Project: Prediction of medication risks and drug-related problems
Medication related problems is a major problem for society, especially with an ageing population and increasing use of medicines. This research project will provide better knowledge about the prevalence of potential drug-related problems, how well current decision support systems and knowledge databases can predict actual problems, and how they can be improved.
Full project name
Prediction of medication risks and drug-related problems – a novel pharmacoepidemiological approach using real-world data, decision support algorithms and machine learning
Other project members
Alisa Lincke, Olle Björneld, Rafael Messias Martins, Welf Löwe, Thomas Holgersson, Linnaeus University; Ylva Askfors, Hanna Justad, Region Stockholm; Björn Wettermark, Uppsala University; Marine Andersson, Karolinska universitetssjukhuset Huddinge
Linnaeus University; Region Stockholm; Uppsala University, Karolinska University Hospital
1 jan 2023 – 31 dec 2025
eHealth, Health informatics (Department of Medicine and Optometry, Faculty of Health and Life Sciences)
More about the project
Research problem and specific questions
Drug-related problems (DRPs) occur frequently and is a common cause of hospitalizations and death. Many DRPs may be prevented by using clinical decision support systems in health care. In Sweden we have Janusmed knowledge databases, developed as decision support to detect potential drug-drug interactions, side-effects, and inappropriate dosing. There is a need for better knowledge about the prevalence of these potential DRPs, how well current knowledge databases can predict actual problems, and how they can be improved. The research project has three work packages to answer the following research questions:
- What is the prevalence of potential DRPs identified using Janusmed algorithms in the region of Kalmar, including drug-drug interaction, additive pharmacological effect and renally inappropriate medicines?
- How well can Janusmed algorithms predict actual DRPs, measured as correlation between calculated risk levels and clinical outcomes?
- Can machine learning be used to combine algorithms from Janusmed knowledge databases with additional factors to improve the prediction of DRPs?
Data and method
The core of the project is two sets of data which will be the same for all three WPs:
(1) The real-world data primarily from the electronic health record in region of Kalmar including data on a population of approximately 200,000 patients for 10 years, covering both hospitals and primary care. (2) The rules and algorithms from three Janusmed knowledge databases (Interactions, Risk Profile, and Renal function). WP1 will be a descriptive cross-sectional study, WP2 a nested case-control study, and WP3 will develop and evaluate new machine learning models.
The research project is carried out in an interdisciplinary team with broad competence including clinical pharmacology, pharmacoepidemiology, computer science, statistics, and visual analytics among other things.
DRPs is a major problem for society, especially with an ageing population and increasing use of medicines. Knowledge from the current project can be used to improve current decision support algorithms, as well as provide new insights about how advanced technology such as artificial intelligence can be used to improve predictions. Improved predictions can lead to improved patient safety and reduced societal costs.
The research is part of:
- eHealth Institute
- Linnaeus University Centre for Data Intensive Sciences and Applications
- Linnaeus Knowledge Environment: Sustainable Health
- Linnaeus Knowledge Environment: Digital Transformations
- Alisa Lincke Senior lecturer
- +46 470-70 84 15
- Olof Björneld Doctoral student
- Rafael Messias Martins Senior lecturer
- +46 470-70 86 08
- Thomas Holgersson Professor
- +46 470-70 83 76
- Tora Hammar Senior lecturer
- +46 480-49 71 76
- +46 72-594 97 16
- Welf Löwe Professor
- +46 470-70 84 95
- +46 76-760 36 62