Learning medical triage from clinicians using Deep Q-Learning
  Medical Triage is of paramount importance to healthcare systems, allowing for
the correct orientation of patients and allocation of the necessary resources
to treat them adequately. While reliable decision-tree methods exist to triage
patients based on their presentation, those trees implicitly require human
inference and are not immediately applicable in a fully automated setting. On
the other hand, learning triage policies directly from experts may correct for
some of the limitations of hard-coded decision-trees. In this work, we present
a Deep Reinforcement Learning approach (a variant of DeepQ-Learning) to triage
patients using curated clinical vignettes. The dataset, consisting of 1374
clinical vignettes, was created by medical doctors to represent real-life
cases. Each vignette is associated with an average of 3.8 expert triage
decisions given by medical doctors relying solely on medical history. We show
that this approach is on a par with human performance, yielding safe triage
decisions in 94% of cases, and matching expert decisions in 85% of cases. The
trained agent learns when to stop asking questions, acquires optimized decision
policies requiring less evidence than supervised approaches, and adapts to the
novelty of a situation by asking for more information. Overall, we demonstrate
that a Deep Reinforcement Learning approach can learn effective medical triage
policies directly from expert decisions, without requiring expert knowledge
engineering. This approach is scalable and can be deployed in healthcare
settings or geographical regions with distinct triage specifications, or where
trained experts are scarce, to improve decision making in the early stage of
care.
