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Interval Coded Scoring Extensions for Larger Problems
Boekbijdrage - Boekhoofdstuk Conferentiebijdrage
© 2017 IEEE. In many medical problems, clinicians are suffering from information overload. Nowadays, clinical decision support systems (CDSS) are widely available to alleviate this issue. However, they are often not transparent, in contrast to medical scoring systems originating in the clinical world itself. This work presents an extension of Interval Coded Scoring System (ICS), an approach for semi-automatic data-driven extraction of such scoring systems from clinical data. It focuses on two ICS improvements for large or high-dimensional datasets. Firstly, it offers an alternative elastic net implementation. This can be solved efficiently due to equivalence with Support Vector Machines and the existing efficient solver for its primal formulation. Secondly, an informed preselection of the variables allows to lower ICS' computational burden. ICS is applied to problems as diverse as arrhythmia diagnosis, breast cancer prognosis, functional capacity assessment and diagnosis of spinal diseases and obtains good results. In particular, a comparison on the arrhythmia database shows the importance of using the extensions which yield similar performance as the original ICS while shortening the execution time with a factor of ten.
Boek: Proc. of the 2nd ICTS4eHealth workshop, 22nd IEEE symposium on Computers and Communications
Pagina's: 198 - 203
Jaar van publicatie:2017