Machine learning for patient stratification and outcome prediction from ‘real world evidence’

Electronic health records (EHR) and other medical data (such as medical images) has potential to find substructures or subtypes of disease.

For example, within heart failure there are currently recognised differences in a patient’s heart efficiency. A measurement system to assess this efficiency difference – known as “ejection fraction”, measures the percentage of blood leaving and entering the heart. Approximately half of people with heart failure have preserved ejection fraction, while the other half have reduced ejection fraction. Rather than only using clinically driven parameters, machine learning offers an opportunity to take data driven approaches based on the patient diagnosis pathway to better predict outcomes or stratify disease subtypes.

In this talk, Ben will discuss Sensyne Health’s approach in using AI to personalise treatment and provide examples of the type of analysis being performed at Sensyne Health.