# A Meta-embedding-based Ensemble Approach for ICD Coding Prediction

arXiv: https://arxiv.org/abs/2102.13622 · HTML: https://mlsanity.com/paper/2102.13622/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 26 references that are in the map.
- First posted on arXiv 26 Feb 2021.

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

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [A Structured Self-attentive Sentence Embedding](https://mlsanity.com/paper/1703.03130/) (2017)
- [Explainable Prediction of Medical Codes from Clinical Text](https://mlsanity.com/paper/1802.05695/) (2018)
- [All-but-the-Top: Simple and Effective Postprocessing for Word Representations](https://mlsanity.com/paper/1702.01417/) (2017)
- [ICD Coding from Clinical Text Using Multi-Filter Residual Convolutional Neural Network](https://mlsanity.com/paper/1912.00862/) (2019)
- [An Empirical Study on Large-Scale Multi-Label Text Classification Including Few and Zero-Shot Labels](https://mlsanity.com/paper/2010.01653/) (2020)
- [Multimodal Machine Learning for Automated ICD Coding](https://mlsanity.com/paper/1810.13348/) (2018)
- [Inferring Degrees from Incomplete Networks and Nonlinear Dynamics](https://mlsanity.com/paper/2004.10546/) (2020)
- [Frustratingly Easy Meta-Embedding -- Computing Meta-Embeddings by Averaging Source Word Embeddings](https://mlsanity.com/paper/1804.05262/) (2018)
- [Dynamic Meta-Embeddings for Improved Sentence Representations](https://mlsanity.com/paper/1804.07983/) (2018)
- [Unsupervised Post-processing of Word Vectors via Conceptor Negation](https://mlsanity.com/paper/1811.11001/) (2018)
- [Think Globally, Embed Locally --- Locally Linear Meta-embedding of Words](https://mlsanity.com/paper/1709.06671/) (2017)
- [Learning Geometric Word Meta-Embeddings](https://mlsanity.com/paper/2004.09219/) (2020)
- [Med2Meta: Learning Representations of Medical Concepts with Meta-Embeddings](https://mlsanity.com/paper/1912.03366/) (2019)

## Built on by (most cited first, 2 of 2)

- [Hierarchical Label-wise Attention Transformer Model for Explainable ICD Coding](https://mlsanity.com/paper/2204.10716/) (2022)
- [TreeMAN: Tree-enhanced Multimodal Attention Network for ICD Coding](https://mlsanity.com/paper/2305.18576/) (2023)

## Abstract (arXiv)

International Classification of Diseases (ICD) are the de facto codes used globally for clinical coding. These codes enable healthcare providers to claim reimbursement and facilitate efficient storage and retrieval of diagnostic information. The problem of automatically assigning ICD codes has been approached in literature as a multilabel classification, using neural models on unstructured data. Our proposed approach enhances the performance of neural models by effectively training word vectors using routine medical data as well as external knowledge from scientific articles. Furthermore, we exploit the geometric properties of the two sets of word vectors and combine them into a common dimensional space, using meta-embedding techniques. We demonstrate the efficacy of this approach for a multimodal setting, using unstructured and structured information. We empirically show that our approach improves the current state-of-the-art deep learning architectures and benefits ensemble models.