Explainable Machine Learning for Early Warning Systems

Objective
In collaboration with Karolinska University Hospital (KUH) and Karolinska Institute (KI), the PI and Co-PI of KTH propose the ISPP postdoc project EMERDENSY to develop trust-worthy machine learning algorithms with explainable outcomes and then use the algorithms for the design of Early Warning Systems (EWS).

Background
Artificial Intelligence (AI) can be used to detect infection. Often, doctors and nurses cannot be sure about the growth of infection due to the absence of clearly visible symptoms. Once infection starts, the body’s immune system starts to fight bacteria and viruses. Physiological parameters of the body, such as heart rate, blood pressure, breathing patterns, and temperature, change slowly.

AI can detect subtle changes, but humans cannot. AI can also predict infection type and patient deterioration. The medical care team will then spend precious time deciding on life-saving interventions. This heralds the use of AI-based early warning systems (EWS).

The big question: can we trust the AI systems, mainly its core called machine learning for data analysis and predictions? Can the machine learning algorithms explain their predictions to the healthcare personnel?

About the Digital Futures Industrial Postdocs
To be announced

Main supervisor
Saikat Chatterjee, Associate Professor, Division of Information Science and Engineering at KTH

Co-supervisor
Sebastiaan Meijer, Professor and Vice Dean, Division of Health Informatics and Logistics at KTH

Watch the recorded presentation at the Digitalize in Stockholm 2023 event: