Respiratory sound classification (RSC) is challenging due to varied acoustic
signatures, primarily influenced by patient demographics and recording
environments. To address this issue, we introduce a text-audio multimodal model
that utilizes metadata of respiratory sounds, which provides useful
complementary information for RSC. Specifically, we fine-tune a pretrained
text-audio multimodal model using free-text descriptions derived from the sound
samples' metadata which includes the gender and age of patients, type of
recording devices, and recording location on the patient's body. Our method
achieves state-of-the-art performance on the ICBHI dataset, surpassing the
previous best result by a notable margin of 1.17%. This result validates the
effectiveness of leveraging metadata and respiratory sound samples in enhancing
RSC performance. Additionally, we investigate the model performance in the case
where metadata is partially unavailable, which may occur in real-world clinical
setting.

通过利用呼吸音样本的元数据，我们引入了一个文本 - 音频多模态模型来改进呼吸音分类的性能，并且在治疗临床环境中部分元数据不可用的情况下也进行了研究验证。

BTS：基于元数据辅助的呼吸音分类的文本和声音模态的桥接

BTS: Bridging Text and Sound Modalities for Metadata-Aided Respiratory  Sound Classification

The large amount of time clinicians spend sifting through patient notes and
documenting in electronic health records (EHRs) is a leading cause of clinician
burnout. By proactively and dynamically retrieving relevant notes during the
documentation process, we can reduce the effort required to find relevant
patient history. In this work, we conceptualize the use of EHR audit logs for
machine learning as a source of supervision of note relevance in a specific
clinical context, at a particular point in time. Our evaluation focuses on the
dynamic retrieval in the emergency department, a high acuity setting with
unique patterns of information retrieval and note writing. We show that our
methods can achieve an AUC of 0.963 for predicting which notes will be read in
an individual note writing session. We additionally conduct a user study with
several clinicians and find that our framework can help clinicians retrieve
relevant information more efficiently. Demonstrating that our framework and
methods can perform well in this demanding setting is a promising proof of
concept that they will translate to other clinical settings and data modalities
(e.g., labs, medications, imaging).

通过利用电子病历审计日志，我们的工作在特定的临床背景和特定时间点上，将机器学习作为便签相关性的监督源进行概念化，以降低临床医生在记录过程中查找相关病史所需的努力。我们的评估集中在急诊部动态检索，这是一个具有独特信息检索和记录编写模式的高急症设置。我们展示了我们的方法在预测哪些便签将在个人记录会话中被阅读方面可以达到 0.963 的 AUC。此外，我们对数名临床医生进行了用户研究，发现我们的框架可以帮助临床医生更有效地检索相关信息。在这个要求高的环境中展示我们的框架和方法表现良好是一个有希望的概念验证，表明它们将适用于其他临床设置和数据模态（例如实验室、药物、影像）。

电子健康记录笔记的动态信息检索的机器学习概念化

Conceptualizing Machine Learning for Dynamic Information Retrieval of  Electronic Health Record Notes

Modern electronic health records (EHRs) provide data to answer clinically
meaningful questions. The growing data in EHRs makes healthcare ripe for the
use of machine learning. However, learning in a clinical setting presents
unique challenges that complicate the use of common machine learning
methodologies. For example, diseases in EHRs are poorly labeled, conditions can
encompass multiple underlying endotypes, and healthy individuals are
underrepresented. This article serves as a primer to illuminate these
challenges and highlights opportunities for members of the machine learning
community to contribute to healthcare.

该论文阐述了电子病历（EHR）提供数据以回答临床相关问题，EHR 中不完整的标签、供建多个基础病因的疾病和健康人群缺乏等问题给机器学习在临床应用中带来的挑战，并强调了机器学习在医疗保健中发挥作用的机会。