Mining Dynamic Problem Lists from Clinical Notes for the Interpretable Prediction of Adverse Outcomes

Justin Lovelace, Nathan Hurley, Adrian Haimovich, Bobak Mortazavi

Abstract: 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 to first extract problem lists from clinical notes and subsequently use the extracted 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 it can be used to develop dynamic problem lists that present clinical problems along with their quantitative importance. This allows clinicians to both easily identify the relevant problems and gain insight into the factors driving the model’s prediction.