The high number of alerts presented to the user can lead to alert fatigue and contribute to clinician frustration and burnout. Ignoring clinically relevant information and non-adherence to alerts can raise severe ADE.
Alert Recommendation Systems (ARSs) are novel systems that can provide personalized alerts that are
more relevant and better fit care providers’ expertise and needs.
You will contribute to the solution to this problem by:
- building a proof-of-concept ARS for Amsterdam UMC's medication alerts to increase the overall alert acceptance;
- investigating the ARS potential value compared to the current CDSS alerts.
To achieve these goals, the candidate will explore the use and development of recommendation techniques based on machine learning, deep learning, and neurosymbolic AI to uncover potentially complex interactions between alert and patient data.
We are looking for a Postdoc candidate who is eager to contribute to better clinical decision support and safer medication prescription in the hospital settings with the help of ARS.
You will investigate how to build and evaluate a suitable ARS for Amsterdam UMC’s medication alerts. This investigation will address several methodological topics; a non-exhaustive list includes:
- Collaborative filtering;
- Content-based filtering;
- Hybrid recommender systems;
- Neurosymbolic recommender systems;
- Autoencoder-based recommender systems.
You will provide efficient and scalable implementations of your methods and will integrate them with popular open-source systems. Via the Department of Medical Informatics, you will have access to large datasets comprising millions of CDSS alerts.