Decision Support
❯
Predict
❯Applying machine learning to predict patient risk for in-hospital new infections
A fairer and more personalised credit decision
For:
Payment and credit providers
Goal:
Improved Customer Experience
Moving Beyond Generalised Linear Models: A sophisticated pricing strategy incorporating competitor pricing
For:
Customer pricing
Actuaries
Goal:
Increase Revenues
Machine-learning based triage to determine low-severity patients that can be fast-tracked to admission in ED due to their short discharge length
For:
Emergency Departments in hospitals.
Goal:
Improved Employee Efficiency
Disaster and Emergency Prediction & Impact Model
For:
National-level disaster management professionals, Climate change adaptation experts, Government agencies, At-risk communities
Automated threat enrichment intelligence in Cyber security
Goal:
Improve Operation Efficiency
Deep reinforcement learning for personalized treatment recommendation
For:
Clinicians who prescribe long-term and adjustable treatments
Goal:
Improved Employee Efficiency
Applying machine learning to predict patient risk for in-hospital new infections
For:
Hospitals
Goal:
Improved Customer Experience, Anticipate Risks
Large Companies
Small Companies
Management - Market Intelligence
For:
Product managers and Marketers.
Scope:
Growing active users for a music streaming service by identifying high-value customer behavior.
Goal:
Improved Customer Experience, Increase Revenues
Weather Forecasting in Agriculture
For:
Farmers
Goal:
Anticipate Risks, Improve Operation Efficiency
AI for customers' loss prevention of telecommunication services
For:
Telecommunication companies
Goal:
Improved Customer Experience
Large Companies
Manufacturing and Factories – Predictive Maintenance
For:
Operation, R&D
Scope:
Avoiding unplanned shutdowns in Manufacturing using Machine Learning to predict failure states in equipment.
Goal:
Anticipate Risks, Improve Operation Efficiency
Disaster and Emergency Prediction & Impact Model
For:
National-level disaster management professionals, Climate change adaptation experts, Government agencies, At-risk communities.
Applying machine learning to predict patient risk for in-hospital new infections
For:
HospitalsGoal:
Improved Customer Experience, Anticipate RisksProblem addressed
Sometimes an infection is contracted at the hospital or a complication occurs after surgery. Such health-related adverse events occur in 8% to 12% of all hospitalizations, according to the World Health Organization.
A 2017 report from the Organisation for Economic Co-operation and Development (OECD) shows that more than 10% of hospital spending is related to the treatment of health-related adverse events that occur during hospitalizations.
Description
The CHUGA hospital in France teamed up with Elsevier to apply machine learning to their historical patient data with the aim of creating models that identify patients at higher risk for healthcare-related adverse events.
The models identify the top 5% of patients with a 4.7x increased risk for life threatening events like thromboembolism or a 40% risk of a prolonged hospital stay. This will allow the hospital to flag patients that fit the risk profile and provide more directed care.
Raw Data
AI: Perceive
Machine Learning
AI: Understand
Predict / Forecast
AI: Act