Automatic Coding at Scale: Design and Deployment of a Nationwide System
  for Normalizing Referrals in the Chilean Public Healthcare System
Fabián Villena Matías Rojas Felipe Arias Jorge Pacheco Paulina Vera Jocelyn Dunstan
The disease coding task involves assigning a unique identifier from a
controlled vocabulary to each disease mentioned in a clinical document. This
task is relevant since it allows information extraction from unstructured data
to perform, for example, epidemiological studies about the incidence and
prevalence of diseases in a determined context. However, the manual coding
process is subject to errors as it requires medical personnel to be competent
in coding rules and terminology. In addition, this process consumes a lot of
time and energy, which could be allocated to more clinically relevant tasks.
These difficulties can be addressed by developing computational systems that
automatically assign codes to diseases. In this way, we propose a two-step
system for automatically coding diseases in referrals from the Chilean public
healthcare system. Specifically, our model uses a state-of-the-art NER model
for recognizing disease mentions and a search engine system based on
Elasticsearch for assigning the most relevant codes associated with these
disease mentions. The system's performance was evaluated on referrals manually
coded by clinical experts. Our system obtained a MAP score of 0.63 for the
subcategory level and 0.83 for the category level, close to the best-performing
models in the literature. This system could be a support tool for health
professionals, optimizing the coding and management process. Finally, to
guarantee reproducibility, we publicly release the code of our models and
experiments.