Really awesome #deeplearning #rnn paper 👏🏻 explaining an increase in predicted risk for clinical alerts.

Explanations in a temporal setting where a stateful dynamical model produces a sequence of risk estimates given an input at each time step.

The goal here is to alert a clinician when a patient’s risk of deterioration rises. The clinician then has to decide whether to intervene and adjust the treatment.

Given a potentially long sequence of new events since she last saw the patient, a concise explanation helps her to quickly triage the alert.

Authors developed methods to lift static attribution techniques to the dynamical setting, where they identified and addressed challenges specific to dynamics.

They then experimentally assess the utility of different explanations of clinical alerts through expert evaluation.

Here’s full paper: https://lnkd.in/duMQYyW
#healthcare #diagnostics #clinical
#prediction
✴️ @AI_Python_EN