Abstract
AbstractMedical 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.
Highlights
Clinical notes generated by clinicians contain rich information about patients’ diagnoses and treatment procedures
We propose a Recalibrated Aggregation Module (RAM) that abstracts features learned by the bidirectional gated recurrent unit (BiGRU), recalibrates the abstraction, aggregates the abstraction and the recalibrated features, and eventually combines the new representation with the original one
We examine the general usefulness of the two main components - multitask training (MTL) and RAM module, by conducting an ablation study, where we consider the performance of three representative International Classification of Diseases (ICD) coding models: CAML, MultiResCNN, and the GRU-based model, with and without the specific components
Summary
Clinical notes generated by clinicians contain rich information about patients’ diagnoses and treatment procedures. Healthcare institutions digitized these clinical texts into Electronic Health Records (EHRs), together with other structural medical and treatment histories of patients, for clinical data management, health condition tracking and automation. Clinical notes are usually annotated with standardized statistical codes. Different diagnosis classification systems utilize various medical coding systems. One of the most widely used coding systems is the International Classification of Diseases (ICD) maintained by the World Health Organization. The ICD system is used to transform diseases, symptoms, signs, and treatment procedures into standard medical codes and has been widely used for clinical data analysis, automated medical decision support [8], and medical insurance reimbursement [24]
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