A5021155076- Human-Automation Interaction and Safety
- Health Systems, Economic Evaluations, Quality of Life
- Cardiovascular Function and Risk Factors
- Chronic Disease Management Strategies
- Machine Learning in Healthcare
- Geriatric Care and Nursing Homes
- Evacuation and Crowd Dynamics
- Sepsis Diagnosis and Treatment
- Facility Location and Emergency Management
- Heart Failure Treatment and Management
- Hydrological Forecasting Using AI
Imperial College London
2018
Worldwide, sepsis is the leading cause of death in hospitals. If mortality rates patients with can be predicted early, medical resources allocated efficiently. We constructed machine learning (ML) models to predict a hospital emergency department.
The number of out hospital cardiac arrests has been on the rise for a variety reasons. While arrest poses serious threat to life, timely use an Automated External Defibrillator (AED) can greatly increase patient’s survival rate. distance between AED and site impact As such, when determining location AEDs, it is crucial take into account inverse relationship response time. In situations where multiple occur in close proximity, may be necessary have several AEDs available at single location....