Multi-label Few-shot ICD Coding as Autoregressive Generation with Prompt

• URL: http://arxiv.org/abs/2211.13813v1
• Date: Thu, 24 Nov 2022 22:10:50 GMT
• Title: Multi-label Few-shot ICD Coding as Autoregressive Generation with Prompt
• Authors: Zhichao Yang, Sunjae Kwon, Zonghai Yao, Hong Yu
• Abstract summary: This study transforms this multi-label classification task into an autoregressive generation task. Instead of directly predicting the high dimensional space of ICD codes, our model generates the lower dimension of text descriptions. Experiments on MIMIC-III-few show that our model performs with a marco F1 30.2, which substantially outperforms the previous MIMIC-III-full SOTA model.
• Score: 7.554528566861559
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Automatic International Classification of Diseases (ICD) coding aims to assign multiple ICD codes to a medical note with an average of 3,000+ tokens. This task is challenging due to the high-dimensional space of multi-label assignment (155,000+ ICD code candidates) and the long-tail challenge - Many ICD codes are infrequently assigned yet infrequent ICD codes are important clinically. This study addresses the long-tail challenge by transforming this multi-label classification task into an autoregressive generation task. Specifically, we first introduce a novel pretraining objective to generate free text diagnoses and procedure using the SOAP structure, the medical logic physicians use for note documentation. Second, instead of directly predicting the high dimensional space of ICD codes, our model generates the lower dimension of text descriptions, which then infer ICD codes. Third, we designed a novel prompt template for multi-label classification. We evaluate our Generation with Prompt model with the benchmark of all code assignment (MIMIC-III-full) and few shot ICD code assignment evaluation benchmark (MIMIC-III-few). Experiments on MIMIC-III-few show that our model performs with a marco F1 30.2, which substantially outperforms the previous MIMIC-III-full SOTA model (marco F1 4.3) and the model specifically designed for few/zero shot setting (marco F1 18.7). Finally, we design a novel ensemble learner, a cross attention reranker with prompts, to integrate previous SOTA and our best few-shot coding predictions. Experiments on MIMIC-III-full show that our ensemble learner substantially improves both macro and micro F1, from 10.4 to 14.6 and from 58.2 to 59.1, respectively.

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