Explainable and Ethical Machine Learning for Knowledge Discovery from Medical Data Sources

Objective
The Explainable and Ethical Machine Learning for Knowledge Discovery from Medical Data Sources (EXTREMUM) project aims to develop a novel platform for learning from complex medical data sources with a focus on two healthcare application areas: adverse drug event detection and early detection and treatment of cardiovascular diseases.

The team will present a new framework for data management and analysis of data integration, methods for machine learning, and ethical issues related to predictive models. This project’s fundamental breakthrough is establishing a novel knowledge management and discovery framework for medical data sources. The outcome will be a set of methods and tools for integrating complex medical data sources, a set of predictive models for learning from these sources emphasising interpretability and explanatory features, and simultaneously focusing on maintaining ethical integrity in the underlying decision mechanisms that rule machine learning.

Background
One of the biggest challenges that research and business entities have been facing recently is that the data provided by today’s technologies originate from multiple data sources in massive quantities and at rapid rates. In addition to their volume, such data sources are complex and heterogeneous. In the presence of these complex and continuously growing information sources, domain scientists, data analysts, and novice users have been struggling to manage this complexity and arrive from abundant data to usable and interpretable models and exploitable domain knowledge. Towards this end, these data sources need to be monitored in real-time. Hence, data integration indexing and predictive modelling have become a major challenge. Consider, for example, the healthcare domain, where numerous data sources in the form of Electronic Health Records (EHRs), such as billing codes, registry data, and pharmaceutical data, are used for developing predictive models of, e.g., heart failure prevention and treatment progression, or adverse drug effect (ADE) detection.

An example workflow of the EXTREMUM framework starts from radiography exams, ranking them based on their severity and urgency, then moving on to their classification and tagging while eventually producing clinically relevant explanatory text.

Cross-disciplinary collaboration
The project is a collaborative effort between four research institutions: the Department of Computer and Systems Sciences at Stockholm University, the Department of Law at Stockholm University, RISE Research Institutes Sweden, Division ICT, and the Department of Automatic Control, School of Electrical Engineering and Computer Science (EECS, KTH).

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