# Multitask Recalibrated Aggregation Network for Medical Code Prediction

arXiv: https://arxiv.org/abs/2104.00952 · HTML: https://mlsanity.com/paper/2104.00952/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 36 references that are in the map.
- First posted on arXiv 2 Apr 2021.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Multi-Task Deep Neural Networks for Natural Language Understanding](https://mlsanity.com/paper/1901.11504/) (2019)
- [Explainable Prediction of Medical Codes from Clinical Text](https://mlsanity.com/paper/1802.05695/) (2018)
- [Doctor AI: Predicting Clinical Events via Recurrent Neural Networks](https://mlsanity.com/paper/1511.05942/) (2015)
- [ICD Coding from Clinical Text Using Multi-Filter Residual Convolutional Neural Network](https://mlsanity.com/paper/1912.00862/) (2019)
- [Does the Magic of BERT Apply to Medical Code Assignment? A Quantitative Study](https://mlsanity.com/paper/2103.06511/) (2021)
- [Multi-Label Classification of Patient Notes a Case Study on ICD Code Assignment](https://mlsanity.com/paper/1709.09587/) (2017)
- [Dilated Convolutional Attention Network for Medical Code Assignment from Clinical Text](https://mlsanity.com/paper/2009.14578/) (2020)

## Built on by (most cited first, 5 of 5)

- [A Unified Review of Deep Learning for Automated Medical Coding](https://mlsanity.com/paper/2201.02797/) (2022)
- [Multitask Balanced and Recalibrated Network for Medical Code Prediction](https://mlsanity.com/paper/2109.02418/) (2021)
- [Aligning AI Research with the Needs of Clinical Coding Workflows: Eight Recommendations Based on US Data Analysis and Critical Review](https://mlsanity.com/paper/2412.18043/) (2024)
- [Patient Outcome and Zero-shot Diagnosis Prediction with Hypernetwork-guided Multitask Learning](https://mlsanity.com/paper/2109.03062/) (2021)
- [Structured Information Matters: Explainable ICD Coding with Patient-Level Knowledge Graphs](https://mlsanity.com/paper/2509.09699/) (2025)

## Abstract (arXiv)

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.