In online forums like Reddit, users share their experiences with medical
conditions and treatments, including making claims, asking questions, and
discussing the effects of treatments on their health. Building systems to
understand this information can effectively monitor the spread of
misinformation and verify user claims. The Task-8 of the 2023 International
Workshop on Semantic Evaluation focused on medical applications, specifically
extracting patient experience- and medical condition-related entities from user
posts on social media. The Reddit Health Online Talk (RedHot) corpus contains
posts from medical condition-related subreddits with annotations characterizing
the patient experience and medical conditions. In Subtask-1, patient experience
is characterized by personal experience, questions, and claims. In Subtask-2,
medical conditions are characterized by population, intervention, and outcome.
For the automatic extraction of patient experiences and medical condition
information, as a part of the challenge, we proposed language-model-based
extraction systems that ranked $3^{rd}$ on both subtasks' leaderboards. In this
work, we describe our approach and, in addition, explore the automatic
extraction of this information using domain-specific language models and the
inclusion of external knowledge.

利用基于语言模型的提取系统以及外部知识，从社交媒体用户帖子中自动提取与医疗状况相关的患者经历和医疗状况信息，可用于有效监测医疗谣言的传播和验证用户主张。

MasonNLP+ 在 SemEval-2023 任务 8 中的应用：基于知识增强预训练语言模型从社交媒体中提取医疗问题、经验和主张

MasonNLP+ at SemEval-2023 Task 8: Extracting Medical Questions,  Experiences and Claims from Social Media using Knowledge-Augmented  Pre-trained Language Models

Understanding patient experience in healthcare is increasingly important and
desired by medical professionals in a patient-centred care approach. Healthcare
discourse on social media presents an opportunity to gain a unique perspective
on patient-reported experiences, complementing traditional survey data. These
social media reports often appear as first-hand accounts of patients' journeys
through the healthcare system, whose details extend beyond the confines of
structured surveys and at a far larger scale than focus groups. However, in
contrast with the vast presence of patient-experience data on social media and
the potential benefits the data offers, it attracts comparatively little
research attention due to the technical proficiency required for text analysis.
In this paper, we introduce the Design-Acquire-Process-Model-Analyse-Visualise
(DAPMAV) framework to equip non-technical domain experts with a structured
approach that will enable them to capture patient-reported experiences from
social media data. We apply this framework in a case study on prostate cancer
data from /r/ProstateCancer, demonstrate the framework's value in capturing
specific aspects of patient concern (such as sexual dysfunction), provide an
overview of the discourse, and show narrative and emotional progression through
these stories. We anticipate this framework to apply to a wide variety of areas
in healthcare, including capturing and differentiating experiences across
minority groups, geographic boundaries, and types of illnesses.

本文通过 DAPMAV 框架对社交媒体患者报道经验进行了结构化处理和分析，在前列腺癌数据上展示了该框架的价值，可以应用于医疗保健的各个领域，并能捕捉和区分不同群体，地理边界和疾病类型的经验。

利用 DAPMAV 框架从社交媒体中披露患者报告的医疗经验

Revealing Patient-Reported Experiences in Healthcare from Social Media using the DAPMAV Framework

Healthcare organizations are in a continuous effort to improve health
outcomes, reduce costs and enhance patient experience of care. Data is
essential to measure and help achieving these improvements in healthcare
delivery. Consequently, a data influx from various clinical, financial and
operational sources is now overtaking healthcare organizations and their
patients. The effective use of this data, however, is a major challenge.
Clearly, text is an important medium to make data accessible. Financial reports
are produced to assess healthcare organizations on some key performance
indicators to steer their healthcare delivery. Similarly, at a clinical level,
data on patient status is conveyed by means of textual descriptions to
facilitate patient review, shift handover and care transitions. Likewise,
patients are informed about data on their health status and treatments via
text, in the form of reports or via ehealth platforms by their doctors.
Unfortunately, such text is the outcome of a highly labour-intensive process if
it is done by healthcare professionals. It is also prone to incompleteness,
subjectivity and hard to scale up to different domains, wider audiences and
varying communication purposes. Data-to-text is a recent breakthrough
technology in artificial intelligence which automatically generates natural
language in the form of text or speech from data. This chapter provides a
survey of data-to-text technology, with a focus on how it can be deployed in a
healthcare setting. It will (1) give an up-to-date synthesis of data-to-text
approaches, (2) give a categorized overview of use cases in healthcare, (3)
seek to make a strong case for evaluating and implementing data-to-text in a
healthcare setting, and (4) highlight recent research challenges.

数据转文本技术在医疗保健中有着广泛的应用，可以帮助医疗组织提高医疗服务的效果、降低成本，并改善患者的治疗体验。本文综述了数据转文本的各种方法，总结了其在医疗应用方面的分类和优势，并探讨了目前该技术在医疗领域面临的挑战。