Clinical Decision Transformer
: Intended Treatment Recommendation through Goal Prompting


Code: soon be available


Summary

cdt_greedy_inference_overview


goal_prompting



Contextual Embedding

contextual_embedding



Attention Pattern

attn_pattern



Abstract

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 a GPT architecture to model sequential medications required to reach that goal state. 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.


Keywords: Clinical Recommender System, Clinical Decision Support Systems, Human-AI Interface, Electronic Health Records, Causal Inference



References

[1] Bica, I., Alaa, A. M., Jordon, J., and van der Schaar, M. Estimating counterfactual treatment outcomes over time through adversarially balanced representations. arXiv preprint arXiv:2002.04083, 2020
[2] American Diabetes Association. Understanding A1c.

* The data extraction process from the EHR system was approved by an official review committee.