Training and Validating a Treatment Recommender with Partial Verification Evidence
CoRR(2024)
摘要
Current clinical decision support systems (DSS) are trained and validated on
observational data from the target clinic. This is problematic for treatments
validated in a randomized clinical trial (RCT), but not yet introduced in any
clinic. In this work, we report on a method for training and validating the DSS
using the RCT data. The key challenges we address are of missingness – missing
rationale for treatment assignment (the assignment is at random), and missing
verification evidence, since the effectiveness of a treatment for a patient can
only be verified (ground truth) for treatments what were actually assigned to a
patient. We use data from a multi-armed RCT that investigated the effectiveness
of single- and combination- treatments for 240+ tinnitus patients recruited and
treated in 5 clinical centers.
To deal with the 'missing rationale' challenge, we re-model the target
variable (outcome) in order to suppress the effect of the randomly-assigned
treatment, and control on the effect of treatment in general. Our methods are
also robust to missing values in features and with a small number of patients
per RCT arm. We deal with 'missing verification evidence' by using
counterfactual treatment verification, which compares the effectiveness of the
DSS recommendations to the effectiveness of the RCT assignments when they are
aligned v/s not aligned.
We demonstrate that our approach leverages the RCT data for learning and
verification, by showing that the DSS suggests treatments that improve the
outcome. The results are limited through the small number of patients per
treatment; while our ensemble is designed to mitigate this effect, the
predictive performance of the methods is affected by the smallness of the data.
We provide a basis for the establishment of decision supporting routines on
treatments that have been tested in RCTs but have not yet been deployed
clinically.
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