Background: Postoperative nausea and vomiting (PONV) is a frequently observed
complication in patients undergoing surgery under general anesthesia. Moreover,
it is a frequent cause of distress and dissatisfaction during the early
postoperative period. The tools used for predicting PONV at present have not
yielded satisfactory results. Therefore, prognostic tools for the prediction of
early and delayed PONV were developed in this study with the aim of achieving
satisfactory predictive performance.
Methods: The retrospective data of adult patients admitted to the
post-anesthesia care unit after undergoing surgical procedures under general
anesthesia at the Sheba Medical Center, Israel, between September 1, 2018, and
September 1, 2023, were used in this study. An ensemble model of machine
learning algorithms trained on the data of 54848 patients was developed. The
k-fold cross-validation method was used followed by splitting the data to train
and test sets that optimally preserve the sociodemographic features of the
patients, such as age, sex, and smoking habits, using the Bee Colony algorithm.
Findings: Among the 54848 patients, early and delayed PONV were observed in
2706 (4.93%) and 8218 (14.98%) patients, respectively. The proposed PONV
prediction tools could correctly predict early and delayed PONV in 84.0% and
77.3% of cases, respectively, outperforming the second-best PONV prediction
tool (Koivuranta score) by 13.4% and 12.9%, respectively. Feature importance
analysis revealed that the performance of the proposed prediction tools aligned
with previous clinical knowledge, indicating their utility.
Interpretation: The machine learning-based tools developed in this study
enabled improved PONV prediction, thereby facilitating personalized care and
improved patient outcomes.

该研究利用机器学习开发了基于临床数据的新型工具，用于预测手术后恶心和呕吐的发生，以提供个性化的护理，并改善患者的治疗效果。

使用机器学习预测术后恶心和呕吐：模型开发与验证研究

Predicting Postoperative Nausea And Vomiting Using Machine Learning: A  Model Development and Validation Study

After the pandemic, artificial intelligence (AI) powered support for mental
health care has become increasingly important. The breadth and complexity of
significant challenges required to provide adequate care involve: (a)
Personalized patient understanding, (b) Safety-constrained and medically
validated chatbot patient interactions, and (c) Support for continued
feedback-based refinements in design using chatbot-patient interactions. We
propose Alleviate, a chatbot designed to assist patients suffering from mental
health challenges with personalized care and assist clinicians with
understanding their patients better. Alleviate draws from an array of publicly
available clinically valid mental-health texts and databases, allowing
Alleviate to make medically sound and informed decisions. In addition,
Alleviate's modular design and explainable decision-making lends itself to
robust and continued feedback-based refinements to its design. In this paper,
we explain the different modules of Alleviate and submit a short video
demonstrating Alleviate's capabilities to help patients and clinicians
understand each other better to facilitate optimal care strategies.

本研究介绍了一个名为 “Alleviate” 的机器人的不同模块，这是一个帮助患者和临床医生更好地理解彼此，以便促进最佳护理策略的人工智能聊天机器人，其可以提供个性化护理和辅助临床医生更好地了解其患者的挑战。

演示 AI 功能的远程健康虚拟助手：以心理健康为例

Demo Alleviate: Demonstrating Artificial Intelligence Enabled Virtual  Assistance for Telehealth: The Mental Health Case

The rapid progress in digitalization and automation have led to an
accelerated growth in healthcare, generating novel models that are creating new
channels for rendering treatment with reduced cost. The Metaverse is an
emerging technology in the digital space which has huge potential in
healthcare, enabling realistic experiences to the patients as well as the
medical practitioners. The Metaverse is a confluence of multiple enabling
technologies such as artificial intelligence, virtual reality, augmented
reality, internet of medical devices, robotics, quantum computing, etc. through
which new directions for providing quality healthcare treatment and services
can be explored. The amalgamation of these technologies ensures immersive,
intimate and personalized patient care. It also provides adaptive intelligent
solutions that eliminates the barriers between healthcare providers and
receivers. This article provides a comprehensive review of the Metaverse for
healthcare, emphasizing on the state of the art, the enabling technologies for
adopting the Metaverse for healthcare, the potential applications and the
related projects. The issues in the adaptation of the Metaverse for healthcare
applications are also identified and the plausible solutions are highlighted as
part of future research directions.

该文章综合评述了 Metaverse 技术在医疗保健领域的最新状况、应用潜力、相关项目及其在医疗保健领域应用中的问题和解决方案。

医疗健康元宇宙：潜在应用、挑战和未来方向综述

Metaverse for Healthcare: A Survey on Potential Applications, Challenges and Future Directions

Most machine learning (ML) models are developed for prediction only; offering
no option for causal interpretation of their predictions or
parameters/properties. This can hamper the health systems' ability to employ ML
models in clinical decision-making processes, where the need and desire for
predicting outcomes under hypothetical investigations (i.e., counterfactual
reasoning/explanation) is high. In this research, we introduce a new
representation learning framework (i.e., partial concept bottleneck), which
considers the provision of counterfactual explanations as an embedded property
of the risk model. Despite architectural changes necessary for jointly
optimising for prediction accuracy and counterfactual reasoning, the accuracy
of our approach is comparable to prediction-only models. Our results suggest
that our proposed framework has the potential to help researchers and
clinicians improve personalised care (e.g., by investigating the hypothetical
differential effects of interventions)

本研究提出了一种新的表示学习框架 - 部分概念瓶颈，该框架将反事实解释作为风险模型的嵌入式属性，有助于研究人员和临床医生改进个性化护理，探索干预的假设性差异效应。