š» The Catch? Complexity
Dynamic updates need:
Continuous data
More computing power
Digital infrastructure in place
Also, no method kept the original performance in later years. So, updating helpsābut doesnāt fix everything.
šÆ Why It Matters
One form of driftācalibration driftāmeans predicted risks no longer match actual outcomes.
š High-risk patients might be missed.
š Low-risk patients might be overtreated.
This can harm patients and erode trust in predictive tools.
In our commentary, we have expanded on the findings of the study, the impacts of temporal drift and the importance of addressing it.
āļø Fairness Under Threat
Temporal drift can impact some groups more than others.
Even worse, updating models doesnāt guarantee fairness. It can reduceāor worsenāperformance in minority groups.
We need to monitor algorithmic fairness, not just accuracy.
šØ What About After Implementation?
Once a model is used in practice, it changes patient outcomes (e.g., high-risk patients get treated and avoid the outcome).
This shifts the relationship between risk factors and outcomes. Updating without accounting for this is risky.
š°ļø What is Temporal Drift?
Data that clinical prediction models face when implemented can differ from the data they were trained on, even in the same setting. This can be due to changing patients, treatments or practices
The result? The modelās predictions start to go wrong.
š§µ In Summary
Prediction models are not āset and forget.ā They must be constantly checked, updated, and re-evaluatedābefore and after clinical use.
With thoughtful updating, we can keep models useful and safe over time.
š Read our commentary: kwnsfk27.r.eu-west-1.awstrack.me/L0/https:%2F...
A recent study looked at which methods could be effective at addressing this drift doi.org/10.1016/j.bp...
They found that without updating, model performance deteriorated and dynamic updating might be able to address this.
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