  Human coders assign standardized medical codes to clinical documents
generated during patients' hospitalization, which is error-prone and
labor-intensive. Automated medical coding approaches have been developed using
machine learning methods such as deep neural networks. Nevertheless, automated
medical coding is still challenging because of the imbalanced class problem,
complex code association, and noise in lengthy documents. To solve these
issues, we propose a novel neural network called Multitask Balanced and
Recalibrated Neural Network. Significantly, the multitask learning scheme
shares the relationship knowledge between different code branches to capture
the code association. A recalibrated aggregation module is developed by
cascading convolutional blocks to extract high-level semantic features that
mitigate the impact of noise in documents. Also, the cascaded structure of the
recalibrated module can benefit the learning from lengthy notes. To solve the
class imbalanced problem, we deploy the focal loss to redistribute the
attention of low and high-frequency medical codes. Experimental results show
that our proposed model outperforms competitive baselines on a real-world
clinical dataset MIMIC-III.
