Paper Info
Title
An Interpretable Data-Driven Approach For Rules Construction: Application To Cardiovascular Risk Assessment
Abstract
The development of models able to produce an understandable decision by the clinicians is of great importance to support their decision. Therefore, the research of methodologies able to extract useful knowledge from existing datasets, as well as to integrate this knowledge into the current clinical evidence, is a key aspect in the enhancement of the clinical decision. This work focuses on the development of interpretable models to assess the patient's condition based on supervised clustering theories, enabling the discovery of a set of features that best represents that condition. At the same time, the technique is supported on a structure that enables the formulation of simple and interpretable rules. Despite its general applicability, the proposed methodology is applied to coronary artery disease (CAD), particularly, in the risk of death assessment (30 days after the admission) of patients that have been admitted to the emergency unit. The validation is performed using a real dataset with Acute Coronary Syndromes, provided by the Portuguese Society of Cardiology. While the methodology produces simple and interpretable rules, the performance achieves an improvement of 7% in relation to geometric mean, when compared with GRACE model (commonly used in Portugal).
Year
DOI
Venue
2017
10.1109/EMBC.2017.8037401
2017 39TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Field
DocType
Volume
CAD,Data mining,Data-driven,Computer science,Risk assessment,Cluster analysis
Conference
2017
ISSN
Citations 
PageRank 
1094-687X
0
0.34
References 
Authors
3
6
Name
Order
Citations
PageRank
Diana Mendes102.03
S. Paredes22610.61
Teresa Rocha3146.30
Paulo Carvalho425047.68
J Henriques53314.56
J Morais673.02