Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
The SMART Retrieval System—Experiments in Automatic Document Processing
The SMART Retrieval System—Experiments in Automatic Document Processing
ICDM'11 Proceedings of the 11th international conference on Advances in data mining: applications and theoretical aspects
Expert Systems with Applications: An International Journal
Mining rare cases in post-operative pain by means of outlier detection
ISSPIT '11 Proceedings of the 2011 IEEE International Symposium on Signal Processing and Information Technology
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
The analysis of medical data is a challenging task for health care systems since a huge amount of interesting knowledge can be automatically mined to effectively support both physicians and health care organizations. This paper proposes a data analysis framework based on a multiple-level clustering technique to identify the examination pathways commonly followed by patients with a given disease. This knowledge can support health care organizations in evaluating the medical treatments usually adopted, and thus the incurred costs. The proposed multiple-level strategy allows clustering patient examination datasets with a variable distribution. To measure the relevance of specific examinations for a given disease complication, patient examination data has been represented in the Vector Space Model using the TF-IDF method. As a case study, the proposed approach has been applied to the diabetic care scenario. The experimental validation, performed on a real collection of diabetic patients, demonstrates the effectiveness of the approach in identifying groups of patients with a similar examination history and increasing severity in diabetes complications.