Open AccessApplication‐specific fine tuning of multi‐parameter patient monitors
Author(s) -
Vaijeyanthi V.,
Kumar C.S.,
Ramachandran K.I.,
Joy J.K,
Kumar A.A.
Publication year - 2013
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2013.2273
Subject(s) - sensitivity (control systems) , alarm , versa , computer science , baseline (sea) , intensive care unit , false alarm , health care , constant false alarm rate , data mining , algorithm , machine learning , medicine , engineering , intensive care medicine , geology , economic growth , economics , aerospace engineering , oceanography , database , electronic engineering
Multi‐parameter patient monitors (MPMs) have become increasingly important in providing quality health care to patients. A high alarm accuracy (sensitivity) will need a lower threshold for alarm detection which will lead to lower no‐alarm accuracy (specificity) and vice‐versa. MPMs when used in an intensive care unit (ICU) need to have high sensitivity. However they need to have high specificity when used in in‐patient wards for regular health check‐ups. Proposed is a novel algorithm to trade‐off specificity for sensitivity and vice‐versa depending on the application. The proposed method is referred as detection error trade‐off, trade‐off specificity for better sensitivity and vice‐versa. The algorithm will help to extend the application of MPMs from ICUs to in‐patient wards and thus enhance the quality of health care. Experiments have been conducted with an MPM using the classification and regression tree algorithm. By using the proposed algorithm, an improvement of 10.18% in sensitivity was obtained by trading‐off 0.40% in specificity. Furthermore, the overall performance of the refined system is 1.15% better than the baseline system.