Retrieve and rerank for automated ICD coding via Contrastive Learning

Automated ICD coding is a multi-label prediction task aiming at assigning patient diagnoses with the most relevant subsets of disease codes. In the deep learning regime, recent works have suffered from large label set and heavy imbalance distribution. To mitigate the negative effect in such scenarios, we propose a retrieve and rerank framework that introduces the Contrastive Learning (CL) for label retrieval, allowing the model to make more accurate prediction from a simplified label space. Given the appealing discriminative power of CL, we adopt it as the training strategy to replace the standard cross-entropy objective and retrieve a small subset by taking the distance between clinical notes and ICD codes into account. After properly training, the retriever could implicitly capture the code co-occurrence, which makes up for the deficiency of cross-entropy assigning each label independently of the others. Further, we evolve a powerful model via a Transformer variant for refining and reranking the candidate set, which can extract semantically meaningful features from long clinical sequences. Applying our method on well-known models, experiments show that our framework provides more accurate results guaranteed by preselecting a small subset of candidates before fine-level reranking. Relying on the framework, our proposed model achieves 0.590 and 0.990 in terms of Micro-F1 and Micro-AUC on benchmark MIMIC-III.

Automated summary

Automated ICD coding is a multi-label prediction task in the deep learning regime. To mitigate the negative effects of large label sets and heavy imbalance distribution, a retrieve and rerank framework is proposed, which introduces Contrastive Learning (CL) for label retrieval and a Transformer variant for refining and reranking the candidate set. Experiments show that this framework provides more accurate results by preselecting a small subset of candidates before fine-level reranking.