To improve patient safety, we should identify which alarms are really needed and where the difficulties lie for the correct alarm programming.
To improve patient safety, we should seek to identify which alarms are really needed and where the difficulties lie for the correct alarm programming in the monitors and support respiratory equipment.
Our results demonstrate that the proposed method can effectively reduce false alarm rate, provide effective and correct leak alarms, and give early warning to operators.
While there are noticeable differences between the topics, in terms of the performance of the classifier, these differences do not seem to translate into large practical differences in the overall rate of the New Update Alerts, nor in the overall rate of correct alerts or false alarms.
In the final GLM, we included regressors for Hits, Correct Rejections, False Alarms, and Misses, crossed with the response phase (modeled at the trial onset) and feedback phase (modeled at the feedback presentation) of each trial.
Continuous old/new confidence ratings were dichotomized as old (<0) and new (>0) to enable calculation of hits, false alarms, correct rejections, and misses, as well as d' (using Macmillan and Kaplan63 adjustments as required).
For a given procedure, the ideal point on the ROC is, in part, a function of the subjective values associated with hits, false alarms, correct rejections, and misses (see Equation 1.14 in Green & Swets, 1966, p. 22).
These data were collected based on the basic principles of signal detection theory (SDT) [ 34], which can be used to measure four conditions describing device performance with respect to: hits, false alarms, correct rejects, and misses.
Event-related stick-function regressors were used to model trials corresponding to one of nine conditions: immediate recollection, immediate familiarity, immediate forgotten, delayed recollection, delayed familiarity, delayed forgotten, correct rejections, false alarms, and no-response trials.
According to this model, remember estimates are "remember-responses" corrected for false-alarms and familiarity estimates are derived by dividing proportions of "know" responses (corrected for false alarms, Kcorr) by 1 − Rcorr [Fcorr = Kcorr/(1 − Rcorr)] [Yonelinas and Jacoby, 1995].
