Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare
Abstract Machine learning (ML) and artificial intelligence (AI) algorithms have the potential to
derive insights from clinical data and improve patient outcomes. However, these highly …
derive insights from clinical data and improve patient outcomes. However, these highly …
Data drift in medical machine learning: implications and potential remedies
Data drift refers to differences between the data used in training a machine learning (ML)
model and that applied to the model in real-world operation. Medical ML systems can be …
model and that applied to the model in real-world operation. Medical ML systems can be …
There is no such thing as a validated prediction model
Background Clinical prediction models should be validated before implementation in clinical
practice. But is favorable performance at internal validation or one external validation …
practice. But is favorable performance at internal validation or one external validation …
Continual updating and monitoring of clinical prediction models: time for dynamic prediction systems?
Clinical prediction models (CPMs) have become fundamental for risk stratification across
healthcare. The CPM pipeline (development, validation, deployment, and impact …
healthcare. The CPM pipeline (development, validation, deployment, and impact …
Impact of a deep learning sepsis prediction model on quality of care and survival
Sepsis remains a major cause of mortality and morbidity worldwide. Algorithms that assist
with the early recognition of sepsis may improve outcomes, but relatively few studies have …
with the early recognition of sepsis may improve outcomes, but relatively few studies have …
Targeted validation: validating clinical prediction models in their intended population and setting
Clinical prediction models must be appropriately validated before they can be used. While
validation studies are sometimes carefully designed to match an intended population/setting …
validation studies are sometimes carefully designed to match an intended population/setting …
Assessing the effects of data drift on the performance of machine learning models used in clinical sepsis prediction
Background Data drift can negatively impact the performance of machine learning
algorithms (MLAs) that were trained on historical data. As such, MLAs should be …
algorithms (MLAs) that were trained on historical data. As such, MLAs should be …
Dynamic models to predict health outcomes: current status and methodological challenges
Background Disease populations, clinical practice, and healthcare systems are constantly
evolving. This can result in clinical prediction models quickly becoming outdated and less …
evolving. This can result in clinical prediction models quickly becoming outdated and less …
Evaluation of domain generalization and adaptation on improving model robustness to temporal dataset shift in clinical medicine
Temporal dataset shift associated with changes in healthcare over time is a barrier to
deploying machine learning-based clinical decision support systems. Algorithms that learn …
deploying machine learning-based clinical decision support systems. Algorithms that learn …
Predicting readmission or death after discharge from the ICU: external validation and retraining of a machine learning model
OBJECTIVES: Many machine learning (ML) models have been developed for application in
the ICU, but few models have been subjected to external validation. The performance of …
the ICU, but few models have been subjected to external validation. The performance of …