  Machine Learning (ML) is widely used to automatically extract meaningful
information from Electronic Health Records (EHR) to support operational,
clinical, and financial decision-making. However, ML models require a large
number of annotated examples to provide satisfactory results, which is not
possible in most healthcare scenarios due to the high cost of clinician-labeled
data. Active Learning (AL) is a process of selecting the most informative
instances to be labeled by an expert to further train a supervised algorithm.
We demonstrate the effectiveness of AL in multi-label text classification in
the clinical domain. In this context, we apply a set of well-known AL methods
to help automatically assign ICD-9 codes on the MIMIC-III dataset. Our results
show that the selection of informative instances provides satisfactory
classification with a significantly reduced training set (8.3\% of the total
instances). We conclude that AL methods can significantly reduce the manual
annotation cost while preserving model performance.
