English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Performance monitoring of machine learning (ML)-based risk prediction modelsin healthcare is complicated by the issue of confounding medical interventions(CMI): when an algorithm predicts a patient to be at high risk for an adverseevent, clinicians are more likely to administer prophylactic treatment andalter the very target that the algorithm aims to predict. A simple approach isto ignore CMI and monitor only the untreated patients, whose outcomes remainunaltered. In general, ignoring CMI may inflate Type I error because (i)untreated patients disproportionally represent those with low predicted riskand (ii) evolution in both the model and clinician trust in the model caninduce complex dependencies that violate standard assumptions. Nevertheless, weshow that valid inference is still possible if one monitors conditionalperformance and if either conditional exchangeability or time-constantselection bias hold. Specifically, we develop a new score-based cumulative sum(CUSUM) monitoring procedure with dynamic control limits. Through simulations,we demonstrate the benefits of combining model updating with monitoring andinvestigate how over-trust in a prediction model may delay detection ofperformance deterioration. Finally, we illustrate how these monitoring methodscan be used to detect calibration decay of an ML-based risk calculator forpostoperative nausea and vomiting during the COVID-19 pandemic.

Monitoring machine learning (ML)-based risk prediction algorithms in the presence of confounding medical interventions