Tracking Upper Limb Motion via Wearable Solutions: Systematic Review of Research From 2011 to 2023.

Background: The development of wearable solutions for tracking upper limb motion has gained research interest over the past decade. This paper provides a systematic review of related research on the type, feasibility, signal processing techniques, and feedback of wearable systems for tracking upper limb motion, mostly in rehabilitation applications, to understand and monitor human movement.

Objective: The aim of this article is to investigate how wearables are used to capture upper limb functions, especially related to clinical and rehabilitation applications.

Assessing the impact of ultrasound image standardization in deep learning-based segmentation of carotid plaque types.

Background And Objective: Carotid B-mode ultrasound (CBUS) imaging is often used to detect and assess atherosclerotic plaques. Doctors often need to segment plaques in the CBUS images to further examine them. Multiple studies have proposed two-dimensional CBUS plaque segmentation deep learning (DL)-based solutions, achieving promising results.

Explainable AI Modeling in the Prediction of Cardiovascular Disease Risk.

The objective of this study was to develop explainable AI modeling in the prediction of cardiovascular disease. The XGBoost algorithm was used followed by rule extraction and argumentation theory that provides interpretability, explainability and accuracy in scenarios with low confidence results or dilemmas. Our findings are in agreement with previous research utilizing the XGBoost machine learning algorithm for prediction of cardiovascular risk, however it is supported by rule based explainability, offering significant advantages in terms of providing both global and local explainability.

An Overview of Explainable AI Studies in the Prediction of Sepsis Onset and Sepsis Mortality.

Explainable artificial intelligence (AI) focuses on developing models and algorithms that provide transparent and interpretable insights into decision-making processes. By elucidating the reasoning behind AI-driven diagnoses and treatment recommendations, explainability can gain the trust of healthcare experts and assist them in difficult diagnostic tasks. Sepsis is characterized as a serious condition that happens when the immune system of the body has an extreme response to an infection, causing tissue and organ damage and leading to death.

Emergency Department Length of Stay Classification Based on Ensemble Methods and Rule Extraction.

This study employs machine learning techniques to identify factors that influence extended Emergency Department (ED) length of stay (LOS) and derives transparent decision rules to complement the results. Leveraging a comprehensive dataset, Gradient Boosting exhibited marginally superior predictive performance compared to Random Forest for LOS classification. Notably, variables like triage acuity and the Elixhauser Comorbidity Index (ECI) emerged as robust predictors.