ICD(International Classification of Diseases) coding involves assigning ICD
codes to patients visit based on their medical notes. ICD coding is a
challenging multilabel text classification problem due to noisy medical
document inputs. Recent advancements in automated ICD coding have enhanced
performance by integrating additional data and knowledge bases with the
encoding of medical notes and codes. However, most of them ignore the code
hierarchy, leading to improper code assignments. To address these problems, we
propose a novel framework based on associated and hierarchical code description
distillation (AHDD) for better code representation learning and avoidance of
improper code assignment.we utilize the code description and the hierarchical
structure inherent to the ICD codes. Therefore, in this paper, we leverage the
code description and the hierarchical structure inherent to the ICD codes. The
code description is also applied to aware the attention layer and output layer.
Experimental results on the benchmark dataset show the superiority of the
proposed framework over several state-of-the-art baselines.

基于关联和层次化代码描述提炼（AHDD）的新框架用于改善代码表示学习和避免错误的代码分配。实验证明，该框架在基准数据集上优于几种最先进的基线方法。

基于相关和层次码描述筛选的一种新型 ICD 编码框架

A Novel ICD Coding Framework Based on Associated and Hierarchical Code  Description Distillation

Clinical notes in healthcare facilities are tagged with the International
Classification of Diseases (ICD) code; a list of classification codes for
medical diagnoses and procedures. ICD coding is a challenging multilabel text
classification problem due to noisy clinical document inputs and long-tailed
label distribution. Recent automated ICD coding efforts improve performance by
encoding medical notes and codes with additional data and knowledge bases.
However, most of them do not reflect how human coders generate the code: first,
the coders select general code categories and then look for specific
subcategories that are relevant to a patient's condition. Inspired by this, we
propose a two-stage decoding mechanism to predict ICD codes. Our model uses the
hierarchical properties of the codes to split the prediction into two steps: At
first, we predict the parent code and then predict the child code based on the
previous prediction. Experiments on the public MIMIC-III data set show that our
model performs well in single-model settings without external data or
knowledge.

本文提出了一个两阶段解码机制来预测国际疾病分类（ICD）码，该模型利用了代码的分层属性，在公共 MIMIC-III 数据集上表现良好。

一种用于高效 ICD 编码的两阶段解码器

A Two-Stage Decoder for Efficient ICD Coding

Although BERT is widely used by the NLP community, little is known about its
inner workings. Several attempts have been made to shed light on certain
aspects of BERT, often with contradicting conclusions. A much raised concern
focuses on BERT's over-parameterization and under-utilization issues. To this
end, we propose o novel approach to fine-tune BERT in a structured manner.
Specifically, we focus on Large Scale Multilabel Text Classification (LMTC)
where documents are assigned with one or more labels from a large predefined
set of hierarchically organized labels. Our approach guides specific BERT
layers to predict labels from specific hierarchy levels. Experimenting with two
LMTC datasets we show that this structured fine-tuning approach not only yields
better classification results but also leads to better parameter utilization.

该研究针对 BERT 内部机制进行研究，提出一种结构性的微调方法，专注于大规模多标签文本分类中，使用预定的层次标签集合，通过引导 BERT 特定层级来预测标签，以提高分类效果和参数利用率。