Hi,
Many outcomes of interest in prediction might occur in the context of an inpatient encounter. Within the current ATLAS framework, is there a way to only use features that occur up to (but not including) the visit where the outcome of interest occurs? If not, I think this would be a useful configuration to add to the framework.
The issue is that (probably due to erroneous / insufficiently detailed timestamps), other procedures, diagnoses, etc. that co-occur in the same inpatient encounter with the outcome of interest can become predictors in the model, invariably some of these are directly connected to the occurrence of the outcome and distort the model.
For example, in the PLP tutorial in Oct, we looked at trying to predict ICU admission for pneumonia, and found that procedures (e.g. mechanical intubation) that describe the process of bringing someone to the ICU rise to the top as highly predictive features (this validates the great work that has been done in building the model framework - but obviously poses a problem).
That use case is difficult because you’d need features from that inpatient presentation to predict a patient’s deterioration - I don’t see a way around this until data timestamping improves.
In my current use case, I’m trying to create prediction models for a series of surgical outcomes. I’d like to use all features up to, but NOT including the inpatient encounter where the surgery occurs, as predictors to skirt this timestamp issue. I’ve tried various ways of implementing the target cohort (by trying to generate a cohort of preoperative patients for example), but ultimately when you configure the model and declare the outcome cohort of interest, I can’t think of a way of stopping features that co-occur in the same encounter as that outcome from being in the model (within the current ATLAS config options).
Any thoughts?