Using Enhanced Representations to Predict Medical Procedures from Clinician Notes

Nowadays, most health professionals use electronic health records to keep track of patients. To properly use and share these data, the community has relied on medical classification standards to represent patient information. However, the coding process is tedious and time-consuming, often limiting its application. This paper proposes a novel feature representation method that considers the distinction between diagnoses and procedure codes, and applies this to the task of medical procedure code prediction. Diagnosis codes are combined with text annotations, and the result is then used as input to a downstream procedure code prediction task. Various diagnosis code representations are considered by exploiting a code hierarchy. Furthermore, different text representation strategies are also used, including embeddings from language models. Finally, the method was evaluated using the MIMIC-III database. Our experiments showed improved performance in procedure code prediction when exploiting the diagnosis codes, outperforming state-of-the-art models.