Clustering Cardiovascular Risk Trajectories of Patients with Type 2 Diabetes Using Process Mining

被引：0

作者：

Pebesma, Joyce ^{[1
]}

Martinez-Millana, Antonio ^{[2
]}

Sacchi, Lucia ^{[3
]}

Fernandez-Llatas, Carlos ^{[2
]}

De Cata, Pasquale ^{[4
]}

Chiovato, Luca ^{[4
]}

Bellazzi, Riccardo ^{[3
]}

Traver, Vicente ^{[2
]}

机构：

[1] Univ Twente, Drienerlolaan 5, NL-7522 NB Enschede, Netherlands

[2] Univ Politecn Valencia, ITACA, Camino Vera Sn, Valencia 46022, Spain

[3] Univ Pavia, Dept Elect Comp & Biomed Engn, Pavia, Italy

[4] ICS Maugeri, UO Med Interna & Endocrinol, Pavia, Italy

来源：

2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) | 2019年

关键词：

DISEASE; DIFFERENCE;

D O I：

10.1109/embc.2019.8856507

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Patients with type 2 diabetes have a higher chance of developing cardiovascular diseases and an increased odds of mortality. Reliability of randomized clinical trials is continuously judged due to selection, attrition and reporting bias. Moreover, cardiovascular risk is frequently assessed in cross-sectional studies instead of observing the evolution of risk in longitudinal cohorts. In order to correctly assess the course of cardiovascular risk in patients with type 2 diabetes, we applied process mining techniques based on the principles of evidence-based medicine. Using a validated formulation of the cardiovascular risk, process mining allowed to cluster frequent risk pathways and produced 3 major trajectories related to risk management: high risk, medium risk and low risk. This enables the extraction of meaningful distributions, such as the gender of the patients per cluster in a human understandable manner, leading to more insights to improve the management of cardiovascular diseases in type 2 diabetes patients.

引用

页码：341 / 344

页数：4

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