  Machine learning systems show significant promise for forecasting patient
adverse events via risk scores. However, these risk scores implicitly encode
assumptions about future interventions that the patient is likely to receive,
based on the intervention policy present in the training data. Without this
important context, predictions from such systems are less interpretable for
clinicians. We propose a joint model of intervention policy and adverse event
risk as a means to explicitly communicate the model's assumptions about future
interventions. We develop such an intervention policy model on MIMIC-III, a
real world de-identified ICU dataset, and discuss some use cases that highlight
the utility of this approach. We show how combining typical risk scores, such
as the likelihood of mortality, with future intervention probability scores
leads to more interpretable clinical predictions.
