Early identification of ICU patients at risk of complications: Regularization based on robustness and stability of explanations

Navigating the machine learning pipeline: a scoping review of inpatient delirium prediction models

“…The search strategy identified a total of 921 records; after duplicate removal and title and abstract screening, 114 full-text studies were retrieved, of which 3916–54 met the selection criteria for inclusion in the final analysis (figure 2).…”

“…Study characteristics are summarised in online supplemental table 1. Studies originated from the USA (n=12),17 19–23 25 41 43 50 51 54 Austria (n=9),24 28–31 33 39 47 48 China (n=6),26 32 35 49 52 53 Germany (n=3),37 45 46 South Korea (n=3),27 40 44 Canada (n=3),30 36 38 Brazil (n=1),16 Japan (n=1),34 Spain (n=1)18 and one study was labelled as international 42. Over the 6-year distribution of publications to June 2022, most studies were published in 2021 (n=10) and the first half of 2022 (n=12), indicating considerable growth in research in this area since the publication of previous reviews of studies published up to 2019 11 12.…”

“…Thirty of thirty-nine publications described the training and internal validation of a delirium model,17 18 21–26 30 32–41 43 44 46–54 with investigators of 6 of these studies (20%) externally validating their model in a subsequent paper 16 19 20 27 29 42. Investigators of three studies (10%) implemented and evaluated their model in real-time clinical workflows,2 8 31 45 but no publications described monitoring or optimising a deployed model.…”

“…Second, similar to the findings of previous reviews,11 12 most models described in our scoping review did not mature past the stage of internal validation. Only six studies validated their model on an external dataset16 19 20 27 29 42 despite evidence that models that perform well on ‘hold out’ training data usually have lower performance when applied to more noisy datasets from different institutions due to model overfitting 5…”

Ubiquitous and smart healthcare monitoring frameworks based on machine learning: A comprehensive review

“…Change management is defined as the process of continuously renewing the infrastructure, and capabilities of healthcare facilities, to meet the ever-changing needs of patients. However in the healthcare context, once the ML model is implemented, a major issue is whether it will continue to apply the same prediction logic as it was originally intended [109] . Following the worldwide adoption of such systems, interpretability of intelligent systems has become a necessity to explain and justify the decisions made by these systems, especially in the health sector [110] .…”