Predictive modelling and identification of critical variables of mortality risk in COVID-19 patients.

South Africa was the most affected country in Africa by the coronavirus disease 2019 (COVID-19) pandemic, where over 4 million confirmed cases of COVID-19 and over 102,000 deaths have been recorded since 2019. Aside from clinical methods, artificial intelligence (AI)-based solutions such as machine learning (ML) models have been employed in treating COVID-19 cases. However, limited application of AI for COVID-19 in Africa has been reported in the literature.

Detecting the most critical clinical variables of COVID-19 breakthrough infection in vaccinated persons using machine learning.

Background: COVID-19 vaccines offer different levels of immune protection but do not provide 100% protection. Vaccinated persons with pre-existing comorbidities may be at an increased risk of SARS-CoV-2 breakthrough infection or reinfection. The aim of this study is to identify the critical variables associated with a higher probability of SARS-CoV-2 breakthrough infection using machine learning.

From Siloed Applications to National Digital Health Ecosystems: A Strategic Perspective for African Countries.

Substantial investment in digital solutions for improved health services has occurred in recent years in Africa. Digital Health provides for proven, beneficial applications in many different areas of health systems. It supports the transformation of healthcare delivery, and its potential is seemingly boundless.

Model-Driven Paediatric Cardiomyopathy Pathways - A Clinical Impact Assessment.

Intermediate results from an ongoing health technology assessment exercise of a simulation model of paediatric cardiomyopathy are reported. Comprehensive data on paediatric cardiomyopathy/heart failure, treatment options, incidence and prevalence, prognoses for different outcomes to be expected were collected. Based on this knowledge, a detailed clinical pathway model was developed and validated against the clinical workflow in a tertiary paediatric care hospital.

Meeting the Semantic Challenge of the Globally Unique Identification of Medicinal Products - The openMedicine Approach.

To better enable cross-border healthcare delivery, particularly the exchange of ePrescriptions, this global undertaking advances the unique identification of medicinal products (MPs) and patient safety in cross-border settings. Major stakeholders harmonise their respective efforts to deliver • common data models for prescribed MPs • a common vocabulary for unambiguous definition, description, and identification of MPs • rules to harmonise practices of therapeutic and economic substitution • a global roadmap for post-project actions and implementations Based on earlier activities of standard development organisations (SDOs), use case scenarios are developed, where the identification of an MP is an issue, including pharmacological and pharmacokinetic attributes, clinical indications, and risks to be considered. Next, the univocal identification of MPs is addressed, for standard pre-packed ones as well as for special cases like MPs with multi-components, biologics, or special packaging.