| Literature DB >> 29493360 |
Arianna Dagliati1,2, Valentina Tibollo1, Giulia Cogni1, Luca Chiovato1, Riccardo Bellazzi1,3, Lucia Sacchi3.
Abstract
In this work we describe the application of a careflow mining algorithm to detect the most frequent patterns of care in a type 2 diabetes patients cohort. The applied method enriches the detected patterns with clinical data to define temporal phenotypes across the studied population. Novel phenotypes are discovered from heterogeneous data of 424 Italian patients, and compared in terms of metabolic control and complications. Results show that careflow mining can help to summarize the complex evolution of the disease into meaningful patterns, which are also significant from a clinical point of view.Entities:
Keywords: data mining; temporal data analytics; type 2 diabetes complications
Mesh:
Year: 2018 PMID: 29493360 PMCID: PMC5851241 DOI: 10.1177/1932296818761751
Source DB: PubMed Journal: J Diabetes Sci Technol ISSN: 1932-2968