A5042814540- Genetic Associations and Epidemiology
- ECG Monitoring and Analysis
- Artificial Intelligence in Healthcare
- Epigenetics and DNA Methylation
- Chronic Disease Management Strategies
- Facility Location and Emergency Management
- Cardiovascular Health and Risk Factors
- Cardiac Health and Mental Health
- Machine Learning in Healthcare
- Global Health Care Issues
- Trauma and Emergency Care Studies
Medirex Group Academy
2023-2025
Edinburgh Cancer Research
2021-2023
Type 2 diabetes mellitus (T2D) presents a major health and economic burden that could be alleviated with improved early prediction intervention. While standard risk factors have shown good predictive performance, we show the use of blood-based DNA methylation information leads to significant improvement in 10-year T2D incidence risk. Previous studies been largely constrained by linear assumptions, cytosine–guanine pairs one-at-a-time binary outcomes. We present flexible approach (via an R...
Cardiogenic shock (CS) is a severe complication of acute coronary syndrome (ACS) with mortality rates approaching 50%. The ability to identify high-risk patients prior the development CS may allow for pre-emptive measures prevent CS. objective was derive and externally validate simple, machine learning (ML)-based scoring system using variables readily available at first medical contact predict risk developing during hospitalization in ACS. Observational multicentre study on ACS hospitalized...
Recent advances in machine learning provide new possibilities to process and analyse observational patient data predict outcomes. In this paper, we introduce a processing pipeline for cardiogenic shock (CS) prediction from the MIMIC III database of intensive cardiac care unit patients with acute coronary syndrome. The ability identify high-risk could possibly allow taking pre-emptive measures thus prevent development CS. We mainly focus on techniques imputation missing by generating...
The reorganization of an emergency medical system means that we look for new locations ambulance stations with the aim improving accessibility service. We applied two tools are well known in operations research community, namely mathematical programming, and computer simulation. Using hierarchical pq-median model, proposed optimal throughout country within large towns. Several solutions have been calculated differ number supposed to be relocated positions. by programming model were evaluated...
Abstract Type 2 diabetes mellitus (T2D) presents a major health and economic burden that could be alleviated with improved early prediction intervention. While standard risk factors have shown good predictive performance, we show the use of blood-based DNA methylation information leads to significant improvement in 10-year T2D incidence risk. Previous studies been largely constrained by linear assumptions, CpGs one-at-a-time, binary outcomes. We present flexible approach (via an R package,...
Background: Cardiogenic shock (CS) complicating acute coronary syndrome (ACS) is a life-threatening condition with mortality reaching 50% despite the use of mechanical circulatory support devices (MCS). It hypothesized that early implantation MCS before hemodynamic deterioration could prevent CS. For this purpose, we have developed and externally validated an AI model for CS prediction available as smartphone application (STOPSHOCK app). Research question: Could STOPSHOCK app identify ACS...