首页|Findings from Mayo Clinic Broaden Understanding of Machine Learning (Novel Machine Learning Model To Improve Performance of an Early Warning System In Hospitalized Patients: a Retrospective Multisite Cross-validation Study)
Findings from Mayo Clinic Broaden Understanding of Machine Learning (Novel Machine Learning Model To Improve Performance of an Early Warning System In Hospitalized Patients: a Retrospective Multisite Cross-validation Study)
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NETL
NSTL
Elsevier
New research on Machine Learning is the subject of a report. According to news reporting out of Rochester, Minnesota, by NewsRx editors, research stated, "Threshold-based early warning systems (EWS) are used to predict adverse events (Aes). Machine learning (ML) algorithms that incorporate all EWS scores prior to an event may perform better in hospitalized patients." Our news journalists obtained a quote from the research from Mayo Clinic, "The deterioration index (DI) is a proprietary EWS. A threshold of DI ＞60 is used to predict a composite AE: all-cause mortality, cardiac arrest, transfer to intensive care, and evaluation by the rapid response team in practice. The DI scores were collected for adult patients (＞= 18 y-o) hospitalized on medical or surgical services during 8-23-2021 to 3-31-2022 from four different Mayo Clinic sites in the United States. A novel ML model was developed and trained on a retrospective cohort of hospital encounters. DI scores were represented in a high -dimensional space using random convolution kernels to facilitate training of a classifier and the area under the receiver operator characteristics curve (AUC) was calculated. Multiple time intervals prior to an AE were analyzed. A leave-one-out cross-validation protocol was used to evaluate performance across separate clinic sites. Three different classifiers were trained on 59,617 encounter-derived DI scores in highdimensional feature space and the AUCs were compared to two threshold models. All three tested classifiers improved the AUC over the threshold approaches from 0.56 and 0.57 to 0.76, 0.85 and 0.94. Time interval analysis of the top performing classifier showed best accuracy in the hour before an event occurred (AUC 0.91), but prediction held up even in the 12 h before an AE (AUC 0.80 at minus 12 h, 0.81 at minus 9 h, 0.85 at minus 6 h, and 0.88 at minus 3 h before an AE). Multisite cross-validation using leave-one-out approach on data from four different clinical sites showed broad generalization performance of the top performing ML model with AUC of 0.91, 0.91, 0.95, and 0.91. A novel ML model that incorporates all the longitudinal DI scores prior to an AE in a hospitalized patient performs better at outcome prediction than the currently used threshold model."
RochesterMinnesotaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningMayo Clinic
NETL
NSTL
Elsevier
