  Machine learning-based multi-label medical text classifications can be used
to enhance the understanding of the human body and aid the need for patient
care. We present a broad study on clinical natural language processing
techniques to maximise a feature representing text when predicting medical
codes on patients with multi-morbidity. We present results of multi-label
medical text classification problems with 18, 50 and 155 labels. We compare
several variations to embeddings, text tagging, and pre-processing. For
imbalanced data we show that labels which occur infrequently, benefit the most
from additional features incorporated in embeddings. We also show that high
dimensional embeddings pre-trained using health-related data present a
significant improvement in a multi-label setting, similarly to the way they
improve performance for binary classification. High dimensional embeddings from
this research are made available for public use.
