Decision Support
❯
Predict
❯Deep reinforcement learning for personalized treatment recommendation
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.
Deep reinforcement learning for personalized treatment recommendation
For:
Clinicians who prescribe long-term and adjustable treatmentsGoal:
Improved Employee EfficiencyProblem addressed
One main challenge in precision medicine is to understand common disease at the molecular level to recommend individualized therapies to patients, permitting high efficacy for different and possibly unknown disease subtypes. Another challenge in clinical practice is how to adapt treatment assignments to possible changes in patients' health states and preferences, including previous treatment history.
Description
Formulating a ranking system measuring the efficiency of the drugs as a scoring function that scores each candidate drug at each ranking position. The scores usually represent some relevance, inducing an ordering of the drugs by sorting them in descending order of relevance to form a ranked list at each position. When moving to the next position, according to the dynamic change of the candidate drugs, the context environment, and the corresponding feature vectors, the scores for the remaining drugs may change under the scoring function.
Raw Data
AI: Perceive
Deep Learning
AI: Understand
Predict / Forecast
AI: Act