This is an overview of the eleventh edition of the BioASQ challenge in the
context of the Conference and Labs of the Evaluation Forum (CLEF) 2023. BioASQ
is a series of international challenges promoting advances in large-scale
biomedical semantic indexing and question answering. This year, BioASQ
consisted of new editions of the two established tasks b and Synergy, and a new
task (MedProcNER) on semantic annotation of clinical content in Spanish with
medical procedures, which have a critical role in medical practice. In this
edition of BioASQ, 28 competing teams submitted the results of more than 150
distinct systems in total for the three different shared tasks of the
challenge. Similarly to previous editions, most of the participating systems
achieved competitive performance, suggesting the continuous advancement of the
state-of-the-art in the field.

本文概述了 BioASQ 挑战赛的第十一届比赛，并介绍了挑战赛的主要任务和研究领域，包括生物医学语义索引和问题回答，以及医学实践中关键的临床内容和医疗程序的语义标注。本届比赛总共收到了来自 28 支竞争队伍提交的 150 多个系统的结果，大部分参赛系统表现竞争力强，表明该领域的技术正在不断进步。

2023 年 BioASQ 概述：大规模生物医学语义索引和问题回答的第十一届 BioASQ 挑战赛

Overview of BioASQ 2023: The eleventh BioASQ challenge on Large-Scale  Biomedical Semantic Indexing and Question Answering

Web scraping is a technique that allows us to extract data from websites
automatically. in the field of medicine, web scraping can be used to collect
information about medical procedures, treatments, and healthcare providers.
this information can be used to improve patient care, monitor the quality of
healthcare services, and identify areas for improvement. one area where web
scraping can be particularly useful is in medical ministrations. medical
ministrations are the actions taken to provide medical care to patients, and
web scraping can help healthcare providers identify the most effective
ministrations for their patients. for example, healthcare providers can use web
scraping to collect data about the symptoms and medical histories of their
patients, and then use this information to determine the most appropriate
ministrations. they can also use web scraping to gather information about the
latest medical research and clinical trials, which can help them stay
up-to-date with the latest treatments and procedures.

网络爬虫技术可以帮助医疗保健提高患者护理、监测医疗服务质量和辨识改进领域，尤其可用于医疗行动范畴来确定最有效的护理方式，如收集患者症状和医史信息，或收集最新的医学研究和临床试验。

网页抓取的医疗服务

Medical ministrations through web scraping

We present an ongoing project that aims to develop a social robot to help
children cope with painful and distressing medical procedures in a clinical
setting. Our approach uses automated planning as a core component for action
selection in order to generate plans that include physical, sensory, and social
actions for the robot to use when interacting with humans. A key capability of
our system is that the robot's behaviour adapts based on the affective state of
the child patient. The robot must operate in a challenging physical and social
environment where appropriate and safe interaction with children,
parents/caregivers, and healthcare professionals is crucial. In this paper, we
present our system, examine some of the key challenges of the scenario, and
describe how they are addressed by our system.

开发一种社交机器人，通过自动化规划帮助儿童应对医疗操作中的疼痛和不适感，机器人行为会根据儿童的情感状态进行自适应调整。

在儿童临床环境中使用自动规划的社交辅助机器人

A Socially Assistive Robot using Automated Planning in a Paediatric Clinical Setting

Deformable registration is a crucial step in many medical procedures such as
image-guided surgery and radiation therapy. Most recent learning-based methods
focus on improving the accuracy by optimizing the non-linear spatial
correspondence between the input images. Therefore, these methods are
computationally expensive and require modern graphic cards for real-time
deployment. In this paper, we introduce a new Light-weight Deformable
Registration network that significantly reduces the computational cost while
achieving competitive accuracy. In particular, we propose a new adversarial
learning with distilling knowledge algorithm that successfully leverages
meaningful information from the effective but expensive teacher network to the
student network. We design the student network such as it is light-weight and
well suitable for deployment on a typical CPU. The extensively experimental
results on different public datasets show that our proposed method achieves
state-of-the-art accuracy while significantly faster than recent methods. We
further show that the use of our adversarial learning algorithm is essential
for a time-efficiency deformable registration method. Finally, our source code
and trained models are available at: this https URL

本文提出了一种新的轻量级可变形配准网络，使用对抗学习算法成功地将教师网络的有意义信息转移到了学生网络，同时在显著降低计算费用的同时达到了竞争性的准确性。