  Problem lists are intended to provide clinicians with a relevant summary of
patient medical issues and are embedded in many electronic health record
systems. Despite their importance, problem lists are often cluttered with
resolved or currently irrelevant conditions. In this work, we develop a novel
end-to-end framework that first extracts diagnosis and procedure information
from clinical notes and subsequently uses the extracted medical problems to
predict patient outcomes. This framework is both more performant and more
interpretable than existing models used within the domain, achieving an AU-ROC
of 0.710 for bounceback readmission and 0.869 for in-hospital mortality
occurring after ICU discharge. We identify risk factors for both readmission
and mortality outcomes and demonstrate that our framework can be used to
develop dynamic problem lists that present clinical problems along with their
quantitative importance. We conduct a qualitative user study with medical
experts and demonstrate that they view the lists produced by our framework
favorably and find them to be a more effective clinical decision support tool
than a strong baseline.
