  Background and Objective: Code assignment is of paramount importance in many
levels in modern hospitals, from ensuring accurate billing process to creating
a valid record of patient care history. However, the coding process is tedious
and subjective, and it requires medical coders with extensive training. This
study aims to evaluate the performance of deep-learning-based systems to
automatically map clinical notes to ICD-9 medical codes. Methods: The
evaluations of this research are focused on end-to-end learning methods without
manually defined rules. Traditional machine learning algorithms, as well as
state-of-the-art deep learning methods such as Recurrent Neural Networks and
Convolution Neural Networks, were applied to the Medical Information Mart for
Intensive Care (MIMIC-III) dataset. An extensive number of experiments was
applied to different settings of the tested algorithm. Results: Findings showed
that the deep learning-based methods outperformed other conventional machine
learning methods. From our assessment, the best models could predict the top 10
ICD-9 codes with 0.6957 F1 and 0.8967 accuracy and could estimate the top 10
ICD-9 categories with 0.7233 F1 and 0.8588 accuracy. Our implementation also
outperformed existing work under certain evaluation metrics. Conclusion: A set
of standard metrics was utilized in assessing the performance of ICD-9 code
assignment on MIMIC-III dataset. All the developed evaluation tools and
resources are available online, which can be used as a baseline for further
research.
