Application‐specific fine tuning of multi‐parameter patient monitors. Issue 23 (1st November 2013)

• Record Type: Journal Article
• Authors: Vaijeyanthi, V.; Kumar, C.S.; Ramachandran, K.I.; Joy, J.K; Kumar, A.A.
• Is Part Of: Electronics letters. Volume 49:Issue 23(2013)
• Page Start: 1432
• Page End: 1433
• Publication Date: 2013-11-01
• Subjects: biomedical equipment -- decision trees -- health care -- learning (artificial intelligence) -- medical computing -- patient monitoring; multiparameter patient monitors -- alarm accuracy -- intensive care unit -- sensitivity -- detection error tradeoff method -- tradeoff specificity -- health care quality -- classification regression tree algorithm -- application specific fine tuning; Electronics -- Periodicals; 621.381
• DOI: 10.1049/el.2013.2273
• Languages: English
• ISSNs: 0013-5194

Abstract

Abstract : 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.