ArXiv Preprint
With recent achievements in tasks requiring context awareness, foundation
models have been adopted to treat large-scale data from electronic health
record (EHR) systems. However, previous clinical recommender systems based on
foundation models have a limited purpose of imitating clinicians' behavior and
do not directly consider a problem of missing values. In this paper, we propose
Clinical Decision Transformer (CDT), a recommender system that generates a
sequence of medications to reach a desired range of clinical states given as
goal prompts. For this, we conducted goal-conditioned sequencing, which
generated a subsequence of treatment history with prepended future goal state,
and trained the CDT to model sequential medications required to reach that goal
state. For contextual embedding over intra-admission and inter-admissions, we
adopted a GPT-based architecture with an admission-wise attention mask and
column embedding. In an experiment, we extracted a diabetes dataset from an EHR
system, which contained treatment histories of 4788 patients. We observed that
the CDT achieved the intended treatment effect according to goal prompt ranges
(e.g., NormalA1c, LowerA1c, and HigherA1c), contrary to the case with behavior
cloning. To the best of our knowledge, this is the first study to explore
clinical recommendations from the perspective of goal prompting. See
https://clinical-decision-transformer.github.io for code and additional
information.
Seunghyun Lee, Da Young Lee, Sujeong Im, Nan Hee Kim, Sung-Min Park
2023-02-01
