Deep learning approaches exhibit promising performances on various text
tasks. However, they are still struggling on medical text classification since
samples are often extremely imbalanced and scarce. Different from existing
mainstream approaches that focus on supplementary semantics with external
medical information, this paper aims to rethink the data challenges in medical
texts and present a novel framework-agnostic algorithm called Text2Tree that
only utilizes internal label hierarchy in training deep learning models. We
embed the ICD code tree structure of labels into cascade attention modules for
learning hierarchy-aware label representations. Two new learning schemes,
Similarity Surrogate Learning (SSL) and Dissimilarity Mixup Learning (DML), are
devised to boost text classification by reusing and distinguishing samples of
other labels following the label representation hierarchy, respectively.
Experiments on authoritative public datasets and real-world medical records
show that our approach stably achieves superior performances over classical and
advanced imbalanced classification methods.

本文提出了一种名为 Text2Tree 的新型算法，该算法只利用内部标签层次结构来训练深度学习模型，通过嵌入 ICD 标签树结构到级联注意力模块中，学习具有层次感知的标签表示。引入了相似度替代学习（SSL）和差异混合学习（DML）两种新的学习方案，通过重用和区分其他标签的样本来提高文本分类性能。实验证明，我们的方法在公共数据集和真实医疗记录上表现出优于传统和先进的不平衡分类方法的稳定性能。

文本对齐到标签树层次结构的医疗分类不平衡问题

Text2Tree: Aligning Text Representation to the Label Tree Hierarchy for  Imbalanced Medical Classification

Zero-shot classification has enabled the classification of text into classes
that were not seen during training. In this paper, we investigate the
effectiveness of pre-trained language models to accurately classify responses
from Doctors and AI in health consultations through zero-shot learning. Our
study aims to determine whether these models can effectively detect if a text
originates from human or AI models without specific corpus training. For our
experiments, we collected responses from doctors to patient inquiries about
their health and posed the same question/response to AI models. Our findings
revealed that while pre-trained language models demonstrate a strong
understanding of language generally, they may require specific corpus training
or other techniques to achieve accurate classification of doctor- and
AI-generated text in healthcare consultations. As a baseline approach, this
study shows the limitations of relying solely on zero-shot classification in
medical classification tasks. This research lays the groundwork for further
research into the field of medical text classification, informing the
development of more effective approaches to accurately classify doctor- and
AI-generated text in health consultations.

通过零样本学习，本研究探讨了预训练语言模型在准确分类医生与人工智能在健康咨询中的回答方面的有效性。实验结果表明，虽然预训练语言模型在语言理解方面表现出很强的能力，但其在医疗咨询中准确分类医生和人工智能生成的文本可能需要特定的语料库训练或其他技术。这项研究作为基准方法，显示了仅依赖零样本分类在医疗分类任务中的局限性，为医学文本分类领域的进一步研究奠定了基础，并为开发更有效的方法以准确分类医生和人工智能生成的健康咨询文本提供了信息。

医学人工智能对话语料库 (MEDIC): 医生和人工智能在健康咨询中的零封闭分类

MedAI Dialog Corpus (MEDIC): Zero-Shot Classification of Doctor and AI  Responses in Health Consultations

Participant recruitment based on unstructured medical texts such as clinical
notes and radiology reports has been a challenging yet important task for the
cohort establishment in clinical research. Recently, Large Language Models
(LLMs) such as ChatGPT have achieved tremendous success in various downstream
tasks thanks to their promising performance in language understanding,
inference, and generation. It is then natural to test their feasibility in
solving the cohort recruitment task, which involves the classification of a
given paragraph of medical text into disease label(s). However, when applied to
knowledge-intensive problem settings such as medical text classification, where
the LLMs are expected to understand the decision made by human experts and
accurately identify the implied disease labels, the LLMs show a mediocre
performance. A possible explanation is that, by only using the medical text,
the LLMs neglect to use the rich context of additional information that
languages afford. To this end, we propose to use a knowledge graph as auxiliary
information to guide the LLMs in making predictions. Moreover, to further boost
the LLMs adapt to the problem setting, we apply a chain-of-thought (CoT) sample
selection strategy enhanced by reinforcement learning, which selects a set of
CoT samples given each individual medical report. Experimental results and
various ablation studies show that our few-shot learning method achieves
satisfactory performance compared with fine-tuning strategies and gains superb
advantages when the available data is limited. The code and sample dataset of
the proposed CohortGPT model is available at:
this https URL

通过使用知识图谱作为辅助信息来指导大型语言模型在疾病标签分类的任务中进行预测，在少量数据情况下，我们提出的少样本学习方法相比微调策略表现出令人满意的性能和超强的优势。