Can language models read biomedical texts and explain the biomedical
mechanisms discussed? In this work we introduce a biomedical mechanism
summarization task. Biomedical studies often investigate the mechanisms behind
how one entity (e.g., a protein or a chemical) affects another in a biological
context. The abstracts of these publications often include a focused set of
sentences that present relevant supporting statements regarding such
relationships, associated experimental evidence, and a concluding sentence that
summarizes the mechanism underlying the relationship. We leverage this
structure and create a summarization task, where the input is a collection of
sentences and the main entities in an abstract, and the output includes the
relationship and a sentence that summarizes the mechanism. Using a small amount
of manually labeled mechanism sentences, we train a mechanism sentence
classifier to filter a large biomedical abstract collection and create a
summarization dataset with 22k instances. We also introduce conclusion sentence
generation as a pretraining task with 611k instances. We benchmark the
performance of large bio-domain language models. We find that while the
pretraining task help improves performance, the best model produces acceptable
mechanism outputs in only 32% of the instances, which shows the task presents
significant challenges in biomedical language understanding and summarization.

本文介绍了生物医学机制总结任务，并利用少量手工标注的机制句子，训练了机制句子分类器以过滤大量的生物医学摘要并创建了一个包含 22k 实例的总结数据集。此外，我们还引入了 611k 实例的结论句子生成作为预训练任务，并基准测试了大型生物领域语言模型的性能。我们发现，虽然预训练任务有助于提高性能，但最佳模型仅在 32％的情况下产生可接受的机制输出，这表明该任务在生物医学语言理解和总结方面面临巨大挑战。

SuMe: 面向生物医学机制概述的数据集

SuMe: A Dataset Towards Summarizing Biomedical Mechanisms

Artificial Intelligence (AI), along with the recent progress in biomedical
language understanding, is gradually changing medical practice. With the
development of biomedical language understanding benchmarks, AI applications
are widely used in the medical field. However, most benchmarks are limited to
English, which makes it challenging to replicate many of the successes in
English for other languages. To facilitate research in this direction, we
collect real-world biomedical data and present the first Chinese Biomedical
Language Understanding Evaluation (CBLUE) benchmark: a collection of natural
language understanding tasks including named entity recognition, information
extraction, clinical diagnosis normalization, single-sentence/sentence-pair
classification, and an associated online platform for model evaluation,
comparison, and analysis. To establish evaluation on these tasks, we report
empirical results with the current 11 pre-trained Chinese models, and
experimental results show that state-of-the-art neural models perform by far
worse than the human ceiling. Our benchmark is released at
\url{this https URL&lang=en-us}.

本文介绍了第一个中文生物医学语言理解基准评估（CBLUE），其涵盖了一系列自然语言处理任务，包括命名实体识别、信息抽取、临床诊断标准化、单句 / 句对分类，与相应的在线平台进行模型评估、比较和分析，并通过当前的 11 个预训练中文模型的实证结果表明，优秀的神经模型表现远低于人类水平。

CBLUE：一个中文生物医学语言理解评估基准

CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark

Automatic ICD coding is the task of assigning codes from the International
Classification of Diseases (ICD) to medical notes. These codes describe the
state of the patient and have multiple applications, e.g., computer-assisted
diagnosis or epidemiological studies. ICD coding is a challenging task due to
the complexity and length of medical notes. Unlike the general trend in
language processing, no transformer model has been reported to reach high
performance on this task. Here, we investigate in detail ICD coding using
PubMedBERT, a state-of-the-art transformer model for biomedical language
understanding. We find that the difficulty of fine-tuning the model on long
pieces of text is the main limitation for BERT-based models on ICD coding. We
run extensive experiments and show that despite the gap with current
state-of-the-art, pretrained transformers can reach competitive performance
using relatively small portions of text. We point at better methods to
aggregate information from long texts as the main need for improving BERT-based
ICD coding.

本研究使用 PubMedBERT，探究了自动 ICD 编码任务的性能。研究表明，目前的 BERT 模型在处理长文本时存在困难，提出更好的信息汇聚方法是改进 ICD 编码的主要需要。