Machine Learning solutions have spread along many domains, including critical
applications. The development of such models usually relies on a dataset containing …

Algorithmic evaluation is a vital step in developing new approaches to machine learning and
relies on the availability of existing datasets. However, real-world datasets often do not cover …

Purpose Machine learning (ML) methods showed a higher accuracy in identifying
individuals without cancer who were unable to return to work (RTW) compared to the …

The digitisation of healthcare records has generated vast amounts of unstructured data,
presenting opportunities for improvements in disease diagnosis when clinical coding falls …

A key research theme identified during the 2021 Strategic Delivery Plan (SDP) for Defence
Medical Services (DMS) Research was preventing and treating musculoskeletal injury …

Machine Learning (ML) models have the potential to support decision-making in healthcare
by grasping complex patterns within data. However, decisions in this domain are sensitive …

Trust is a crucial factor affecting the adoption of machine learning (ML) models. Qualitative
studies have revealed that end-users, particularly in the medical domain, need models that …

The health domain has been largely benefited by Machine Learning solutions, which can be
used for building predictive models to support medical decisions. But, for increasing the …

Abstract Introduction and Objective. Compartment syndrome (CS) is a severe and rapidly
progressing condition associated with muscle compartments restricted by fascia. It most …

Background: Previous studies on populations with exercise-related leg pain (ERLP) have
suggested differences in symptoms and treatment outcomes between civilian and military …