Medication recommendation is a crucial task for intelligent healthcare
systems. Previous studies mainly recommend medications with electronic health
records (EHRs). However, some details of interactions between doctors and
patients may be ignored or omitted in EHRs, which are essential for automatic
medication recommendation. Therefore, we make the first attempt to recommend
medications with the conversations between doctors and patients. In this work,
we construct DIALMED, the first high-quality dataset for medical dialogue-based
medication recommendation task. It contains 11,996 medical dialogues related to
16 common diseases from 3 departments and 70 corresponding common medications.
Furthermore, we propose a Dialogue structure and Disease knowledge aware
Network (DDN), where a QA Dialogue Graph mechanism is designed to model the
dialogue structure and the knowledge graph is used to introduce external
disease knowledge. The extensive experimental results demonstrate that the
proposed method is a promising solution to recommend medications with medical
dialogues. The dataset and code are available at
this https URL.

该研究提出一种基于医疗对话的药物推荐方法，使用自然语言处理技术结合病历数据，构建了一个高质量的医疗对话数据集 DIALMED, 并提出了 DDN 模型和 QA 对话图机制，有效考虑了各种因素的影响，实现了医患对话中对药物的智能推荐。

DialMed: 基于对话的药物推荐数据集

DialMed: A Dataset for Dialogue-based Medication Recommendation

Pre-trained language models such as ClinicalBERT have achieved impressive
results on tasks such as medical Natural Language Inference. At first glance,
this may suggest that these models are able to perform medical reasoning tasks,
such as mapping symptoms to diseases. However, we find that standard benchmarks
such as MedNLI contain relatively few examples that require such forms of
reasoning. To better understand the medical reasoning capabilities of existing
language models, in this paper we introduce DisKnE, a new benchmark for Disease
Knowledge Evaluation. To construct this benchmark, we annotated each positive
MedNLI example with the types of medical reasoning that are needed. We then
created negative examples by corrupting these positive examples in an
adversarial way. Furthermore, we define training-test splits per disease,
ensuring that no knowledge about test diseases can be learned from the training
data, and we canonicalize the formulation of the hypotheses to avoid the
presence of artefacts. This leads to a number of binary classification
problems, one for each type of reasoning and each disease. When analysing
pre-trained models for the clinical/biomedical domain on the proposed
benchmark, we find that their performance drops considerably.

本文介绍一种新的基准测试 ——DisKnE，用于评估疾病知识。该测试使用正例进行注释，并以对抗性的方式构建负例。在对临床 / 生物医学领域的预训练模型进行测试时，发现它们的表现大幅下降。

探索预训练语言模型中的疾病知识

Probing Pre-Trained Language Models for Disease Knowledge

Knowledge of a disease includes information of various aspects of the
disease, such as signs and symptoms, diagnosis and treatment. This disease
knowledge is critical for many health-related and biomedical tasks, including
consumer health question answering, medical language inference and disease name
recognition. While pre-trained language models like BERT have shown success in
capturing syntactic, semantic, and world knowledge from text, we find they can
be further complemented by specific information like knowledge of symptoms,
diagnoses, treatments, and other disease aspects. Hence, we integrate BERT with
disease knowledge for improving these important tasks. Specifically, we propose
a new disease knowledge infusion training procedure and evaluate it on a suite
of BERT models including BERT, BioBERT, SciBERT, ClinicalBERT, BlueBERT, and
ALBERT. Experiments over the three tasks show that these models can be enhanced
in nearly all cases, demonstrating the viability of disease knowledge infusion.
For example, accuracy of BioBERT on consumer health question answering is
improved from 68.29% to 72.09%, while new SOTA results are observed in two
datasets. We make our data and code freely available.

本文介绍了利用有关疾病的特定知识和 BERT 模型相结合来提高健康相关任务（例如消费者健康问答和医疗语言推理）的准确性，实验结果表明，这种方法在多数情况下都能够显著改善任务效果。