  Text classification tasks which aim at harvesting and/or organizing
information from electronic health records are pivotal to support clinical and
translational research. However these present specific challenges compared to
other classification tasks, notably due to the particular nature of the medical
lexicon and language used in clinical records. Recent advances in embedding
methods have shown promising results for several clinical tasks, yet there is
no exhaustive comparison of such approaches with other commonly used word
representations and classification models. In this work, we analyse the impact
of various word representations, text pre-processing and classification
algorithms on the performance of four different text classification tasks. The
results show that traditional approaches, when tailored to the specific
language and structure of the text inherent to the classification task, can
achieve or exceed the performance of more recent ones based on contextual
embeddings such as BERT.
