International Classification of Diseases (ICD) coding is the task of
assigning ICD diagnosis codes to clinical notes. This can be challenging given
the large quantity of labels (nearly 9,000) and lengthy texts (up to 8,000
tokens). However, unlike the single-pass reading process in previous works,
humans tend to read the text and label definitions again to get more confident
answers. Moreover, although pretrained language models have been used to
address these problems, they suffer from huge memory usage. To address the
above problems, we propose a simple but effective model called the Multi-Hop
Label-wise ATtention (MHLAT), in which multi-hop label-wise attention is
deployed to get more precise and informative representations. Extensive
experiments on three benchmark MIMIC datasets indicate that our method achieves
significantly better or competitive performance on all seven metrics, with much
fewer parameters to optimize.

提出了一种称为多跳标签注意力（MHLAT）的简单但有效的模型，通过多跳标签注意力机制获取更精确和丰富的表示，以解决 ICD 编码中的标签分类问题及预训练语言模型的巨大内存使用问题。在三个基准 MIMIC 数据集上进行的广泛实验表明，该方法在所有七个指标上达到了显著更好或可比性能，且参数较少。

MHLAT：用于自动 ICD 编码的多跳标签注意模型

MHLAT: Multi-hop Label-wise Attention Model for Automatic ICD Coding

Post-acute sequelae of SARS-CoV-2 infection (PASC) or Long COVID is an
emerging medical condition that has been observed in several patients with a
positive diagnosis for COVID-19. Historical Electronic Health Records (EHR)
like diagnosis codes, lab results and clinical notes have been analyzed using
deep learning and have been used to predict future clinical events. In this
paper, we propose an interpretable deep learning approach to analyze historical
diagnosis code data from the National COVID Cohort Collective (N3C) to find the
risk factors contributing to developing Long COVID. Using our deep learning
approach, we are able to predict if a patient is suffering from Long COVID from
a temporally ordered list of diagnosis codes up to 45 days post the first COVID
positive test or diagnosis for each patient, with an accuracy of 70.48\%. We
are then able to examine the trained model using Gradient-weighted Class
Activation Mapping (GradCAM) to give each input diagnoses a score. The highest
scored diagnosis were deemed to be the most important for making the correct
prediction for a patient. We also propose a way to summarize these top
diagnoses for each patient in our cohort and look at their temporal trends to
determine which codes contribute towards a positive Long COVID diagnosis.

使用诊断代码数据及深度学习，我们能够预测出是否出现了了长期 COVID 症状并从最高分诊断方案中判断出其中较重要的因素。