DISA

When? Friday September 20th 14-16
Where? Onsite: D0073 at Linnaeus University in Växjö and online
Registration: Please sign up for the PhD-seminar via this link https://forms.gle/h8oQ9VVYJUFu89i77 by September 18th (especially important if you plan on attending onsite so we have fika for everyone)

Agenda

14.00-14.10 Welcome and practical information from Welf Löwe
14.10-14.55 Presentation and discussion: Improving medication safety though the collaboration between researchers from medicine and computer science – Tora Hammar
14.55 – 15.05 Coffee break
15.05 – 15.50 Presentation and discussion – A machine learning approach to improving drug risk assessment – Daniel Nilsson
15.50 -16.00 Sum up and plan for the October seminar

Abstracts

Improving medication safety though the collaboration between researchers from medicine and computer science – Tora Hammar
In my presentation I will talk about our research project where we use health data to see if we can predict medication related problems and improve predictions compared with the current clinical decision support systems (CDSS) used in health care. The project is an interdisciplinary collaboration between researchers from medicine, pharmacy and computer science.

Medication usage and the simultaneous use of many medications is increasing world-wide. Problems with side-effects (adverse drug events) are common and cause suffering and even death, as well as substantial costs for society. One method to prevent harmful combinations of medications is by using CDSS in health care or at pharmacies that can detect potential ADEs. Todays CDSS are often based on rules written by humans (so called knowledge databases). Although we have high quality knowledge databases in Sweden these systems have known weaknesses such as having to many non-relevant alerts causing alert fatigue among users. One reason is that the rules are often very simple, another reason is that they have not been validated in large populations. In our research project we use data from health care in the region of Kalmar County for over ten years of time, including data on all medications being used during that time. We also have the rules and algorithms from the Swedish knowledge databases called Janusmed which is used in health care to give warnings about potentially harmful combinations of medications. In the project we aim to:

• Increase knowledge of effects when combining many different medications
• Study how well current CDSS can predict adverse drug events (Spoiler alert! Not very well.)
• Examine if we can improve predictions compared with the current CDSS by using machine learning.
• Develop methods to identify adverse drug events in clinical notes (unstructured data) using large language models.

Tora is an associate professor in health informatics at the eHealth Institute at Linnaeus University. She is a pharmacist, with a master and PhD in Biomedical Science. In her research she is using different methods to improve information systems and decision support in the medication management process. Much of her research is done in collaboration with computer science as a part of LnuC DISA, and she is the research leader for the DISA eHealth group.

Daniel Nilsson who is presenting after Tora is working in the research project Tora is presenting and will dive deeper into some of the questions.

A machine learning approach to improving drug risk assessment – Daniel Nilsson

Many medications are associated with adverse side effects. An understanding of what factors influence the risk of adverse drug events is important for managing this risk. In this presentation I will describe the results of a project to investigate ways to improve risk assessments for two categories of adverse drug events (bleedings and QT-prolongation) compared to the currently used knowledge database Janusmed. Using data on adverse drug events from the healthcare information system from Kalmar region (comprising ten years of event data, and ~280 000 patients), we seek to use machine learning methods to answer questions such as:

• Do the Janusmed risk values provide predictive information?
• Can we combine the Janusmed risk values (which only contain information of current medication) with demographic information and additional health data to improve predictions?
• Are the risk values assigned to different medications in Janusmed in alignment with the risks observed in the data?

Daniel is working in the DISA eHealth group as part of the AI Sweden program Eye for AI. He has a PhD in computational biology from Lund University, where he studied computational methods for protein simulation.