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.

本研究旨在解决将多标签分类任务转变为自回归生成任务的长尾挑战，通过引入新颖的生成自由文本的预训练目标，生成低维度的文本描述，从而推断出 ICD 代码，实验结果表明我们所提出的模型实现了新的 SOTA 表现，并设计了一种新的交叉关注再排序器来集成先前的 SOTA 和我们最佳的少样本编码预测。

基于 Prompt 的多标记少样本 ICD 编码自回归生成

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

We address the problem of learning to benchmark the best achievable
classifier performance. In this problem the objective is to establish
statistically consistent estimates of the Bayes misclassification error rate
without having to learn a Bayes-optimal classifier. Our learning to benchmark
framework improves on previous work on learning bounds on Bayes
misclassification rate since it learns the {\it exact} Bayes error rate instead
of a bound on error rate. We propose a benchmark learner based on an ensemble
of $\epsilon$-ball estimators and Chebyshev approximation. Under a smoothness
assumption on the class densities we show that our estimator achieves an
optimal (parametric) mean squared error (MSE) rate of $O(N^{-1})$, where $N$ is
the number of samples. Experiments on both simulated and real datasets
establish that our proposed benchmark learning algorithm produces estimates of
the Bayes error that are more accurate than previous approaches for learning
bounds on Bayes error probability.

本文提出了一个学习如何对最佳分类器的表现进行基准测试的框架，实现了对贝叶斯误分类误差率的准确评估，基于一个包含估计器的集成学习器和切比雪夫逼近进行基准学习，通过实验验证，相较之前的方法精确的评估了贝叶斯误差率。