Multitask Recalibrated Aggregation Network for Medical Code Prediction
This work addresses the time-consuming and error-prone manual coding process in medical information systems, offering an incremental improvement for healthcare and insurance domains.
The paper tackled the problem of automated medical coding from lengthy clinical documents by proposing a multitask recalibrated aggregation network, which improved predictive performance on the MIMIC-III dataset.
Medical coding translates professionally written medical reports into standardized codes, which is an essential part of medical information systems and health insurance reimbursement. Manual coding by trained human coders is time-consuming and error-prone. Thus, automated coding algorithms have been developed, building especially on the recent advances in machine learning and deep neural networks. To solve the challenges of encoding lengthy and noisy clinical documents and capturing code associations, we propose a multitask recalibrated aggregation network. In particular, multitask learning shares information across different coding schemes and captures the dependencies between different medical codes. Feature recalibration and aggregation in shared modules enhance representation learning for lengthy notes. Experiments with a real-world MIMIC-III dataset show significantly improved predictive performance.