The International Classification of Diseases (ICD) serves as a definitive
medical classification system encompassing a wide range of diseases and
conditions. The primary objective of ICD indexing is to allocate a subset of
ICD codes to a medical record, which facilitates standardized documentation and
management of various health conditions. Most existing approaches have suffered
from selecting the proper label subsets from an extremely large ICD collection
with a heavy long-tailed label distribution. In this paper, we leverage a
multi-stage ``retrieve and re-rank'' framework as a novel solution to ICD
indexing, via a hybrid discrete retrieval method, and re-rank retrieved
candidates with contrastive learning that allows the model to make more
accurate predictions from a simplified label space. The retrieval model is a
hybrid of auxiliary knowledge of the electronic health records (EHR) and a
discrete retrieval method (BM25), which efficiently collects high-quality
candidates. In the last stage, we propose a label co-occurrence guided
contrastive re-ranking model, which re-ranks the candidate labels by pulling
together the clinical notes with positive ICD codes. Experimental results show
the proposed method achieves state-of-the-art performance on a number of
measures on the MIMIC-III benchmark.

本文介绍了一种用于 ICD 编码的多阶段 “检索和重排序” 框架，通过混合离散检索方法和对比学习，在一个简化的标签空间中使模型能够更准确地预测，实验结果显示该方法在 MIMIC-III 基准测试上达到了最先进的性能水平。

自动医学编码推荐的多阶段检索和再排序模型

Multi-stage Retrieve and Re-rank Model for Automatic Medical Coding  Recommendation

Automatic coding of International Classification of Diseases (ICD) is a
multi-label text categorization task that involves extracting disease or
procedure codes from clinical notes. Despite the application of
state-of-the-art natural language processing (NLP) techniques, there are still
challenges including limited availability of data due to privacy constraints
and the high variability of clinical notes caused by different writing habits
of medical professionals and various pathological features of patients. In this
work, we investigate the semi-structured nature of clinical notes and propose
an automatic algorithm to segment them into sections. To address the
variability issues in existing ICD coding models with limited data, we
introduce a contrastive pre-training approach on sections using a soft
multi-label similarity metric based on tree edit distance. Additionally, we
design a masked section training strategy to enable ICD coding models to locate
sections related to ICD codes. Extensive experimental results demonstrate that
our proposed training strategies effectively enhance the performance of
existing ICD coding methods.

通过研究调查临床记录的半结构化性质，我们提出了一种自动算法将其分割为不同的部分。为了解决现有 ICD 编码模型在数据有限性方面的可变性问题，我们引入了对于部分使用基于树编辑距离的软多标签相似度度量的对比预训练方法。此外，我们设计了一个掩码部分训练策略，使 ICD 编码模型能够定位与 ICD 编码相关的部分。广泛的实验结果表明，我们提出的训练策略有效地提升了现有 ICD 编码方法的性能。

基于树型对比学习的半结构化自动 ICD 编码研究

Towards Semi-Structured Automatic ICD Coding via Tree-based Contrastive  Learning

Healthcare providers usually record detailed notes of the clinical care
delivered to each patient for clinical, research, and billing purposes. Due to
the unstructured nature of these narratives, providers employ dedicated staff
to assign diagnostic codes to patients' diagnoses using the International
Classification of Diseases (ICD) coding system. This manual process is not only
time-consuming but also costly and error-prone. Prior work demonstrated
potential utility of Machine Learning (ML) methodology in automating this
process, but it has relied on large quantities of manually labeled data to
train the models. Additionally, diagnostic coding systems evolve with time,
which makes traditional supervised learning strategies unable to generalize
beyond local applications. In this work, we introduce a general
weakly-supervised text classification framework that learns from class-label
descriptions only, without the need to use any human-labeled documents. It
leverages the linguistic domain knowledge stored within pre-trained language
models and the data programming framework to assign code labels to individual
texts. We demonstrate the efficacy and flexibility of our method by comparing
it to state-of-the-art weak text classifiers across four real-world text
classification datasets, in addition to assigning ICD codes to medical notes in
the publicly available MIMIC-III database.

该研究介绍了一种利用机器学习和文本分类方法来自动识别医疗记录中的疾病编码 (ICD) 的框架，而不需要使用任何人工标记文档。

通过自动弱监督分类非结构化临床笔记

Classifying Unstructured Clinical Notes via Automatic Weak Supervision

International Classification of Diseases(ICD) is an authoritative health care
classification system of different diseases and conditions for clinical and
management purposes. Considering the complicated and dedicated process to
assign correct codes to each patient admission based on overall diagnosis, we
propose a hierarchical deep learning model with attention mechanism which can
automatically assign ICD diagnostic codes given written diagnosis. We utilize
character-aware neural language models to generate hidden representations of
written diagnosis descriptions and ICD codes, and design an attention mechanism
to address the mismatch between the numbers of descriptions and corresponding
codes. Our experimental results show the strong potential of automated ICD
coding from diagnosis descriptions. Our best model achieves 0.53 and 0.90 of F1
score and area under curve of receiver operating characteristic respectively.
The result outperforms those achieved using character-unaware encoding method
or without attention mechanism. It indicates that our proposed deep learning
model can code automatically in a reasonable way and provide a framework for
computer-auxiliary ICD coding.

本文提出了一种具有注意力机制的分层深度学习模型，该模型可以根据诊断描述自动分配 ICD 诊断代码。实验结果表明，我们的深度学习模型能够在合理的范围内自动编码，并为计算机辅助 ICD 编码提供了框架。