Visuospatial neglect is a disorder characterised by impaired awareness for
visual stimuli located in regions of space and frames of reference. It is often
associated with stroke. Patients can struggle with all aspects of daily living
and community participation. Assessment methods are limited and show several
shortcomings, considering they are mainly performed on paper and do not
implement the complexity of daily life. Similarly, treatment options are sparse
and often show only small improvements. We present an artificial intelligence
solution designed to accurately assess a patient's visuospatial neglect in a
three-dimensional setting. We implement an active learning method based on
Gaussian process regression to reduce the effort it takes a patient to undergo
an assessment. Furthermore, we describe how this model can be utilised in
patient oriented treatment and how this opens the way to gamification,
tele-rehabilitation and personalised healthcare, providing a promising avenue
for improving patient engagement and rehabilitation outcomes. To validate our
assessment module, we conducted clinical trials involving patients in a
real-world setting. We compared the results obtained using our AI-based
assessment with the widely used conventional visuospatial neglect tests
currently employed in clinical practice. The validation process serves to
establish the accuracy and reliability of our model, confirming its potential
as a valuable tool for diagnosing and monitoring visuospatial neglect. Our VR
application proves to be more sensitive, while intra-rater reliability remains
high.

提出了一种基于人工智能的方法来准确评估患者的视空间忽略症，为个性化的康复和医疗护理提供了有前景的途径并通过与常规测试的比较来验证了其准确性和可靠性。

采用高斯过程回归进行主动学习的视觉空间忽略评估和治疗

Assessment and treatment of visuospatial neglect using active learning  with Gaussian processes regression

Social media sites have become a popular platform for individuals to seek and
share health information. Despite the progress in natural language processing
for social media mining, a gap remains in analyzing health-related texts on
social discourse in the context of events. Event-driven analysis can offer
insights into different facets of healthcare at an individual and collective
level, including treatment options, misconceptions, knowledge gaps, etc. This
paper presents a paradigm to characterize health-related information-seeking in
social discourse through the lens of events. Events here are board categories
defined with domain experts that capture the trajectory of the
treatment/medication. To illustrate the value of this approach, we analyze
Reddit posts regarding medications for Opioid Use Disorder (OUD), a critical
global health concern. To the best of our knowledge, this is the first attempt
to define event categories for characterizing information-seeking in OUD social
discourse. Guided by domain experts, we develop TREAT-ISE, a novel multilabel
treatment information-seeking event dataset to analyze online discourse on an
event-based framework. This dataset contains Reddit posts on
information-seeking events related to recovery from OUD, where each post is
annotated based on the type of events. We also establish a strong performance
benchmark (77.4% F1 score) for the task by employing several machine learning
and deep learning classifiers. Finally, we thoroughly investigate the
performance and errors of ChatGPT on this task, providing valuable insights
into the LLM's capabilities and ongoing characterization efforts.

通过事件的视角，本文提出了一种用于表征社交话语中与健康相关的信息搜寻的范例。利用 Reddit 帖子进行案例研究，以药物治疗 Opioid Use Disorder (OUD) 为例，建立了 TREAT-ISE 数据集，该数据集包含基于不同事件类型进行注释的 OUD 社交话语中的信息搜寻事件。并借助多个机器学习和深度学习分类器，建立了一个强大的性能基准（77.4% F1 score），并深入研究了 ChatGPT 在这一任务上的表现和错误，提供了有价值的见解和持续的表征工作。

健康相关社交对话中信息查询事件的特征化

Characterizing Information Seeking Events in Health-Related Social  Discourse

The propensity score is a common tool for estimating the causal effect of a
binary treatment in observational data. In this setting, matching,
subclassification, imputation, or inverse probability weighting on the
propensity score can reduce the initial covariate bias between the treatment
and control groups. With more than two treatment options, however, estimation
of causal effects requires additional assumptions and techniques, the
implementations of which have varied across disciplines. This paper reviews
current methods, and it identifies and contrasts the treatment effects that
each one estimates. Additionally, we propose possible matching techniques for
use with multiple, nominal categorical treatments, and use simulations to show
how such algorithms can yield improved covariate similarity between those in
the matched sets, relative the pre-matched cohort. To sum, this manuscript
provides a synopsis of how to notate and use causal methods for categorical
treatments.

本文介绍了使用倾向得分法估算二元治疗的因果效应，探讨了一些减少治疗和对照组之间初始协变量偏差的方法，并针对多种分类治疗方法提出了可能的匹配技术，利用模拟演示了如何提高匹配集合中协变量的相似度。