its standardized format, widespread availability and coverage. We present a methodology to preprocess, cluster and mine diagnosis information and the results of a preliminary use case with diabetes type II. Some meaningful disease courses have been found but less useful patterns do also emerge. Future work involves lowering the level of granularity chosen (ICD three digit codes) and extending the time span of the data available (three years).